Bleacher Report - Articles by Zach Fein

NFL: Examining the In-Season Correlation of Defensive Performance

Zach Fein — Sat, 10 Oct 2009 01:56:15 -0400

Riddle me this: If many a fantasy analyst proclaim that it’s very difficult to predict how a defense will do before the season, then how much stock should we put into early-season performance?

Think about it. Is it at all likely that said defense will perform up to those numbers the rest of the season if they are so unpredictable and inconsistent?

Moreover, during which week of the season is it easiest to predict a defense’s stats for the remainder of the year? In other words, can you better predict the final 12 games using the first four, or the final four games using the first 12?

I looked at every team’s game-by-game stats since 2002 (gathered from pro-football-reference.com) to investigate. I compared defensive stats prior to and after a certain point of a season, as described above, as well as splitting up teams into four quartiles and seeing how each group performs the rest of the way.

First up is the relationship between in-season defensive performance. I found each team’s stats up to and after each game played (for instance, the average points allowed in the first three games and the final 13 games) and compared the two using correlation and average absolute error.

Quick explanation: Correlation is a number between one and negative-one that describes how close a set of values are related. A negative number, in this case, would mean that the more points allowed in the first half of the season, the less allowed in the second half, and vice versa; a positive number means that a team that allows a low number of points in the first half will also allow a low number in the second half.

The further the correlation away from zero, the more the two sets of data are related.

Average absolute error (AAE), on the other hand, is much easier to understand; it’s essentially the average difference between each of the two data sets.

The tables below show this data. The higher correlation and the lower AAE, the better the relationship is for each stat.

(The first row reads: The correlation between points allowed in a defense’s first game and points allowed per game in their remaining games played is .165.)

A lot to digest there. First of all, take a look at the correlations for passing yards and rushing yards. The highest correlation for passing yards is .211, after five games, whereas the correlation for rushing yards after just one game is .227.

In other words, it’s easier to predict a team’s rushing yards allowed for the rest of the season using just one week of data than it is to predict passing yards allowed at any points of the season.

Although sample size gets larger as the season goes on, that doesn’t mean that the autocorrelation—the correlation with itself—of each stat does too. After 15 games, you have a large amount of data, but there’s only one game left in the season.

The data show that the autocorrelations and AAEs for each stat are at their highest and lowest, respectively, near the middle of the season, when there is an equal number of games in each bucket.

Let’s now move our attention to our next topic. I split all 192 team-seasons into four quartiles based on their defensive performance after four, eight, and 12 weeks (one quartile is eight teams per year).

I then averaged the stats of all teams in each quartile the first N weeks (four, eight, or 12) and the remainder of the season. Are the best teams early in the season the best teams for the rest of the season?

(The first row reads: After four weeks, teams in the first quartile—those with the lowest in each stat—allowed 13.1 points per game, and they allowed 20.0 per game the rest of the season.)

Through four games and eight games, points allowed have a clear but weak autocorrelation, as the four quartiles finish in the same order the rest of the season.

But after 12 games, there’s no relationship at all—the teams in the fourth quartile allow almost as few points in the last four games as teams in the first quartile.

This is especially key for fantasy owners who look at a player’s schedule in the final weeks of the season during the fantasy playoffs.

As the first table shows, pass yards allowed does not have as strong an autocorrelation as rush yards allowed.

For each week, the quartiles’ pass yards does not line up for the remainder of the season; after four games, for instance, teams in the third quartile allow five less yards per game through the air as the second quartile.

This is not the same for rushing yards. Teams that are at the top or bottom of the league in rush yards allowed through four, eight, or 12 games usually are at the top and bottom, respectively, the rest of the season; after 12 games, in fact, teams regress only 45 percent to the mean in the final four weeks.

Turnovers face the same problem as passing yards—the third quartile (actually those with the second-most takeaways) has more takeaways than the fourth quartile the rest of the season at each interval.

Though the data isn’t divided into interceptions and fumbles, I can tell you that both have little autocorrelation from additional research: Interceptions have a year-to-year correlation of .09, and for fumbles recovered the correlation is .13.

NFL: Regression to the Mean, Sample Size, and In-Season Projections

Zach Fein — Sat, 12 Sep 2009 01:43:32 -0400

What if I told you that Adrian Peterson isn’t as good as his stats say?

My reasoning is the Curse of the Leading Rusher. You’ve never heard of it before, but it’s an obvious trend. Since 1980, the NFL’s leading rusher has seen his rushing yards fall by 489 yards and his YPC by almost half a yard just one season later. Only six of the 31 leading rushers even increased their rushing yards the following season, and nine had less than 1,000 yards.

Convinced? You shouldn’t be. Their decline is nothing more than regression to the mean and a lack of sample size. Let me explain.

Regression to the Mean

Regression to the mean—also known as the law of averages—is the phenomenon that explains why extreme seasons far from the average (such as a passer rating over 100 or below 70) tend to be closer to the mean the following year.

It’s why we see the leading rusher put up worse numbers the next year, why quarterbacks don’t put up 40 touchdowns in back-to-back years, and, yes, why the Curse of 370 is a myth. Of course, regression to the mean also affects those on the bottom end of the spectrum—Brett Favre won’t throw 22 interceptions again, partly from regression and partly because his past numbers have always been better than last year’s.

Which brings us to the next principle: True score theory. True score theory states that a player’s observed performance is a combination of his true talent level and random error (or “luck,” in layman’s terms). Any time we see a player’s production in a certain time frame (one year, three years, or 10 years, for instance), we expect that his actual true talent level is somewhere in between the observed performance and the league average.

Let’s look at an example. I split all quarterbacks since 1980 into four quartiles based on their fantasy points per attempt, such that the top passers went into one group and the worst went into another. I found the average fantasy points per attempt for each quartile, and then compared that number to the group’s collective next-season value.

The graph below shows this with fantasy points per attempt divided by the average to create an “index value” (where 1.00 is average).

As the blue lines indicate, the quartiles regressed 63 percent to the mean as a whole. In comparison, should Drew Brees regress that much this season, he would have a 50-point drop in fantasy points, which would’ve put him at No. 8 among passers last season. (Of course, that’s assuming no one else would regress.)

I know what you’re thinking. That’s one year of data; of course they’ll decline. Yet regression to the mean even occurs for running backs with three straight 1,000-yard seasons: Those 99 backs averaged over 1,340 yards each of their first three years—with a yards-per-carry rate between 4.33 and 4.36 each year—but they had just 1,161 yards with 4.21 YPC in the next year; almost one-third (32) had less than 1,000 yards to boot.

In other words, those 99 rushers regressed about 45 percent to the mean, which may seem large, but my calculations would’ve predicted a 42 percent regression. That said, their regression was 18 percent less than those of the quarterback example above.

The more sample size we have, the less regression to the mean there is and the more certain we are that observed performance is close to a player’s true talent level.

And that brings us to the next topic.

Sample Size, Past Performance, and Mid-season Projections

Most fantasy football players create their own set of rankings usually based on hours of research and trends. So, explain to me why their prognostications should change drastically halfway through the season?

It’s not just Average Joes that do this either. ESPN’s Christopher Harris, for example, ranked Peyton Manning as his No. 2 quarterback in the preseason, but after Week Seven he had dropped Manning all the way down to No. 7.

Manning was 13th in fantasy points (with a quarterback rating just over 80) after seven weeks, but in the final 10 weeks of the season, he was the No. 4 quarterback and had a 105.2 rating, highest among qualifiers.

The moral of the story: Half a season is not enough to warrant a major change in your preseason rankings!

Curious, I went about calculating the change in true talent for players who had a considerable difference in production during the first half of the season compared to their preseason projection. I’m only looking at overachievers, but these numbers also hold for first-half disappointments.

I chose one player at each skill position whose 2009 FEIN projection was equal to a low-end starter in fantasy leagues (Matt Ryan, Larry Johnson, and Eddie Royal) and assumed they were to exceed their preseason projection by 15 percent in the first half (yet with no difference in projected attempts or catches).

Then, based on their projected first-half attempts, I calculated the amount of regression for each player: With 250 first-half pass attempts, for instance, Ryan would be expected to regress about two-thirds back to his preseason forecast in terms of yards per attempt. (I regress to their preseason projection as a shortcut to calculating a whole new set of projections.)

The results indicate that, with 15 percent higher production than expected in the first half of the season, Ryan would only perform 5.4 fantasy points above his preseason projection in the second half, compared to 2.7 for Johnson—including receiving numbers—and 3.4 for Royal.

To further justify the results, the full-season difference between Ryan’s preseason and mid-season projections would not have moved him up a spot in last year’s rankings. (The 10.8-point difference—5.4 doubled—would have increased his 2008 fantasy points from 196 to 206.8, but he would have ranked No. 13 among passers either way.) For Johnson, his outburst would have moved him up just one spot, from 29 to 28, and Royal would have moved two spots to No. 17.

At what point should you adjust your estimate of a player’s true talent? Well, if they overachieved by 15 percent for the first 16 weeks, Ryan’s updated projection would be 16.3 points higher over a full season than his preseason projection (which wouldn’t have moved him up a spot last year), Johnson’s forecast would be 8.4 points higher than expected (a two-spot jump), and Royal’s 9.3-point disparity would have moved him from No. 19 to No. 15.

As a general rule, it takes 21 games for a quarterbacks’ fantasy points to regress 50 percent to their previous projection, as well as 29 to 30 games for a running back and 13 to 14 games for a wide receiver. (The values for quarterbacks and running backs were found assuming there wasn’t a newly calculated projection after 16 games.)

In other words, only for wide receivers should you weigh current-season production more than their preseason projection at any point in the season. That doesn’t mean you should disregard current performance, only that it shouldn’t have as much weight as it usually does when you make trades or waiver-wire pickups.

Remember: If you have a player like Peyton Manning, for whom we have years of stats, then in no way, shape, or form should your opinion of him change after half a season, much less 20 games. Sample size is not your friend, and neither is regression to the mean.

This article can be seen at FeinSports.com and FFWritersWithHair.com.

NFL: How Breakout and Breakdown Seasons Affect Future Performance

Zach Fein — Mon, 31 Aug 2009 00:50:48 -0400

To say that Seahawks quarterback Matt Hasselbeck was a bust last year is an understatement.

Hasselbeck had finished in the top six in quarterback fantasy points three of five years from 2003 to 2007; fantasy owners were expecting another good season, as he was drafted as the No. 9 quarterback in standard leagues.

Injuries and poor performance plagued Hasselbeck. Just one year after throwing for career-highs in yards and touchdowns, he set career-worsts in passer rating (57.8), completion percentage (52.2), interception percentage (4.8), and yards per attempt (5.8).

This year, despite his team’s acquiring former Pro Bowl receiver T.J. Houshmandzadeh, Hasselbeck’s average draft position puts him as the No. 15 quarterback.

Should his regression affect his 2009 performance that much? Or is he the top-10 quarterback we saw in previous years?

Naturally, this question applies to breakthrough performances, too. Panthers running back DeAngelo Williams finished as the No. 1 back in fantasy with 1,515 yards and 18 touchdowns last year.

He increased his yards per carry by 11.5 percent and his rushing touchdown percentage by 137 percent from his 2007 stats.

Drafted on average as the No. 9 running back, the consensus is that Williams will fall back some from his 2008 numbers.

Regression to the mean should obviously kick in for Williams, but it’s unseen as to whether his two good-not-great seasons will have any value in our 2009 ranking for him.

Before any analysis, we must first define a breakout. My criterion for a breakout season is a 15 percent increase in fantasy points per attempt or reception—or quarterback rating—over their highest value in the previous two years, and a breakdown season is one in which there is a 15 percent decrease from the minimum value in the previous two years, while staying on the same team each year.

For instance, there have been 37 running backs since 1980 to have a breakout season after two good-not-great seasons, as was the case for Williams; they saw a 19 percent drop in their fantasy points per carry (FPPC) the year after their breakout, regressing almost 75 percent back to their pre-breakout performance.

Yet there’s a selection bias in that sample. Those 37 running backs’ fantasy points per carry was almost 30 percent higher than average in their breakout season, and we’d expect regression to the mean to kick in regardless of whether they broke out.

So, I compared those 37 backs’ performance in the year after their breakout to a baseline performance: The next-year performance of all running backs with a similar FPPC average.

That is, the next-year performance among all running backs since 1980 with a FPPC within 0.015 points of the FPPC average in the 37 rushers’ breakout season was nearly equal to the breakout-backs’ next-year performance—less than one-tenth of one percent higher.

In other words, the fact that there was a breakout—as well as the fact that those 37 backs had almost league-average production in the two years before the breakout—made no difference in their next-year production.

They would have regressed to the mean just as much if they had performed at their breakout level for three straight seasons.

Let’s see how those numbers compare to other positions.

Quarterbacks

To clarify my criteria, I looked at all quarterbacks with 150 attempts in three straight seasons and found all passers who had a breakout or breakdown season in either their first or third years.

Players who fall under the “in the first year” status include Jim Kelly as a “breakout” (97.6 rating in 1991, followed by two near-80 ratings) and Ken Anderson as a “breakdown” (66.9 passer rating in 1980, followed by two above-90 ratings).

The first table below shows QBs who had a breakout or breakdown season—compared to the two seasons prior—as well as their post-breakout or breakdown passer rating, and the baseline performance (the average next-season rating of all QBs with a passer rating within five points of the breakout- or breakdown-season rating).

What the table means: The 43 passers who broke out had a 74.5 rating two years before their breakout, a 75.4 rating one year prior to the breakout, and a 92.3 rating in their breakout season.

The 43 passers had an 83.8 rating the year after the breakout, while the baseline performance was an 86.8 passer rating.

That means that the 92.3 rating they had in their breakout season had no effect on their next-season rating; they regressed more than normal, since they had had two mundane passer ratings the two years before the breakout.

The same occurs for breakdowns, though the difference isn’t as pronounced; because of the two good seasons prior to their breakdown, broken-down passers tend to bounce back more than a quarterback with similar stats.

Let’s see how these stats compare to the quarterbacks whose breakouts or breakdowns came in the first year of a three-year stretch.

There were 26 passers who posted a passer rating that was 15 percent greater than either of their next two seasons’ ratings. One season later, their collective ratings were six points higher than the baseline rating we expected.

The 10 percent increase in passer rating from year three to year four is astonishing. The baseline performance is almost equal to the 26 quarterbacks’ weighted rating in those three years (weighting each season as half as significant as the next returns a 79.4 rating).

Instead, it rises to 84.8, but that rise isn’t all credited to their breakout in the first year. The two years prior to that “breakout,” the 26 quarterbacks had ratings above or just below 90.

That suggests that the passers were slightly lucky when their ratings were in the 90s for three years, unlucky the next two years, and finally regressed back to their true talent level of 84. (Indeed, the following year, their passer rating held steady at 83.7.)

As for the 47 passers in the other group, their breakdown in the first year was a factor in their year-four passer rating.

They had a weighted passer rating average of 83.9 in the first three seasons, which would have risen to 86.2 had they put up a rating of 84 in the first year.

That’s a difference of 2.3 points—also the discrepancy between the baseline performance and their year-four passer rating.

On the other hand, there’s no effect if I look at quarterbacks with a breakout or breakdown season in the first season of a four-year stretch (despite the small sample size).

There were only seven quarterbacks who fit the breakout requirement; they had a decline in passer rating each year after their 100.1 rating.

The 27 passers who had a first-year breakdown produced a year-five rating that was just fractions below their expected performance.

The more attempts they racked up (in years two through four), the more we were certain that their true talent level was in the mid-80’s.

Conclusion

Quarterbacks whose breakout or breakdown occurs in the final year of a three-year stretch regress more to the mean compared to a baseline expectation.

The difference is equal to about two points of quarterback rating, with a larger regression for breakouts than breakdown performances.

The 26 quarterbacks whose breakout came in year one had their rating drop 15 points, stay in the high 70’s, then regress back to the low-to-mid 80’s.

Their up-and-down performance leads to a valuable notice: We always expect that a player’s true talent level is equal to his sample performance plus a regression to the mean.

Quarterbacks who have a breakdown season in the first year of a three-year stretch have a more dramatic decline after the third season than their baseline, owing to the breakdown performance; if those passers had a passer rating of 84 instead of 67.8 in the first season, we would expect their fourth-year production to be equal to that of the baseline.

2009 cases: Matt Hasselbeck, third-year breakdown.

Running Backs

Let’s see how the quarterback data compare to running backs. Only running backs that had 100 carries in three straight seasons are included. Fantasy points per attempt are prorated to 250 attempts in the tables below.

While it seems surprising that breakout rushers performed to their expectation the next year, the 37 backs had such a large increase in fantasy points per 250 attempts (FP/250) in their breakout season (29 percent) that their weighted FP/250 is 174, enough to suggest that the remaining 13-point decrease is from regression to the mean.

Running backs that broke down performed eight percent better than their baseline expectations the year after their breakdown, jumping from 135 to 150 FP/250. Although their FP/250 fell two straight years, their weighted FP/250 (154) predicted otherwise.

Now, what about backs whose breakout or breakdown came in the first year?

The first year of the three-year stretch affected both groups of running backs, though it wasn’t as profound for the breakdown group.

The 52 breakout backs had a five-point increase from the third to the fourth year, while the baseline suggested a five-point drop was in store.

Again, the weighted average (157) predicted their next-season performance better than the breakout itself.

Running backs whose breakdown occurred in the first year of a three-year stretch had a 13 percent drop in production from the third to the fourth years.

That, however, isn’t such a large decrease, since the baseline was an 11 percent drop. That said, the three-point difference in FP/250 is hardly significant.

When we look at four-year stretches with a first-year breakdown, the final-year FP/250 is equal to the baseline performance, at 163 FP/250.

The 16 backs that apply had 133 in their down year, followed by three years of FP/250’s in the 170’s, and then 163 a season later, as shown below.

Conclusion

A breakout or breakdown in the final year of a three-year stretch has no extra effect on a running back’s future production, compared to a baseline expectation.

Breakout backs performed to their baseline, and breakdown backs had 11 more FP/250 than expected.

When the breakout occurs in the first year, however, there’s a 10-point difference that is clearly influenced by the first-year 199 FP/250 rate. There’s no evidence that a breakdown in the first year has any effect on the year-four FP/250.

2009 cases: DeAngelo Williams (third-year breakout); LaDainian Tomlinson, Marion Barber, Joseph Addai, and Fred Taylor (third-year breakdown); Maurice Jones-Drew, Barber, Frank Gore, Steven Jackson, Clinton Portis, Larry Johnson, and Willie Parker (first-year breakout); Williams, Ronnie Brown, and Chester Taylor (first-year breakdown).

Wide Receivers

How do the receivers stack up? I looked at wide receivers and tight ends with 30 receptions in three straight seasons, and the results aren’t shocking.

Here are the data for breakouts and breakdowns in year one. Fantasy points per catch is prorated to 70 receptions (FP/70).

Breakouts and breakdowns for receivers aren’t much different from quarterbacks and running backs, and the disparity between actual and expected performance is much greater for wideouts.

The 89 breakout receivers were 13 points worse than their expectation, which was also nearly equal to their weighted average FP/70 of 149.

Those 89 receivers had a 17.5 percent drop in production from their breakout season to the next, compared to their expected drop of 9.6 percent.

The breakdown group reminds us that a weighted average of past performance is always more accurate for future forecasts than one year alone. Their 117 FP/70 in their low year was expected to be followed up by 125 FP/70, but their three-year weighted average of 133 FP/70 was right on the dot.

There’s an interesting contradiction when we look at first-year breakouts and breakdowns. One table gives one conclusion, but the other says otherwise. The first table shows data for three-year stretches…

And the next table shows data for four-year stretches.

Both tables tend to agree on first-year breakouts; each suggests a three- or seven-point difference in FP/70 between actual and the baseline performance.

But it’s the breakdowns that are fascinating. One table—the three-year stretch—says that receivers with a first-year breakdown perform five points worse than the baseline.

The other, though—looking at four-year stretches—says that the same receivers produce four points better than expected.

We’re arguing semantics, however. Both tables are most likely correct, considering the samples they use: If a receiver has three good seasons after a bad year, the down year (which is four years ago) is outnumbered by the good years and has so little weight in determining the player’s true talent level.

Chances are his true talent lies closer to the three stellar years as opposed to the poor season.

Conclusion

Third-year breakouts and breakdowns for receivers have much less weight compared to other positions in their future production.

As always, a weighted average of past production is better at predicted next year’s stats than on season (the breakout or breakdown) alone.

Wideouts with first-year breakouts perform about three points better than expected. Those with first-year breakdowns, though, tend to play better or worse than expected, based on which group you’re looking at.

With just two seasons of data after a breakdown, receivers perform five points worse than the baseline, but they are four points better than expected with three years of stats after their breakdown.

2009 cases: Derrick Mason, Deion Branch, Steve Smith (NYG), Mark Clayton (third-year breakout); T.J. Houshmandzadeh, Chad Ochocinco, Plaxico Burress, Dallas Clark, Chris Cooley, Heath Miller, Desmond Clark (third-year breakdown); Santana Moss, Josh Reed, Clayton, De. Clark, Owen Daniels (first-year breakout); Andre Johnson, Greg Jennings, Kellen Winslow (first-year breakdown, three-year stretch); Ike Hilliard (first-year breakdown, four-year stretch).

2009 Fantasy Projections: Charting Manning's Downfall and Portis' Dependability

Zach Fein — Mon, 10 Aug 2009 19:32:24 -0400

It’s the middle of August, a time in which many fantasy football owners start to produce their rankings and cheat sheets. Some resort to intuition, while some take an expert’s rankings and tweak them. Others use hours and hours of research to create their rankings.

I’ll use projections.

My FEIN projections (short for Forecasted Evaluative Impartial Numerical projections) aren’t based on intuition or an expert’s rankings or even hours of research. Mine are based on complex algorithms with no human interaction—I input a player’s stats, and out spits their forecast.

The Method

Some of you may be wondering how I control for players switching teams or wide receivers losing a top passer, such as Jay Cutler and Brandon Marshall.

I don’t; I only adjust for players who switch conferences. While I could account for this by using air yards, I could only find yards after catch for quarterbacks for the current year, and there was no data for any previous seasons.

The basic steps for any projection system, as well as these, are:

1. Weight each season;
2. Regress each player’s stats to the mean;
3. Adjust for age.

The FEIN projections look at every player-season since 2002. Each stat—passing yards per attempt, interceptions per attempt, etc.—are weighted differently. I looked at all five-year stretches since 1980 and performed tests to see which weight results in the lowest error between actual performance and projected performance.

For instance, completion percentage is weighted as 0.5, meaning 2008 would have a weight of one, 2007 would have a weight of 0.5, and so on down to a minuscule weight of about 0.016 for 2002, whereas interception percentage has a weight of 0.9.

Before I do that, however, I first adjust each player’s stats to a neutral conference. Since 2002, players moving from the AFC to the NFC mid-season or in the offseason have a 2.1 percent increase in passing touchdowns, so I adjust each AFC player’s passing touchdowns down by 1.1 percent (the square root of 2.1 percent, to get them to a neutral conference).

Then, instead of regressing a player’s stats to the league average, I regress it to their height or weight (or BMI). I did further research to follow up my article on height and weight and found that the only stats that are not affected by height or weight are completion percentage, pass yards per attempt, and pass touchdowns per attempt. I regress the first two to the league average, while I regress the third to completion percentage and interception percentage.

What do I mean by that? I found that completion percentage actually predicts future touchdown-to-interception ratio better than that stat itself, and I already saw a relationship between weight and interceptions, so I project first completion and interception percentage, and use that to project touchdowns.

(Side note: The amount I regress each stat to the mean or to height and weight depends on the year-to-year consistency in each stat. What I mean is that completion percentage is more stable than any other passing stat, so a quarterback won’t be regressed as much for completion percentage than interception percentage.)

I then age adjust, adjust for strength of schedule, and then re-adjust for the player’s 2009 conference.

Strength of schedule adjustments admittedly don’t have that much effect; I regress each team’s 2008 defensive stats to the mean (so much so that the Steelers’ 13.9 points allowed per game rises to 18), total up the passing yards per attempt for each team’s 2009 opponents, and then normalize for the league average (such that a team whose opponents allowed a combined 6.7 yards per attempt with a league average of 6.8 YPA would have a YPA factor of 0.985).

I also project rookies as if they were 2008 rookies. Their “2008 stats” are solely based on their projected attempts or receptions. That is, we know that a running back with 200 attempts usually has a higher YPC than one with 100 attempts, or else he wouldn’t have gotten 200 attempts.

So, I looked at all players post-1980 within 20 pass or rush attempts or 10 receptions of each rookie’s projected playing time and found the average stat line of the players in that group. (As expected, the running backs with more attempts had a higher YPC than low-carry backs. Running backs with 188 to 228 attempts—Chris Wells is projected to have 208—averaged one-tenth of a yard more per carry than those with 51 to 91 carries—Glen Coffee is projected to have 71.)

Once I have that, I run the rookies through the same process as everybody else.

The Projections

I cheated a little, as projected playing time is an average of ESPN’s, CBS’s, and FFToday’s projected attempts and receptions.

For those without a projection and who played in 2008 (plus Michael Vick), I assigned 20 pass and five rush attempts for quarterbacks, 20 rush attempts and five receptions for running backs, five receptions for tight ends, and 10 receptions for wide receivers.

In the spreadsheet below, overall rank is determined as a player’s VBD, or fantasy points over baseline. My baseline was the number of each position picked in a standard, 192-player draft—24 quarterbacks, 57 running backs, 18 tight ends, and 59 wide receivers.

Some players have “xxx” beside their team and bye week; these players either didn’t play in 2008 or are currently a free agent or retired (in the cases of Vick and Brett Favre).

Without further ado, here are the top-10 in fantasy points at each position. Click here to see the full list.

Player	Pos	Comp	Pass Att	Pass Yd	Pass TD	INT
Drew Brees	QB	378	607	4255	33	18
Tom Brady	QB	350	553	4001	29	16
Aaron Rodgers	QB	340	550	3946	29	17
Kurt Warner	QB	354	540	4106	29	16
Philip Rivers	QB	319	513	3772	29	16
Peyton Manning	QB	345	533	3796	27	15
Tony Romo	QB	305	496	3662	28	17
Matt Cassel	QB	316	500	3561	22	13
Jay Cutler	QB	306	495	3615	22	14
Matt Ryan	QB	310	501	3663	23	15

Player	Pos	Rush Att	Rush Yd	Rush TD	Rec	Rec Yd	Rec TD
Maurice Jones-Drew	RB	247	1073	12	60	513	3
Matt Forte	RB	292	1168	10	55	424	5
Adrian L. Peterson	RB	321	1464	12	22	169	2
Michael Turner	RB	334	1438	15	8	48	1
Steven Jackson	RB	291	1203	10	48	401	3
Steve Slaton	RB	277	1241	9	49	385	4
Clinton Portis	RB	315	1330	11	28	213	1
LaDainian Tomlinson	RB	264	1105	12	43	321	2
DeAngelo Williams	RB	271	1235	13	22	139	1
Chris Johnson	RB	264	1196	9	43	312	3

Player	Pos	Rec	Rec Yd	Rec TD
Tony Gonzalez	TE	79	942	7
Antonio Gates	TE	70	839	8
Jason Witten	TE	80	940	6
Dallas Clark	TE	72	802	8
Chris Cooley	TE	73	800	6
Greg Olsen	TE	67	763	6
Zach Miller	TE	62	809	5
Owen Daniels	TE	64	786	5
Kellen Winslow	TE	67	750	5
John Carlson	TE	59	696	6

Player	Pos	Rec	Rec Yd	Rec TD
Larry Fitzgerald	WR	99	1418	10
Calvin Johnson	WR	84	1345	10
Andre Johnson	WR	105	1406	8
Randy Moss	WR	84	1191	11
Reggie Wayne	WR	92	1273	9
Greg Jennings	WR	79	1224	9
Steve Smith	WR	85	1288	7
Roddy White	WR	86	1261	6
Vincent Jackson	WR	71	1140	8
Antonio Bryant	WR	81	1203	7

You can download the above spreadsheet into Excel here.

The Counter-intuitive Forecasts

The projections aren’t going to be accurate for all 470 players. Here are some of those whose projections seem to go against the grain.

Peyton Manning

Peyton Manning finished in the top four in fantasy points at his position each year from 2002 to 2007, but he slipped to seventh last year. A more telling trend is this: Since his record-breaking 2004 season, both his yards per attempt and passer rating have fallen each year, from a 9.2 YPA and 121.1 rating in 2004 to last year’s 7.2 YPA and 95.0 rating.

The FEIN projections don’t take that into account, but Manning is projected to once again decline in both stats as well as finish sixth among quarterbacks in fantasy points, with this forecast: 64.7 completion percentage, 3796 yards, 27 touchdowns, and 15 interceptions.

Matt Cassel

Matt Cassel went from a career backup to earning more than Tom Brady in just one year, after passing for almost 3,700 yards with 21 touchdowns.

As I mentioned before, the projections don’t take into account any changes of coaching staff, personnel, or teammates, and Cassel is one who would be negatively affected with any adjustments for this.

Last year, 57 percent of Cassel’s passing yards came after the catch, while only 42 percent came after the catch for Kansas City quarterback Tyler Thigpen. Cassel’s raw YPA was a yard higher than Thigpen’s, but his air yards per attempt was less than Chiefs QB.

Since wide receivers have more control over YAC than quarterbacks, it’s likely that Cassel’s projected YPA of 7.1 is optimistic.

Clinton Portis

Clinton Portis’ projection isn’t actually odd at all; it’s just that too many fantasy owners massively underrate him.

Since his rookie season in 2002, Portis has finished outside the the top 10 in fantasy points among RBs just twice—he finished 11th in 2004 and 36th in 2006, when he only played eight games (double his fantasy points and he would finish ninth).

Portis has had one sub-1,200-yard season in his career—2006, when he would’ve finished with 1,000 yards if he played all 16 games. He’s also had two sub-nine-touchdown seasons in his career—2004, the year he finished 11th in fantasy points, and 2006, when he would’ve had 14 if he played all 16 games.

Just when everyone thought he was done, Portis had almost 1,500 yards and nine touchdowns in 2008.

His forecast of 1,330 yards and 11 touchdowns is nothing short of ordinary.

Anquan Boldin

Boldin finished seventh in fantasy points last year despite missing four games due to injury, and he finished the year with 1,038 yards and 11 touchdowns.

Boldin’s projection calls for a YPC rate of 12.4, in line with his past numbers. The seven touchdowns is a major drop from his touchdown percentage the last two years, but receiving touchdowns have a weight of 0.8—which means that 2004, 2005, and 2006 are weighted quite heavily in the forecasts.

We get a career weighted touchdown percentage of 8.9, or 7.2 touchdowns in his 81 projected receptions; that’s slightly less than his exact projection of 7.3 touchdowns.

As well, his No. 17 ranking among wide receivers may be due to his forecasted playing time. Give him 89 receptions, and he ranks No. 12 among wideouts.

Vincent Jackson/Antonio Bryant

Jackson and Bryant were the epitomes of late-round or waiver-wire grabs at the wide receiver position last year. Jackson, selected No. 41 among wideouts in 2008, provided owners with almost 1,110 yards and seven touchdowns. Bryant, taken No. 73, finished No. 8 among receivers, with 83 receptions for 1,248 yards and seven scores; amazingly, Bryant had a higher YPC in 2007 than in 2008.

Fantasy football owners certainly think they’ll both cool off, as Bryant and Jackson are being selected as Nos. 17 and 18 among receivers, respectively.

That doesn’t make sense, even if you discount their projections. Jackson is projected to catch 71 balls in 2009, 12 more than he had in 2008. Thus, he’d need a 25 percent drop in yards and touchdowns per catch (which would give him a new career low in YPC by over a yard) from 2008 to finish as the No. 18 receiver, if only because of his (projected) increase in receptions.

As for Bryant, he’s projected to get 81 catches in 2009. If he even ties his career lows in YPC and touchdown percentage, he’d still end up with 138.3 fantasy points—which would place him right ahead of T.J. Houshmandzadeh, who’s projected to end the season with 138 fantasy points and finish No. 18 among receivers.

I would not suggest you use the bare projections as your cheat sheet. The projections don’t take into account any changes in a player’s surrounding—save for a switch in conferences—and, as such, there’s going to be a lot more inaccuracy in the projections for players switching teams.

Is Matt Hasselbeck the No. 24 quarterback? Maybe he was before T.J. Houshmandzadeh signed with the Seahawks; it’s inevitable that Hasselbeck will outperform his projection, and the same could be said for Matt Ryan with Tony Gonzalez now on the team.

Size Does Matter: How an NFL Player's Height and Weight Affect Performance

Zach Fein — Thu, 16 Jul 2009 02:48:32 -0400

Every year, it seems like a few talented college players are being snubbed from the NFL because their measurables, in NFL draft speak—height and weight, for example—are poor. To wit:

• Missouri quarterback Chase Daniel finished fourth in the Heisman Trophy voting in 2007—the second-highest ever for a Tiger—and finished his college career with the most offensive yardage in Missouri history. He had over 13,000 total yards in his three full years as a Tiger, including over 4,300 passing yards in both his junior and senior seasons.

His completion percentage was 72.9 percent in his final season, in which he also had 39 touchdowns and 18 interceptions.

But scouting reports said that Daniel, measured at 6'0" at the Scouting Combine, "lacks ideal height for an NFL quarterback." Daniel went undrafted in the 2009 NFL Draft and is currently fighting for the No. 3 quarterback spot on the Washington Redskins.

• Northern Illinois running back Garrett Wolfe’s worst college season was his junior season. He gained a measly 1,580 yards on the ground with a dismal 16 touchdowns on 243 carries, pathetic numbers for a man who also missed three games due to injury.

Sarcasm aside, Wolfe gained over 1,600 and 1,900 yards in his other two seasons (the latter of which led the NCAA), but at 5'7" and 186 pounds, Wolfe was drafted at the end of the third round to the Chicago Bears.

He hasn’t been given much opportunity to shine at all, with 46 career carries in two seasons in the NFL.

• The prototypical small guy, at 5'9", Wes Welker failed to garner much attention after high school. A week after signing day, Welker received a scholarship from Texas Tech when one of the Red Raiders’ projected signees chose a different school.

In his four-year career at Texas Tech, Welker had 3,475 total yards from scrimmage (rushing and receiving) with 23 touchdowns. His eight career punt return touchdowns set an NCAA record (since tied).

Welker went undrafted in the 2004 NFL draft; he signed with the San Diego Chargers but was ultimately cut after Week One.

Nevertheless, Welker showed that small players can succeed in the NFL. After breaking onto the scene in 2006, with 687 yards on 67 receptions for Miami, he was traded to the Patriots. Welker ended up with a league-high 112 catches in 2007, followed up by 111 in 2008; he had over 1,100 yards as well each of those two years.

Others, such as 6'0" Drew Brees—who, among many other accomplishments, threw for 5,000 yards in 2008—have proved that, when given the chance, size doesn’t matter.

In this article, I’ll be testing the effects of height and weight on seasonal and career production, split up by position.

Do taller quarterbacks have a higher completion percentage, on the notion that they can see over the line of scrimmage? Do taller wide receivers have an advantage over smaller receivers when it comes to yards and touchdowns?

As always, my data comes from Pro-Football-Reference.com. Unfortunately, I could only find height and weight data in the college section of P-F-R, so I went through the 212 colleges with 20 or more guys who played in the NFL and collected height and weight data from there. This gave me height and weight data for all but 898 of the more than 10,000 player seasons since 1980 (and career data for all but 227 of the more than 2,300 player careers).

Now, I don’t know the source of P-F-R’s height and weight data; I presume it is the numbers given out by each team. This may have some drawbacks (smaller players will sometimes get a boost to their height or weight, for example), but in general it works.

The first measure of correlation between measurables and production is, well, correlation.

Correlation shows the relationship between two variables in a number between negative-one and one.

The more related two variables are, and the more the graph between the two looks like a perfect line, the closer to one or negative-one the correlation is; a positive number represents a positive relationship—that is, as one stat goes up, so does the other—and a negative number represents a negative relationship—when one variable goes up, the other goes down.

Since that probably made no sense, take a look at this graph, which shows the correlation of several sets of data. If, say, weight and completion percentage form a straight line rising to the right, the correlation will be one. If they form a straight line rising to the left, the correlation is negative-one. And if there’s no relationship whatsoever (a scattered mess), it’s zero.

I took the career statistics of every player post-1980 (with 300 career pass attempts, 100 rush attempts, or 100 receptions) and plotted them with their height and weight. Here are the results.

In this scenario, a positive correlation means that taller or heavier players have better statistics than shorter, lighter players, aside from interceptions, which is the other way around.

We see a small positive correlation between weight and most passing stats (all those except yards per attempt). In each case, as weight increases, the stat in question rises accordingly (or drops, in the case of interceptions).

The largest correlation between height and any passing statistic is, interestingly, between height and completion percentage, though the correlation is negligible.

There’s not much correlation between measurables and rushing statistics, with the obvious exception of weight and rushing touchdowns. Heavier running backs tend to be the ones who score the most touchdowns, which is what we expected.

Then there’s the wideouts. There’s a very strong correlation between weight and yards per catch, at -0.653, which means that, somewhat counter-intuitively, shorter wide receivers have more yards per catch than taller receivers.

The correlation is even more staggering when you consider that there were fewer than 100 unique weights for the more than 400 receivers in the study; it rises to -0.819 when you only look at the average yards per catch at each of those unique weights.

This, in turn, brings up a good point: If there are so few unique weights or heights (of which there were 14 between those 400 receivers), then looking at correlation coefficients won’t show the real relationship between measurables and on-field production.

In other words, in order to look at this relationship (or non-relationship), we must group receivers based on their height and weight and look at the statistics from the totals in each bin of receivers. I did this for each position, making sure each bin had a sufficient number of players in it (at least 40 or so).

Here are the results for quarterbacks. All heights are in inches and all weights in pounds.

A quarterback’s performance tends to be highest when the passer is 74 inches tall (6'2"), but this could also be contributed to the fact that, either coincidentally or not, Brett Favre, Joe Montana, Steve Young, and Kurt Warner were all 74 inches.

Removing that particular height, quarterback rating increases at each height increment. As well, completion percentage for the shortest quarterbacks is lower than any other height, a full point lower than the tallest players’ completion percentage.

That said, there’s no real pattern between height and completion percentage beyond that.

The largest correlation comes from weight and interception percentage. Interceptions decreased each time weight increased, with a steep decline from quarterbacks weighing over 215 pounds.

I’m hypothesizing here, so bear with me, but heavier quarterbacks may be more inclined to take a sack under pressure, whereas lighter ones will instead throw the ball up to evade a sack.

I don’t have sack data, so I can’t test this hypothesis. This may be, however, just a correlation/causation issue—in other words, there may be another outside factor affecting interceptions.

In addition to looking at the average statistics at each height or weight, I also looked at the chance of a "great" season by looking at the odds of finishing in the top quarter in that particular stat.

I only included seasons where the quarterback had 350 pass attempts. Here are the results.

Does this confirm the myth? Again, excluding 6'2″ quarterbacks, completion percentage increases each time height does as well.

Taller quarterbacks also have higher odds of reaching the top quartile in yards per attempt than their smaller counterparts; 6'5" passers reach that twice as often as 6'1" quarterbacks.

We also see that the chance of having a great season in touchdowns per attempt decreases each time weight increases. We didn’t see this trend in the first quarterback graph, however, so I’d say that that is just a mere coincidence.

Backing up our previous conclusion, the probability of throwing for 12 or fewer interceptions more or less increases as weight goes up.

I then examined the predictive power of height and weight. I looked at all QBs who stayed with the same team and had 300 pass attempts in three straight years, then ran a regression using past two years of data plus height or weight to predict the third year.

For example, quarterback rating is equal to 0.373 * (last year’s rating) + 0.153 * (rating two years ago) - 0.684 * (height) + 91.246. That means the difference between a 73-inch and 77-inch quarterback’s passer rating is equal to (77 - 73) * (-0.684), or 2.74 points of quarterback rating, favoring the smaller passer (the coefficient for height is negative).

Weight affects passer rating by 1.15 points, favoring heavier QBs. Smaller QBs have an 8.82-fantasy-point edge over taller QBs over the course of 450 pass attempts, though weight had a much lower affect on fantasy points (2.21 points, favoring heavier QBs).

Completion percentage wasn’t changed by height or weight much at all. Passing yards, touchdowns, and interceptions favored smaller quarterbacks, yet touchdowns and interceptions also favored heavier QBs.

Almost every stat favors both small (height-wise) and heavy quarterbacks. I then ran the same test using body mass index, or BMI, which is an easy-to-use statistic that identifies a player as underweight, normal, or obese. What we want to see is an edge toward obese players, guys who are heavy yet small in stature.

In fact, we see nearly the same results as before. The difference between a 73-inch, 200-lb. and a 73-inch, 230-lb. quarterback all favor the beefier passer: an edge of 3.16 points on quarterback rating, 8.39 fantasy points, a minimal change in completion percentage (one-half of one percent), 13.89 yards, 1.09 touchdowns, and 1.91 fewer interceptions.

It should be noted, however, that those 8.39 fantasy points were the difference between the Nos. 13 and 17 quarterbacks last year in terms of fantasy points.

So, when you’re in the late rounds of your draft, and you see Eli Manning and David Garrard both available, take the time to think about BMI: The difference between Manning’s BMI (26.5) and Garrard’s BMI (32.2) equates to a whopping 12 fantasy points in 450 attempts.

Now we'll move on to the running backs. Here are the average stats at each height and weight group.

There’s a very strong correlation between height and yards per carry. We see a downward trend in yards per carry as height increases, with the exception of the jump from 74 to 75 inches tall.

This pattern is backed up by the relationship between weight and YPC; for each 10-pound bin, there is an average drop of 0.06 yards per carry (15 to 20 yards in a season for a full-time back).

In fact, the difference between the YPC of the 68- and 73-inch backs (more than one-third of a yard) equates to 90 to 100 yards over the course of a season—the difference between the 11th- and 17th-ranked backs in terms of rushing yards last year, mind you.

There’s no relationship, on the other hand, between height, fantasy points, and touchdown percentage.

As well, the correlation between weight and rushing touchdowns is small but apparent, as touchdown percentage increases somewhat as does weight. The difference in touchdown percentage between the small and heavy backs equates to two touchdowns in a full season, roughly the difference between the 12th- and 17th-ranked backs in terms of fantasy points last year (an average No. 2 back as opposed to a borderline No. 1 RB).

Let’s compare the above table with the one below, which shows the probability of finishing in the top quartile. I included only those backs with 200 rush attempts.

There’s a slight correlation between height and rushing touchdowns, taking out the jump (again) from 74 to 75 inches, that suggests touchdowns fall as height increases.

We also see a very interesting pattern when looking at weights and fantasy points. There’s a downward trend when it comes to the odds of finishing in the top quarter as weight increases. Why?

The answer is fantasy points. Because of their subjective weights (10 yards for one point, six points for a touchdown), the average running back in the sample had 70 percent of his fantasy points come from total yards, which, obviously, benefits the small backs with a lot of yards but just a few scores.

Continuing on with the predictive value of height and weight, the difference between a 5'8" and a 6'3" running back is 5.9 fantasy points in 250 attempts—an advantage to the smaller back by less than 51 yards and three-tenths of a touchdown. (Yes, the coefficient for rushing touchdown percentage was negative, but so small that it’s negligible.)

When looking at weights, we see that the lighter backs have a 2.39-fantasy point and an 18-yard edge over their heavier counterparts, while the heavier rushers have a 0.55-touchdown difference.

Unlike quarterbacks, height and weight seem to show the same conclusions for running backs, that shorter, smaller backs perform better. Thus, looking at BMI—which divides weight by height—will essentially neutralize the height/weight issue.

(This, in fact, is true, as the difference between the highest and lowest backs in BMI is less than one fantasy points, five rushing yards, and one touchdown.)

For running backs, we need to multiply height and weight, to combine them into one statistic.

When we do this, we find that the difference between a 68-inch, 200-lb. and a 75-inch, 250-lb. back is 5.79 fantasy points and 46 yards in favor of the lighter back and less than four-fifths of a touchdown in favor of the heavy runner.

Nevertheless, this is what we expected in the first place: smaller backs get the yards, and the bruisers get the scores. In terms of fantasy points, the differences in rushing yards and touchdowns are nearly equal, but the five-plus advantage in fantasy points for the small guys comes from their receiving.

In addition, another thing that may benefit the smaller rushers is the fact that yards may be a greater function of skill than touchdowns, considering that it only takes a good quarterback to move the offense into the five-yard line for a back to get a score.

Think about getting a small, fast runner like Leon Washington instead of a bruiser like Le’Ron McClain or Tim Hightower heading into the latter rounds of your draft. Washington’s receiving, as well as the fact he may get a few gratuitous touchdowns, may vault him over McClain or Hightower.

Now, let’s look at receivers—both wideouts and tight ends. Here are the statistics of each group of receivers.

There’s a clear correlation between measurables and production for wide receivers, and it’s not what you may have thought: shorter, smaller receivers perform better than taller, larger wideouts.

Shorter receivers have a higher yards-per-catch than taller receivers, though the pattern isn’t as noticeable as the relationship between weight and YPC; we see that yards-per-catch falls each and every time weight increases. The difference between the lightest and heaviest wideouts is 4.37 YPC, which equates to over 300 yards for a starter with 70 catches.

Keep in mind that those extra 30 fantasy points were the difference between the Nos. 8 and 19 receivers in terms of fantasy points, and the 300 yards were the difference between the Nos. 6 and 21 receivers in receiving yards last year.

There is, however, a slight positive trend regarding height and touchdown percentage, in that taller receivers have a small benefit over shorter ones. But the effect of height on touchdowns doesn’t cancel out yards per catch—smaller receivers still have more fantasy points per catch than taller wideouts.

That doesn’t hold steady, though, when looking at weight and touchdowns; there’s no relationship whatsoever between the two variables.

Now we'll examine the chance of producing a season in the top quarter, including only those receivers with 60 receptions.

Looking at the table, we don’t see any trend based on height and fantasy points. But when we split it up based on groups of three, we do: Wide receivers less than 71 inches tall finished in the top quarter of fantasy points 27 percent of the time, compared to 25.5 percent for wideouts between 72 and 74 inches and 22.5 percent for those greater than 75 inches.

Doing this for both yards per catch and touchdowns per catch, we see the obvious—YPC goes down as height increases, and touchdowns goes up as height goes up.

(By the way, the difference in those odds in YPC between the tallest and shortest receivers is 8.2 percent, while that number is 2.3 percent for touchdowns per catch. This suggests that it’s easier for shorter receivers to have a good season in touchdowns than it is for taller receivers to have a good season in terms of yards per catch.)

There’s a loose correlation between weight and yards per catch, while there’s not much relationship between weight and touchdowns.

The receivers above 241 pounds were all tight ends, so don’t look into the zeros too much.

Looking now into the forecasting power of height and weight for receivers, we see that, for receivers with 60 receptions in three straight years for the same team, height affects yards by only 7.86 yards in 70 receptions—benefiting smaller wideouts—while it affects fantasy points and touchdowns by less than one-fourth—both benefiting taller receivers.

Onto weight, we find that 180-pound receivers have a 6.55-fantasy-point and 37-yard edge over 230-pound receivers, as opposed to the 0.45-touchdown advantage the heavier receivers have.

It appears that height multiplied by weight will include both of those measurables into one statistic and would be a better indicator than BMI, but that’s wrong; I did these tests with both stats, and BMI affects production much greater than height x weight.

The difference between a 5′ 9″, 180-pound receiver and a 5′ 9″, 230-pound receiver? A massive 16 fantasy points—82 yards and 1.24 touchdowns, both with the edge to the bonier wideout. Sixteen fantasy points was the difference between the Nos. 15 and 23 receivers in fantasy points last year.

Remember last week’s article regarding aging curves? I commented on how Ted Ginn Jr. could have a breakout season this year, as he’s turning 24.

Here’s more evidence that you should take him late in your draft: The 3.26 difference in BMI between him and highly-touted rookie Michael Crabtree, who is being selected 20 spots ahead of Ginn overall, equates to an advantage for Ginn of seven fantasy points, 36 yards, and half a touchdown.

Be sure to look at Ginn—as well as Cardinals receiver Steve Breaston, whose BMI is even two points less than Ginn—as possible sleepers at wide receiver in your draft.

NFL Aging Curves by Position: Rookie QBs, Third-Year WRs, and Age-30 RBs

Zach Fein — Fri, 10 Jul 2009 00:15:55 -0400

They say age ain’t nothing but a number.

Twenty-one, 26, 31—what’s the significance? Is age merely just a number, or a baseline for seasonal performance? How can we quantify this?

Aging curves—also called aging patterns or age factors—show the relative performance of a group of players for each age, usually either showing how much (in percentage terms) a statistic improves or declines from one age to the next, or how the production at any age compares to the peak age.

One method used for aging curves it to simply add up the stats for every player at each age and look at the resulting sums. This is flawed, however, for it doesn’t account for the fact that there are many more players at age 24 than at age 34—the difference in yards or touchdowns per attempt won’t offset the disparity in attempts.

You can solve this problem by dividing the sum by the number of players at that age, right? Well, technically, yes, but it’s still not enough.

There’s selective sampling issues using this technique: The players who rack up attempts or receptions at age 21 or 22—their rookie season—are usually the best players, and thus it would appear that a player’s first year or two is one of their best.

At 35 and 36, the only guys left are the ones who have had a very successful first 12 years of their career and, as a whole, are typically better than the average player at age 24 or 25...and thus, it would appear that a player also peaks very late in his career.

The solution is to look only at matched pairs: how each player performs from one year to the next. Instead of comparing the average production at age 24 to age 25, you see how those 24-year-olds do the very next year. This way, you are looking at the same players in back-to-back years; previously, not every player was the same in each sample.

So, if those set of 24-year-olds have a completion percentage of 60 percent, and at age 25 that rises to 63 percent, we are fairly certain that completion percentage is 1.05 times higher (63 divided by 60) at age 25 than at age 24.

But we’re forgetting the most important principle at hand: regression to the mean. Observed production is equal to a player’s true talent, plus luck or random noise. What we are trying to do is to eliminate that noise, because, in general, that luck goes away in the next year. (For instance, running backs with over 1,000 yards since 1980 average 6.23 yards per game less the following year, almost 100 yards in a full season.)

We want to regress a player’s Year X stats, but leave the Year X+1 stats alone. The question is, how much should we regress? I found that a quarterback with 512 attempts, a running back with 439 attempts, and a wide receiver with 45 receptions should have their yards per attempt or reception regressed 50 percent to the mean; there are different rates for completion percentage and the like, and the more attempts or catches a player has, the less he’ll be regressed.

Normalize each player’s two years based on the league average, and we’re finished.

Let’s see how these aging curves stack up for each position.

(Note: My data sample ran from 1980 to 2007. I looked at quarterbacks with 50 attempts, running backs with 30 attempts, and wide receivers with 20 receptions in Year X, no matter how many they had in the next year, because I weighed each of the players’ two years by the minimum number of attempts or catches in the back-to-back years. I excluded any players who switched teams mid-year or in the offseason.)

(Another note: Unless otherwise noted, aging curves shown are "chained" and then divided by the peak level. That is, if yards per attempt falls two percent every year from age 21 to age 30, then age 21 would be given a value of 1.00, age 22 would be 0.98, age 23 would be 0.98 x 0.98, or about 0.96, and so on until 0.834 at age 30. The numbers are then divided by the peak value, 1.00 at age 21. In other words, a player’s yards per attempt would be 83.4 percent of what it is at age 21.)

Quarterbacks

Some might say that a quarterback’s best years are in his mid-20s, pointing to common knowledge and the successes of Daunte Culpepper and even Marc Bulger. Others may bring up Tom Brady, Peyton Manning, Kurt Warner, and Brett Favre as guys who succeeded well into their 30s.

Who’s right?

To see this table in graphical form, click here . (The values are adjusted up or down slightly so the lines won’t collide.)

According to the table, a quarterback’s peak age is 25 for all but one of the stats, with 26 to 28 not far behind. There seems to be a steep, upward trend at the beginning of a quarterback’s career, and a gentler fall from their peak.

Notice how the curves of touchdowns and interceptions are much more extreme compared to completion percentage and yards per attempt, which have a moderate slope.

Not coincidentally, touchdowns and interceptions both have a worse year-to-year correlation than do completion percentage and yards per attempts—in other words, it’s more likely for a player to have abnormal numbers in TDs and INTs than in completions or yards.

A quarterback’s biggest statistical jump is from age 24 to age 25: Take a league-average 24-year-old, and the next year (in 450 attempts), he’ll add 1.6 points to his completion percentage, 67 passing yards, almost two touchdowns, and two-fifths of an interceptions.

That equates to 11 more fantasy points over the course of a season—or roughly the difference between the Nos. 13 and 19 quarterbacks last year.

Last year, Jay Cutler and Aaron Rodgers made the leap from their age-24 to their age-25 seasons. This year? Well, Brady Quinn is the only qualifying player who seems to have at least a part of the starting job locked up; Tyler Thigpen, Kevin Kolb, Drew Stanton, and Troy Smith round out the 25-year-olds.

Quinn showed some promise last year, throwing for 239 yards and two touchdowns in a Week 10 game against Denver (though he followed that up with 14 completions in 36 attempts for 185 yards at Buffalo). Think about picking Quinn late in your fantasy draft, and it could pay off.

Age isn’t the only way to split up quarterbacks; experience matters, too. Although an "experience curve" looks roughly the same as an aging curve, we see that rookie quarterbacks have a major increase in production to their sophomore season. (So much for the sophomore slump.)

Rookies had a four percent increase in completion percentage and yards per attempt, a five percent increase in touchdown percentage, and an eight percent decrease in interception percentage.

Both Matt Ryan and Joe Flacco would see a 10-point increase in fantasy points if those percentages held up. Those 10 points wouldn’t have moved Ryan up in his rank among QBs last year (in terms of fantasy points), but it would have moved Flacco from No. 19 to No. 14, right behind Ryan.

Though Ryan and Flacco may regress to the mean, the increase from their second-year experience may offset that regression.

Running Backs

Few running backs have had the late-career outburst that John Riggins did. Riggins had just one 1,000-yard season through age 28, before a miraculous career turnaround that included four 1,000-yard performances in the next six seasons, not including his 533 yards in eight games in the strike-shortened 1983 season.

From age 22 to age 28, Riggins had 4,655 rushing yards and 28 touchdowns; Riggins had 2,000 more yards and almost 50 more touchdowns from 29 to 36 in the same amount of time (he skipped his age-31 season due to contract disputes).

Let’s see if that trend holds steady for every running back.

Click here for this table in a graph.

The vertical lines on the graph show the values at age 29 and 30. You can clearly see that each stat falls after a running back’s age-29 season, but is the myth that 30-year-old running backs see a dramatic decline any true?

Not really. Age-30 backs lose 1.26 percent off their yards per attempt (.937 divided by .949 in the table above, then subtracted from one) and 2.13 percent of their touchdown rate (.862 divided by .881 and subtracted from one)—a drop of 15 yards and one-fourth of a touchdown for a running back with 1,200 yards and 12 touchdowns.

To put it bluntly, don’t downgrade LaDainian Tomlinson, Brian Westbrook, Jamal Lewis, or Larry Johnson on your running back rankings because they are turning 30. That is, unless you’re afraid of losing less than three fantasy points over the course of a season.

Running backs tend to peak around 22 to 25, specifically at 23 and 24. Fantasy points per attempt seem to have, more or less, a triangular pattern from 21 to 26, but then it ever-so-slightly increases with no set pattern until age 32.

This can most likely be attributed to selective sampling. The longer a running back plays, the more chance for them to peak later in their careers, a la John Riggins.

In addition, if a guy is playing into his early-to-mid 30s, chances are he’s been productive late in his career; if a guy fades away at age 31, with, say, a drop of 500 rushing yards from his previous year, he’s going to get little carries the next year—if he isn’t retired by then.

Running backs have a very short period of increasing performance. Rushing yards per carry increase from 21 to 22, but decrease all but two of the next 11 years; touchdowns per carry increase from 21 to 23 but decrease four of the next five years.

Of course, the largest overall increase for a running back is between his age 22 and 23 seasons, in which his touchdowns go up almost 12 percent and fantasy points increase by 2.7 percent. (Rushing yards, however, decline by about 1.2 percent.)

Steve Slaton, Marshawn Lynch, and Kevin Smith headline this year’s age-23 class. That said, don’t expect a huge breakout: If we adjust each of their 2008 stats based on their expected increase, all of them would have a four- or five-point increase in fantasy points.

Neither Slaton nor Smith would have their rank among running backs in fantasy points changed, while Lynch would move up three spots from No. 15 to No. 12.

Thirty-two-year-old running backs had the biggest decline in production, losing 10 percent of their yards per carry, 16 percent of their touchdowns, and 12 percent of their fantasy points. This year, however, only two New England backs are turning 33: Fred Taylor and Kevin Faulk.

I think it’s safe to say that neither will perform as the top-10 backs you expected in May.

Wide Receivers

Conventional wisdom says that wide receivers have a longer peak than most other positions.

Take one look at the best receivers in the NFL today—Larry Fitzgerald, Randy Moss, Steve Smith, Reggie Wayne, Chad Johnson, to name a few—and it becomes clear that the best wideouts typically have numerous 1,000-yard seasons during their prime.

The numbers, unsurprisingly, back up this notion.

A graph of this data can be found here.

Wide receivers have the latest and longest peak of any skill position. Their best year is at age 27, but ages 24 to 30 aren’t far behind. In fact, in terms of yards per receptions, all but one of those six years (excluding age 27) are within one percent of the peak level.

In other words, a wide receiver with a true talent level of 1,000 yards in a constant number of catches will be within 10 yards of that level all but one year from 24 to 30. (He’d be within 13 yards in that one year.)

This also suggests that a receiver’s breakout or banner year is more reliable in establishing a new talent level compared to those of quarterbacks or running backs. (As well, receiving yards per catch has a higher year-to-year correlation than any passing or rushing stat.)

A receiver’s largest statistical jump unquestionably occurs from age 23 to age 24, when yards per catch increases by 3.2 percent and touchdowns per catch goes up 7.7 percent.

That means a receiver with 1,200 yards and seven touchdowns—who’d rank No. 12 among receivers in fantasy points—would gain almost 40 yards and half a touchdown (seven fantasy points), and would jump four spots to No. 8.

If you didn’t respect him before, it’s hard not to now: Calvin Johnson is turning 24. The second-year receiver finished fifth in the league in receiving yards with 1,331 and tied for first in receiving touchdowns with 12. In addition, Johnson’s average of 17.1 yards per catch was the second-highest of any receiver with 60 catches since 2006.

Applying the aforementioned age adjustments, Johnson gains over 40 yards and one touchdown to his already absurd statistics.

Another receiver turning 24 is Miami’s Ted Ginn Jr. The ninth pick in the 2007 draft, Ginn started 14 games last year and caught 56 balls for 790 yards, 570 more than his rookie season total. Ginn finished No. 33 among receivers in fantasy points last year, but most Web sites rank him around No. 40 for the upcoming season.

Ginn could beat out those prognostications by a large margin if the age-24 increase comes about.

Interestingly, both Johnson and Ginn are also entering their third season. We hear fantasy analysts claiming that receivers break out in their third season, because it takes two to acclimate to the NFL. Is this belief actually a renaming of the age-24 jump?

If so, the increase from a receiver’s second to third season would not be as large as the increase from age 23 to age 24.

And we see just that.

You can find a graph of this data here.

A receiver’s yards and touchdowns both peak in their third year—though that doesn’t necessarily confirm the myth. From their second year to their third, wideouts gain 1.4 percent to their yards and 4.2 percent to their touchdowns, compared to 3.2 and 7.7, respectively, from age 23 to age 24.

It appears as if the third-year wide receiver myth is actually the age-24 increase in disguise.

How NFL Statistics Lead to Wins, Pt. 3: Salary Cap Efficiency Since 2000

Zach Fein — Sat, 20 Jun 2009 00:52:15 -0400

Even an incompetent owner can spend money. It takes an intelligent one, on the other hand, to spend that money efficiently.

In a time when multi-billion-dollar entities are going bankrupt, teams are looking to spend less money on free agent signings and draft picks.

The Tampa Bay Buccaneers, for instance, were $38 million under the cap prior to free agency. They re-signed just six of their 12 free agents and signed just five others, only one of which—running back Derrick Ward—has a chance to start.

In mid-May, Tampa Bay was sitting at $37 million under the cap, and since then the only transaction they’ve made was signing their fifth-round pick, offensive tackle Xavier Fulton, to a $1.93 million deal with a $181,000 signing bonus.

The Eagles weren’t any better: Sitting $29 million under the cap, Philadelphia re-signed one of their five free agents, wide receiver Hank Baskett, to a one-year deal.

Of their other four free-agent signings, only one was signed to a multi-year deal: tackle Stacy Andrews, to a six-year deal reportedly worth $42 million.

Though they traded for tackle Jason Peters and agreed to a six-year, $60 million contract, Philadelphia currently has $30 million in cap space, according to Pro Football Talk.

More than ever, franchises are cutting back on spending, even making moves to simply reach the salary floor.

But, nevertheless, owners and presidents still want the results on the field; they aren’t tanking games at the expense of saving a few million dollars here and there.

No, they aren’t hoping that their lack of expenditures leads to a lack of wins, but merely a cost-efficient approach of getting into the playoffs.

In the final part of this series, we’ll measure which teams are the best at spending as little money as possible to get the results the fans want. Which teams get the most bang for their buck?

I pulled salary data from USA Today’s salary database for every player since 2000. I then totaled up the cap values for each team, as well as the cap value of offense and defense, respectively, and normalized these values based on the year.

(Note that the sum of a team’s player’s cap values are not the same as its total payroll as noted by USA Today—the sum of total salaries is. But total salary, as shown, includes a player’s full signing bonus, so a guy like Ben Roethlisberger, who had a $25 million signing bonus but a cap value of just $7.97 million, has a total salary north of $27 million. As well, the total payroll went over the 2008 salary cap of $116 million for 13 of 32 teams and does not reflect a team’s actual salary.)

Then, using the regression equation in Part 2, I calculated the wins (we’ll call them xWins)—and, like the team salaries, the xWins from just the offense (xOW) and defense (xDW), respectively—for every team since 2000 and compared salary to wins as well as many other stats of note.

Let’s start with individual team seasons. Do the undefeated 2007 New England Patriots come in as the most profitable team of the century?

In the table below, you’ll find the top 10 and bottom 10 teams since 2000 in each of eight different stats. To find cost-efficiency, salary is divided by xWins—the less a team spends per win, the more economical it is.

I have also included efficiency for both sides of the ball as well. For defense, however, xDWins are first added to games played before being divided by defensive salary. Why? Here’s an example:

Team A spends $50 million on defense, and its defense is worth minus-8 wins; their salary-per-win would be minus-$6.25 million.

Team B spends $60 million on defense, which is also worth minus-8 wins; their salary-per-win is minus-$7.5 million. Team C spends $50 million and gets back minus-10 wins from their defense; their salary-per-win is minus-$5 million.

Team A is obviously the most cost-efficient, but their money-per-win is in the middle of the three teams; there’s no way to sort them for A to come out on top.

By adding 16 to each team’s xDWins, Team A’s defense comes out with the lowest salary-per-win of the three teams. (The 16 is a fudge factor, yes, but it’s the only way to come up with correct results using the salary-per-win method.)

All dollar values are in millions of dollars.

Key:
$/W: Salary per xW
O$/W: Offensive salary per xOW
D$/W: Defensive salary per xDW, which is first added to games played
$: Team salary normalized based on year
$ SD: Standard deviation of team’s player’s salaries, adjusted based on team’s average salary; a lower number means a team spread the wealth of their salary between its players
xW: Wins based on the regression equation in Part 2
xOWAA: Wins based on offensive stats only, above the average for that year
xDWAA: Wins based on defensive stats only, above the average for that year

The 2001 49ers had the seventh-lowest team salary since 2000, but their 12.2 xW earned them the top spot among the most economical team of the decade.

Their quarterback, Jeff Garcia, had a cap value of $566,666 yet was elected to the Pro Bowl with a 94.8 passer rating.

Center Jeremy Newberry earned a Pro Bowl berth despite a salary less than $600,000, and safety Zack Bronson had seven interceptions and a salary of $498,000.

Three teams from this past year top the list for the most profitable offenses. The Falcons paid just over $8 million for Matt Ryan, Michael Turner, and Roddy White, two of whom went to the Pro Bowl, and the other of whom (Ryan) had an 87.7 passer rating in his rookie season. Atlanta paid $32 million for 11.9 offensive wins.

Miami’s profit came from its high-paid players: Ronnie Brown was elected to the Pro Bowl after 786 total yards and nine touchdowns in his first 10 games; Chad Pennington was paid $4 million, and his 97.4 passer rating was second in the league; and left tackle Jake Long reached the Pro Bowl in his rookie season. The Dolphins paid $35 million for 12 offensive wins.

Denver would have ranked much higher if not for millions spent on backups. Its highest-paid offensive player—tight end Daniel Graham—caught 32 passes and was second on the depth chart to Tony Scheffler, who earned $3.5 million less than Graham.

Their second-highest-paid player was center Tom Nalen, who missed the entire 2008 season with a torn bicep and eventually retired because of the injury. And their Pro Bowl quarterback, Jay Cutler, was actually paid less than backup Patrick Ramsey, who had three attempts last year.

Without those three, their salary-per-xOW drops below $2 million.

Perhaps the biggest surprise on these lists is that the 2—14 2006 Oakland Raiders had the second most cost-efficient defense—until you remember that their defense was actually pretty decent. (Their 10.5 points scored per game, however, was the fifth-lowest since 1980.)

The Raiders were fifth in the league in yards allowed per play, third in adjusted net passing yards per attempt (and seventh in opponent passer rating), and 13th in rushing yards per attempt. Their defense was above average in xDW, and only the 2004 Indianapolis Colts spent less on defense than the Raiders.

Five Colts teams make the top 10 for standard deviation of player salaries. The ‘03 Colts, who lead the list, spent $15 million on Peyton Manning, $7.7 million on Marvin Harrison, and over a million on 10 others, including $5.4 million on defensive end Chad Bratzke, who ended up starting just three games with three sacks in what was the final year of his career. They also started five rookies, who had an average salary under $500,000.

Based on pure xOW, the ‘04 Colts had the best offense of the last nine years, with the ‘07 Patriots at No. 2. But after adjusting for league average, the Greatest Show on Turf moniker proves correct—the 2000 St. Louis Rams’ offense jumps to the top.

The ‘00 Rams scored what was at the time the third-most points in a season (540), and had the most passing yards, passing touchdowns, and rushing touchdowns—and the second-highest rushing yards per attempt—in the league that year.

Running back Marshall Faulk had over 2,000 yards from scrimmage for the third straight year (he’d accomplish the feat again in ‘01 as well), and set a new NFL record with 26 total touchdowns despite missing two games. His 81 receptions remarkably placed him just third on the Rams.

Kurt Warner had over 300 yards in each of the first six games of the season (he was on pace for over 6,000) and had 17 touchdowns in that stretch, before a broken hand cost him five games. Backup Trent Green took over and had three more 300-yard games. Warner and Green combined for over 5,492 yards, the highest in NFL history.

Interestingly, only one of the top—10 teams in xOWAA won the Super Bowl that same year: the 2006 Colts. Yet the top three teams in xDWAA each won the Super Bowl, as well as the No. 6 team on the list, the ‘03 Patriots. Certainly, this gives some credence to the belief that defense wins championships.

Which brings us to the most economical franchises of the decade. The Patriots have been lauded for their front-office management—but are they really the most cost-efficient? Or do the rival Colts beat them out?

The table below lists each team’s yearly average this decade along with salary efficiency data.

All dollar values are in millions of dollars.

(Want to be able to sort each column? Click here.)

Key:
$/W: Salary per xW
O$/W: Offensive salary per xOW
D$/W: Defensive salary per xDW, which is first added to games played
$/Yr: Team (unadjusted) salary per year
$ SD: Standard deviation of team’s player’s salaries, adjusted based on team’s average salary; a lower number means a team spread the wealth of their salary between its players
xW: Wins based on the regression equation in Part 2
xOWAA: Wins based on offensive stats only, above average (9.21 xOW)
xDWAA: Wins based on defensive stats only, above average (-9.25 xDW)

The Colts and Patriots have spent about the same amount of money this decade, yet the Colts have averaged one more xW per year than New England, whose 10 xW per year ranks them fourth in the league.

The Patriots, nevertheless, have won the most games in the nine seasons since 2000 (102, one more than Indianapolis) and rank slightly higher than the Colts in salary per actual win (just $30,000 less).

Meanwhile, the Dolphins rate No. 1 in terms of offensive salary per xOW, despite a below—average offense as shown by their xOWAA; Miami spent $3.78 million less per year than any other team on offense this decade. (Their total money spent on offense was even less than the Texans, who played two less years than Miami.)

The Colts are on top of xOWAA by a large margin, but they rank fifth in offensive salary per xOW because they spent the most money on offense, as well as the largest percent of total salary on offensive players (59.3 percent).

Baltimore’s defense has been worth 2.35 xDWAA, more than half a win above Pittsburgh’s—but the Steelers spent $4 million less than the Ravens each year, earning them a lower salary spent on defense per win.

The Eagles’ average salary has been the highest in the NFL since 2000, $2 M more than the next-highest team. (In fact, they own four of the 10 highest-spending teams this decade, as shown in the first table.)

Their high salaries drop them from second in xW to sixth in $/W; from eighth in xOWAA to 19th in O$/W; and from third in xDWAA to sixth in D$/W.

Philadelphia is also in the bottom 10 in standard deviation of individual salaries, despite having to pay Donovan McNabb, Javon Kearse, and Tra’ Thomas, among others, in the past nine years.

The importance of salary cap efficiency is reflected, simply, in the rankings: The top six teams in money spent per xW have won seven of the nine Super Bowls with two Super Bowl losses this decade, plus a total of 36 playoff appearances and 45 playoff wins.

In baseball, an owner can spend all he wants in order to win a World Series. With a salary cap and penalties for going over the cap, however, NFL teams must manage their payroll economically in order to stay atop the standings at the end of the season, like the Colts, Patriots, or Steelers.

Why Raul Ibanez's Breakout Is Not Caused by Steroids

Zach Fein — Fri, 12 Jun 2009 00:48:30 -0400

Much has been made about Jerod Morris’ blog post regarding Raul Ibanez’s unexpected performance thus far. The post attempts to bust any steroids claims using statistical evidence—not to accuse him of taking performance-enhancing drugs, as the media is making it out to be.

In his final three years as a Seattle Mariner, Ibanez hit a respectable .291/.354/.492, with an average of 26 home runs and 113 RBI per year. This year, his first in the National League in his 14-year career, the 37-year-old is hitting .325/.380/.671 with 20 home runs and 55 RBI.

Ibanez has just three fewer home runs this year than the 23 he hit in 2008, in 452 fewer plate appearances.

Only once in his career has Ibanez hit over .300, and never has he had an OPS above .900. He’s on pace to smash his career highs at age 37, something seen in recent memory only by Barry Bonds.

There are two main factors that could be the cause of his offensive explosion. First, his switch from the AL to the NL and from Safeco Field to more hitter-friendly Citizens Bank Park; and second, random fluctuations resulting from lack of significant sample size.

Let’s deal with the former first. Based on players who moved from the AL to the NL since 2000, and Safeco Field’s and Citizens Bank Park’s four-year park factors from 2005 to 2008, Ibanez would have expected his batting average to increase by 5.8 percent, his on-base percentage by 6.2 percent, his slugging percentage by 10.1 percent, and his home run rate by 20 percent—just by his moving to Philadelphia.

(Those are multiplicative, not additive, by the way. Batting average is multiplied by 1.058, for example, and not increased from, say, .300 to .3058.)

If we adjust his 2006-08 stats from Seattle accordingly, his previously good batting line is now great: .308/.376/.541, with 31 home runs and 116 RBI per year.

His current OBP of .380 is in line with his adjusted OBP, but the slugging percentage is where the major difference lies—an actual .671 versus the adjusted .539.

Why is the disparity so large?

Ibanez has hit 20 home runs in just 80 fly balls, a HR/FB ratio of 25 percent. The league average falls around 10 or 11 percent; Ibanez’s was 10.7 and 10.9 percent each of the past two years, respectively.

From 2006 to 2008, Ibanez’s HR/FB percentage was 12.7 percent. Our estimate for his HR/FB percentage this year is about 14.6, which includes a 20 percent increase and a slight regression to the mean (15 percent, to be exact).

This means that we would expect 11 or 12 home runs in 80 fly balls for Ibanez. (By the way, if we prorate 12 home runs in his 255 plate appearances to an average of 681 in his last three years in Seattle, we’d get an average of 32 homers per year. We previously estimated 31 home runs in Philadelphia for Ibanez.)

If we then take away eight of his 20 homers—and add four doubles, assuming half of those eight are outs and half are doubles—his slugging percentage falls to .566 and his OPS to .946. And if those eight non-homers turned out to be all outs, his actual performance this year would actually be worse than what his adjusted stats estimated.

The second hypothesis as to why Ibanez’s stats are so high is random fluctuation. In THE BOOK: Playing the Percentages in Baseball, the authors show that random fluctuation (known as standard deviation, or SD) in a binomial (such as on-base percentage, in which there are only two options—in this case, reach base or get out; slugging percentage, however, is not a binomial) is calculated as

,

where OBP is the player’s true talent OBP (or any other binomial) and N is the number of plate appearances, currently 255 for Ibanez. My preseason projection for Ibanez was a .359 OBP in the NL and in Philadelphia, but that is equal to an OBP of .348 in a neutral league and park, which is our estimate of his true skill.

Thus, the random fluctuation in Ibanez’s OBP is .03. There’s a 68 percent chance that Ibanez’s current OBP is within one SD of his true talent and a 95 percent chance that it’s within two SDs. (We’ll use his projection instead of his true talent, however, because we want both his projection and actual stats to be in the same environment—Philadelphia.)

In other words, if Ibanez repeated this season 100 times with the same true talent, surrounding cast, and other variables, his OBP would be anywhere from .329 to .389 (within one fluctuation of his true talent) 68 times, and between .299 and .419 all but five times, all due to random variation.

That means that Ibanez, with his .380 OBP, is currently within the range of our projection; his actual OBP is just 0.021 better than his projection, less than one SD.

Applying this to batting average, we see Ibanez is hitting 37 points better than his preseason projection of .288. The fluctuation for his average is 0.028, which means Ibanez is well within two SDs of his projected average.

The outlier is, of course, home runs. We calculate that the fluctuation of Ibanez’s home run percentage (home runs per plate appearance, not per at-bats) is 0.011, but his current percentage of 7.8 percent is over three-and-a-half SDs above his preseason projection of 3.8 percent.

(The probability that he’d finish above three SDs from his projection is just 0.3 percent, which shows that his home run percentage is due for a major regression.)

But remember that eight of his home runs were due to his inflated HR/FB rate? The 12 homers he should have based on his prior HR/FB rate results in a 4.7 home run percentage, less than one fluctuation, which suggests that Ibanez’s 12 “true” home runs actually somewhat represents his true skill level.

The stats show that aside from his insanely high HR/FB rate (20 home runs in 80 fly balls), Ibanez’s current stats are not too far off from his true talent level. Both his on-base percentage and home run percentage are within one fluctuation of his projection, something we’d see 68 percent of the time, and his batting average is within 1.3 SDs of his projection.

Steroids? Nope.

How NFL Statistics Lead to Wins, Pt. 2: Quantifying Player Stats into Wins

Zach Fein — Thu, 28 May 2009 00:54:16 -0400

In Part I of this series, we saw how various stats correlated with wins and points scored. Among the observations:

• Rushing attempts correlate better with wins than do passing attempts, because teams with the lead will rush the ball to run out the clock late in the game; but passing statistics—including yards and touchdowns per pass attempt—lead to wins more than rushing stats per attempt do.

• Tackles have a negative correlation with wins, meaning that as tackles go up, wins go down. This is possibly due to the fact that worse teams have less three-and-outs and are on the field on defense longer than better teams; however, if we normalize this by dividing tackles by opponent plays from scrimmage, we get no significant results—solo tackles per play have a -0.18 correlation with wins, and that correlation is only -0.05 using tackles per play.

• Special teams have relatively no effect on team wins.

You can find a table with every one of the 127 stats I took from Pro-Football-Reference and its correlation with wins, points scored, points allowed, yards, and yards allowed here. I also included the difference in each stat for every team, such as points scored minus points allowed.

REGRESSION

Correlation is a fine measure of how important a statistic is in terms of wins or points or yards.

What it doesn’t tell you, however, is how many wins a stat is worth to a team. Using a regression, we can find out the number of wins a team adds to its record by completing an extra pass, or throwing an extra touchdown, or intercepting the opposing quarterback once more.

A regression spits out an equation to estimate a set of values (team wins, in this case) based on independent variables (in this case, a team’s stats). In Part I, I went over this in greater detail.

The regression included 14 reasonable player stats: completion percentage, passing touchdown percentage, interception percentage, net yards per attempt (pass yards minus sack yards divided by attempts plus sacks), rushing yards per attempt, rushing touchdown percentage, and fumble rate (fumbles divided by completions plus sacks plus rush attempts), all for both offense and defense.

I neglected to add stats such as points scored and allowed because they only add up at the team level and not the player level; they can be estimated for players as touchdowns multiplied by six or seven, but in the end, you’d be counting touchdowns twice (for points and touchdowns, obviously).

The table below shows the results of the regression in terms of wins. The first column represents the actual number of wins each stat is worth. The second represents the coefficient after standardizing each stat; this column shows the relative value of each stat compared to all the others.

The equation ends up like so: Wins = O Comp % x 1.34 + O Pass TD % x 45.98 + … + 8.02.

The regression equation says that for every extra point added onto completion percentage, you add 0.0134 wins, and so on. (One full point—which would make the completion percentage about 1.60, an impossible number—is 1.34 wins, and in this situation, every point is equal to one hundredth of a full point; 1.34 divided by 100 is 0.0134. For all the percentage stats, it’s easier to look at it this way than by the actual coefficient.)

We can look at the equation in simpler terms by converting the percentages into totals. The average team last year attempted 516 passes and completed exactly 61 percent of them. To complete 62 percent, the average team would have had to complete 5.16 more passes than they actually did. We can then say that 5.16 completions equals .0134 wins, or that 385 completions equals one full win.

(Note: This means that you would have had to complete 385 more passes in the same amount of attempts to equal one win—not that completing 385 of the next 385 attempts equals one win. That would be impossible to do using last year’s average stats, but that just shows how unimportant completion percentage is to wins.)

Using this technique, we can find how many of each stat equals one win.

Look at it this way: A team completely average in everything—same number of pass attempts, completions, etc.—will win eight games. That same exact team, but with 11 more touchdowns, will win nine games.

INTRODUCING WINS ABOVE AVERAGE

With the exception of video games, the idea of player ratings for the NFL has never been sold to the general public. You’ve got passer rating and…fantasy points? There’s no definitive stat that ranks running back, wide receivers, or even quarterbacks. (Passer rating is flawed. The weights are unbalanced and completely arbitrary, and since the league average rating increases almost every year, you can’t compare past generations.)

Think about this: Which quarterback’s statistics are better—Quarterback A, who throws for 4,500 yards, 35 touchdowns, 20 interceptions, and a completion percentage of 60 percent; or Quarterback B, who throws for 3,800 yards, 27 touchdowns, 10 interceptions, and a completion percentage of 67 percent?

Or how about this: Running Back A, who runs for 1,500 yards and 10 touchdowns; or Running Back B, who has 1,200 rushing yards and 12 touchdowns in 50 less attempts?

Quarterback B has a better passer rating than Quarterback A, and Running Back B is slightly better on a per-carry basis than Running Back A. But Quarterback and Running Back A have more fantasy points than their counterparts, and in this fantasy-crazy world, chances are they may be labeled as the better players.

With the lack of useful, comparative stats available, we can never quantify how much better one player is than the other.

With the regression equation above, however, we can.

We saw that completion percentage is worth 1.34 wins, passing touchdown percentage is worth 45.98 wins, and so on. Thus, we can estimate player wins as 1.34 x completion percentage + 45.98 x touchdown percentage + … — 33.20 x fumble rate.

For wide receivers (and running backs’ receiving stats), we use targets as “attempts,” and receptions as “completions.” The coefficients of the passing statistics stay the same, so for a receiver, the equation for wins can be estimated as 1.34 x (receptions ÷ targets) + 45.98 x (touchdowns ÷ targets) — 33.20 x fumble rate.

But there’s one caveat: We must account for the fact that a receiver’s targets do not solely comprise a team’s 500-plus pass attempts. Once we find a receiver’s unadjusted wins, we multiply that number by the percentage of attempts that were targeted to the receiver (or, targets ÷ team pass attempts).

We can express wins as points or point differential as well. Since it takes about 34.5 points to make a win (see end of article for details), we can multiply wins by 34.5 to find how many points a player has created; or, equally, wins above average multiplied by 34.5 equals points above average.

If we divide the latter number by 16, we find how many points that player adds over the average player at his position per game. In other words, if you were to take a team full of average players that scores 21 points per game, and swap that team’s running back for one that is four points above average per game, they would score 25 points per game after the switch.

I have also used these ratings to show which players were underpaid or overpaid last year, using salary earned based on individual wins. For quarterbacks, each win was worth $0.75 million; for running backs, $1.70 million; and for wide receivers, $1.72 million. These numbers were found by dividing the combined salary of all qualifiers (200 pass attempts, 100 rush attempts, and 40 receptions) by the total number of wins between them.

QUARTERBACKS

Without further ado, here are the wins above average for every quarterback with 200 attempts last year. I took player wins and subtracted 8.10, the average for the 34 quarterbacks.

Here’s a simple explanation to wins above average: Philip Rivers, for instance, is 2.65 wins above average. If you take a team full of average players (as in the example for points above average per game), and swap their quarterback for Rivers, they would win 10.65 games—the eight prior to the switch plus the 2.65 added on by Rivers.

(That may be not fully correct, though; Rivers is benefited from playing with one of the best running backs in the game in LaDanian Tomlinson, as well as Vincent Jackson and Antonio Gates. The figures shown below are not team-independent, per se, as it’s nearly impossible to separate Rivers from his receivers, running backs, coaching staff, or playbook.)

For quarterbacks, fumble rate was defined as fumbles ÷ (incompletions + sacks + rush attempts). I included rushing yards, but took out rushing touchdowns because some quarterbacks—such as Kyle Orton, who has three rushing touchdowns in 24 attempts—have such high rushing touchdown rates that it completely skews their wins total; as well, most quarterback rushing touchdowns are from inside the five-yard-line and are a result of other factors that lead the possession to the five—e.g., the quarterback’s passing, the rushing game, penalties, or good field position resulting from a defensive stop.

Two names that stand out are Tony Romo and Kurt Warner, Nos. 14 and 15. Their inability to hold onto the ball (over 11 fumbles for each of them) knock off 1.8 wins from Romo’s total and 1.5 from Warner’s. In fact, if I disregarded rushing stats altogether, Romo and Warner would be Nos. 3 and 4, respectively; their rushing drops them both 11 spots.

The three most underpaid quarterbacks last year were Matt Cassel (paid $5.87 million less than he deserved), Tyler Thigpen ($5.85 million), and Seneca Wallace ($5.39 million).

Ben Roethlisberger (paid a whopping $22.9 million more than he deserved), JaMarcus Russell ($11.3 million), and Brett Favre ($7.09 million) were the three most overpaid quarterbacks in 2008. Roethlisberger was the highest-paid player from any position last year, and JaMarcus Russell just showed how badly the league needs a rookie wage scale.

RUNNING BACKS

Third—that’s where LenDale White would have ranked if wins were calculated just as the regression showed.

Sixth—that’s where Tim Hightower would have ranked if wins were calculated just as the regression showed.

You can see where this is heading. Because the regression equation placed so much emphasis on rushing touchdowns, backs that get the load of the red zone carries for their team—such as White, Hightower, and Brandon Jacobs—would place among the league’s best.

Rushing touchdowns are a valuable measure at the team level, but not at the player level, in which the Mike Alstotts of the world can earn nine or ten wins from their touchdown percentage alone. (Alstott’s six touchdowns in 34 attempts in 2005 would have been worth 9.8 wins.)

The solution to this is to find the percentage of his team’s total rushing touchdowns that a running back’s touchdowns make up, and then multiply this figure by the number of wins created by a back’s rushing touchdowns (55.71 x touchdowns ÷ attempts). Add this to the rest of his wins, and subtract 2.15 wins—the average for those with 100 rush attempts—and you have wins above average.

Fantasy football owners will recognize Pierre Thomas’s name at No. 7. Thomas had a touchdown in each game and over 100 total yards in all but one from Weeks 11 to 16. He averaged 17 touches for 113 total yards per game, with a total of nine touchdowns and one fumble during the stretch.

Adrian Peterson and his league-leading 1,760 rushing yards rank No. 30 and below average. Let’s go through each stat one-by-one to see why.

Yards per carry: Peterson had a 4.85 YPC, good for 1.31 wins. The average of the 49 backs with 100 attempts was 4.21, or 1.14 wins. Peterson is +0.17 wins so far.

Rushing touchdowns per carry: Peterson had 10 rushing touchdowns in 363 attempts; the league average, prorated to 363 attempts, was just over 12 touchdowns. But including the fact that the Vikings had less rushing touchdowns than the average, Peterson gained 0.24 wins over the average. He’s now at +0.41 wins.

Fumble rate: Peterson fumbled nine times in 384 attempts and receptions. In that many fumble chances, the league average back would have fumbled 4.1 times, half of Peterson’s total. Peterson’s minus-0.78 wins from his fumble rate means he was 0.42 wins less than the league average. He’s at minus-0.01 wins.

Receiving: Peterson averaged just 3.9 receiving yards per target, had no receiving touchdowns, and caught 54 percent of his targets. His receiving game earned him just 0.35 wins, or 0.27 wins below league average. All combined, Peterson was 0.28 wins below average in 2008.

DeAngelo Williams (worth $5.45 million more than his actual salary last year), Pierre Thomas ($4.80 million), and Maurice Jones-Drew ($4.74 million) were the three most underpaid running backs last year.

The three most overpaid were Michael Turner (whose production was worth $11.3 million less than he actually received), Marion Barber III ($8.90 million), and Steven Jackson ($8.04 million).

The standard deviation of the 49 running back’s wins above average is 0.78, while for quarterbacks it is 1.27. That suggests that running back production is more tightly packed than quarterbacks, and that there’s a steeper drop-off for quarterbacks than running backs.

WIDE RECEIVER

Wide receivers were explained in greater detail above. Here’s how the 59 wideouts with at least 40 catches rank.

According to their wins above average, the most underpaid receivers last year were Roddy White (paid $6.31 million less than what he actually “earned”), Greg Jennings ($4.98 million), and Vincent Jackson ($4.75 million).

Larry Fitzgerald (deserved $11.3 million less than his actual salary), Randy Moss ($9.33 million), and Terrell Owens ($8.81 million) were the three most overpaid.

Now, that isn’t to say those three were actually that bad. If you graph actual salaries against worth, you find that actual salary drops three times faster than worth. For instance, the fifth-highest salary was $11.82 million, but Larry Fitzgerald—who was No. 5 in WAA—was worth only $5.73 million. This is because deserved salary based on WAA is linear (in that you multiply WAA by a constant to get worth) and not exponential.

In addition, Fitzgerald is hurt by the fact that wins are found by adjusting for team pass attempts. If every wideout’s team had 516 attempts (the league average), Fitzgerald would have ranked No. 1 in wins above average, but would have been worth only $6.91 million because of linear calculation.

In Part III of this series, I’ll apply individual wins and worth to the team level to see which teams are the most cost-efficient and get the most production out of their payroll.

How did I come up with 34.5 points?

The simple answer: Take a team that both scores and allows 21 points per game. Using the Pythagorean formula—which estimates a team’s record based points scored and allowed—they’d be expected to win eight games no matter what exponent you use (2.37, 2.64, or a floating exponent based on total points per game).

Now, if you add 34.5 points to that team’s points scored, you get about 23.16 points scored and 21 points allowed per game. Run them through the Pythagorean formula, and you exactly nine wins.

(In the second scenario, the exponent I used was (total points scored and allowed per game)^0.25, which yields better results than any other exponent.)

The complex answer: The formula to find points per win is PPW = 2 x (Pts-PtsAll) x (Pts^x + PtsAll^x) ÷ (Pts^x — PtsAll^x), where x is the floating exponent as given above and points and points allowed are per game. That equation comes from David Smyth and Patriot.

I applied this equation to every team season since 1994, and the average points per win was 34.3; the median was 34. I stuck with 34.5 after performing the test above.

The Effects of the Officials in Super Bowl XL Using Win Probabilities

Zach Fein — Sun, 03 May 2009 01:17:31 -0400

Pop quiz: Who said regarding the referees, “I felt they were cheating us,” and “The way the refs were going, I wouldn’t have trusted them in overtime,” and that they were “[taking] the game away from us”?

If you answered anyone from the Seattle Seahawks, try again. The answer is former Pittsburgh Steelers linebacker Joey Porter, who said that after the officials overturned Troy Polamalu’s fourth-quarter interception against Indianapolis in the AFC Divisional Round.

The NFL must have heard his message.

Three weeks later, the Seahawks were not only facing the Steelers in Super Bowl XL, but also Bill Leavy and the rest of the officiating crew. In the biggest game on the biggest stage, the officials made so many questionable calls that benefited Pittsburgh, all they were missing was a Steelers’ helmet.

Seattle was called for seven penalties for 70 yards, and Pittsburgh was called three times for 20 yards.

Said Seahawks coach Mike Holmgren, “I didn’t know we were going to have to play the guys in the striped shirts as well.” Jason Whitlock wrote about the officials, “Am I the only one who would like to hear them defend their incompetence?” Michael Smith said that the officials were “giving [Pittsburgh] the game.”

By all means, Seattle was robbed of the Super Bowl, or at least a shot of winning.

In order to quantify the effect of the officials on the game, I turned to the win probability calculator on Advanced NFL Stats, which I previously used to figure out when to go for the two-point conversion.

By looking at the win probabilities of Seattle prior to and after each of the seven penalties handed to them, I could see exactly how much the Seahawks’ penalties hurt them.

Before I get to Seattle, I’ll show the effects of Pittsburgh’s three penalties as a baseline. PRE shows the Steelers’ situation before the penalty occurred, and POST shows what happened after the penalty.

Expected points come from the win probability calculator, and end result is the final outcome of the drive in which the penalty occurred.

Pittsburgh’s penalties lost them an average of 2.7 percentage points on their win probability and less than half an expected point.

How does compare to Seattle? A chart showing the seven penalties called on the Seahawks and the change in their win probability is below, with a description of each penalty and its effect below that.

First penalty—first quarter, 5:53 left, offensive holding: After an 18-yard pass to Darrell Jackson, Seattle was called for holding. The Seahawks went from first-and-10 from the PIT 23 to third-and-16 at their 49. They were well in field goal range prior to the holding call, but had to settle for a punt after failing to convert the long third-down try.

Second penalty—first quarter, 2:00 left, offensive pass interference: Darrell Jackson caught a 16-yard pass in the end zone for an apparent touchdown. But the back judge—Bob Waggoner, who is a Pittsburgh native—called a late penalty on Jackson that pushed the Seahawks back to a 1st-and-20 on the PIT 26.

Seattle kicked a field goal, but if the touchdown to Jackson had stood, the Seahawks would have been driving for the game-tying touchdown on their final possession (which ended on an incompletion on 4th-and-7 from the PIT 23 with eight seconds left in the game).

Third penalty—second quarter, 14:44 left, offensive holding: Seattle returned the Steelers’ third punt of the game 34 yards to Pittsburgh’s 46-yard-line, but holding was enforced at the SEA 35.

The Seahawks took possession at their own 25 and ended up punting at the PIT 47 on fourth-and-two. The 29 yards Seattle lost would have put them at Pittsburgh’s 17 with a chip-shot field goal if there had been no penalty.

Fourth penalty—second quarter, 1:46 left, offensive holding: The Seahawks committed a holding penalty on the kickoff return following Pittsburgh’s first touchdown, which lost Seattle only 10 yards.

Though that may not have seemed like a big difference—what with just 1:46 left and the ball on Seattle’s 27 after the penalty—the Seahawks managed to drive down to Pittsburgh’s 36 and missed a 54-yard field goal to end the half.

Without the holding call, the field goal would have been 44 yards; then-kicker Josh Brown has made six percent more 40-yard field goals than 50-yarders in his career.

Fifth penalty—third quarter, 4:37 left, false start: The most insignificant penalty by Seattle, the Seahawks went three-and-out and punted. Still, Seattle had a 13-yard pass play on third-and-15, which would have been a first down if not for the false start penalty.

Sixth penalty—fourth quarter, 12:08 left, offensive holding: This, on the other hand, was huge. After an 18-yard pass to Jerramy Stevens that ended up on the Steelers’ one-yard-line, the officials called tackle Sean Locklear for holding. The play dropped the Seahawks to the PIT 29 facing a first-and-20, and their win probability fell 17 points.

Two plays later, on third-and-18, Matt Hasselbeck was intercepted by Ike Taylor. Seven points came off the board, and the Seahawks ended with a turnover just outside the red zone.

Seventh penalty—fourth quarter, 10:46 left, low block: On the interception return, Hasselbeck was called for a 15-yard low block penalty. The Director of Officiating for the NFL—Mike Pereira—even said that “the call was not correct” and “should not have been made.”

Nevertheless, the Steelers got the ball on their 44-yard-line and scored on an Antwaan Randle El touchdown pass to Hines Ward.

Although the argument could be made that the Steelers would not have attempted such a trick play on their own 42 (the play occurred 15 yards ahead, on the SEA 43), Hines Ward was so far ahead of his defender that he would have run the extra 15 yards to the end zone.

Adding up all of the effects on the score of each penalty—an extra three points for the first penalty, four for the second call, three for the third, none for the fourth or fifth (maybe Brown misses the 44-yarder), seven for the sixth, and none for the last call—the Seahawks effectively lost 17 or 20 points due to penalties alone.

That would have made the final score 27-20 in favor of Seattle.

The Steelers, on the other hand, lost no points from penalties—two false start calls came on the same three-and-out (and we don’t know if Pittsburgh would go another 70 or 80 yards to score a touchdown if there were no false starts called), and they scored a touchdown on the drive in which a pass interference call occurred.

Would the Seahawks have won Super Bowl XL without the detriments of their seven penalties?

It appears so.

Star-Struck: Which Sixth Round Quarterback Will Become a Star?

Zach Fein — Sun, 26 Apr 2009 20:20:09 -0400

In my article showing how draft round affects a quarterback’s career performance, I saw that sixth round quarterbacks perform almost as well as third rounders in their careers.

That conclusion wasn’t just due to Tom Brady—Matt Hasselbeck, Jeff Blake, Marc Bulger, Mark Rypien, Stan Humphries, and Steve Bono were among those selected along with Brady in the sixth round.

From 1985 to 2004, there were 38 quarterbacks taken in the sixth; 12 of them never played in the NFL. But of the 26 that did, seven started for five or more years and six went to at least one Pro Bowl (a total of 13 Pro Bowls between those six), and those 26 QBs combined for a career 77.9 passer rating.

Of course, those numbers are less if you assign a replacement-level value to those players who did not take a snap.

This year, Tom Brandstater, Mike Teel, Keith Null, and Curtis Painter were taken in the sixth. Assuming all take an NFL snap, one player will start five years and another will be selected to two Pro Bowls. The question is, Who will it be?

Tom Brandstater (Fresno State)

Selected by Denver as the first pick of the sixth round, Brandstater should have no trouble competing for a starting job. The Broncos’ only two quarterbacks on their current roster are Kyle Orton and Chris Simms.

Brandstater has good size and bulk (6′ 5″ and 220 pounds), but he has an average arm strength and makes too many bad decisions. He was intercepted 12 times in 371 attempts in his senior season last year. His pocket awareness is not too good either, as he was sacked 17 times each of the past two seasons.

Brandstater doesn’t have the skills to be a starter in the NFL, but he may have the best opportunity to start out of the four sixth round quarterbacks.

Mike Teel (Rutgers)

While some pundits thought the Seahawks would take Mark Sanchez in the first round, Seattle took linebacker Aaron Curry and settled with Teel in the sixth. Teel was intercepted 49 times in his 1,142 career attempts, or 24 interceptions in 550 attempts. Teel has very poor decision-making, and I don’t see this pick as anything but a project.

Matt Hasselbeck only has a few years left in him. Teel may be asked to step in when he is done, but I don’t see him as anything special. Seattle would be wiser to choose to take over for Hasselbeck Seneca Wallace, who had a passer rating of 87 when filling in for an injured Hasselbeck last year.

Keith Null (West Texas A&M)

Drafted by St. Louis to apparently be the heir to Marc Bulger, Null put up terrific college stats in his two years as a starter for West Texas A&M. Null had over 5,000 yards last year with 48 touchdowns and 15 interceptions, and he had a completion percentage over 67 in both of his years starting.

But his arm strength is nothing to be proud of, and Null—who took most snaps out of shotgun—often had four receivers to throw to on every down. Null does not have the physical tools needed to be successful in the NFL.

The Rams currently have Kyle Boller and Brock Berlin backing up Bulger. While Null may be the Rams’ No. 2 QB going into the season, he won’t be able to face NFL defenses after two years in Division II.

Curtis Painter (Purdue)

Of the four quarterbacks picked in the sixth round, Painter has the best chance to be a Pro Bowler. Coming into the season, Painter would have been a first or second round pick, but a poor senior season dropped him to the sixth round.

Painter is experienced and has excellent size (6′ 3″, 225 pounds), but scouts say he locks onto his receivers too much and makes too many poor reads.

In his senior year, he had the same number of interceptions (11) as he had the year before, despite 190 fewer pass attempts. Painter finished his college career with a completion percentage of 59.9.

Though Painter has to wait five or six years for Peyton Manning, who’s 33, to retire, five or six years as Manning’s backup can only be good for Painter—just look what it did to Matt Cassel in New England.

Painter would be about 29 when Peyton retires, and there’s no doubt in my mind that he’d be able to put up three or four 3,000-yard seasons.

How Draft Round Affects a Defensive Back's and Linebacker's Career Performance

Zach Fein — Sat, 25 Apr 2009 02:17:17 -0400

With the skill positions and linemen done and examined, there’s just two more positions left for me to look at: defensive backs and linebackers.

In the first four parts of this series, I’ve found that sixth round quarterbacks are nearly as good as third rounders, there’s a significant downward linear trend for a lineman’s performance based on draft round, there’s not much difference between a fourth round and seventh round running back, and there’s an obvious power trend between a wide receiver’s performance and draft round.

I’ve included at the end of this article a rundown of all positions combined into one analysis, with just the draft round as the variable.

Defensive Backs

Pro Bowls

The first graph shows the percentage of all defensive backs who made at least one Pro Bowl, at least two Pro Bowls, and at least three Pro Bowls in their career.

And this graph shows the career Pro Bowls attended by draft round.

There’s a clear pattern here. The drop from the first to second to third rounds is huge, and there’s no difference whatsoever between the fourth and seventh rounds—just like running backs. Let’s see if this is supported by any other data.

Interceptions

The graph below shows the number of career interceptions by draft round.

Similar the to two graphs above, there’s also little difference between the fourth and seventh rounds in terms of interceptions. The slope from the first to third rounds, however, isn’t as steep as the previous two.

So we know that the fourth round isn’t as good as expected. But is it because they have a lot of busts, or because they are lacking any superstars? The next graph shows the chances of having 20 or more career interceptions (or a good pick) and the chances of having five or less (a bad pick). I did not adjust those baseline numbers for each draft round.

What we see is that there’s a lacking of star players after the fourth round, but the number of busts rapidly increases every round.

Years Starting

The graph below, showing years as primary starter by draft round, brings up an interesting point.

Fourth rounders start over half a year more than seventh rounders, even though they don’t perform nearly as better as that would show. Why? Because teams are more likely to keep a higher-drafted player as a starter despite their early foibles than a seventh-rounder with the exact same stats.

The next graph is a twist on the second interceptions graph above. It shows the percentage of players who started at least five years or one-or-less years for his team.

You read it right: Every defensive back drafted after the second round has more than a 60 percent chance of starting just one year in the NFL. After the second round, there’s a less than a 20 percent chance that a defensive back will start for five or more years.

(Sample size note: Every round had above 88 defensive backs in the sample, with an average of 119.)

Linebackers

Pro Bowls

The graphs in this section will be the same as the ones above, except for the obvious difference in data.

A couple of observations: only three of the 94 drafted linebackers in the fourth round were ever selected to a Pro Bowl, and its three percentages are the lowest for any round save for the seventh.

The small space between the orange and red lines show that few linebackers are selected to just two Pro Bowls; 74 percent of those selected to two were elected to at least one more. Only wide receivers had a smaller gap (75 percent) of all the other positions I looked at.

The number of career Pro Bowls, as shown below, mirrors the graph above.

The drop from the first to the second round is substantial, a difference of 1.30 Pro Bowls. The first round had a total of 73 more Pro Bowls than the second round despite having 28 less drafted linebackers.

Both of these graphs show the third round ahead of and the fifth round right below (or above) the second. Let’s consult the other graphs to see if this is the case.

Sacks

Now, I know sacks is an inexact measurement for linebackers, but tackles never became an official stat until this century, and past numbers are nowhere to be found.

Aside from the increase from the fourth to the fifth round, there’s a decline in sacks from each round to the next. When it comes to career stats, the fifth round falls short of both the second and third rounds.

The graph below shows the percentage of linebackers with 20 or more career sacks and five or less career sacks.

The difference between the stars in the third, fourth, and fifth rounds is next to nothing. But the number of busts steadily increases every round except for the fifth round. Still, over 60 percent of third round linebackers have five or less career sacks.

Years Starting

Will teams fall prey to the high-pick-means-high-playing-time theory again?

The graphs above show that the fifth round is better than the fourth and that the third is better (or just equal to) the second round. But teams again stick with their early picks for longer than they should and don’t start lower picks as long as they should.

(Sample size note: Every round had at least 62 linebackers, with an average of 86.)

A Review of All Positions

Pro Bowls

The following graphs show the effects of draft round on a player regardless of position. This first graph shows the chances of being selected to one, two, or three Pro Bowls.

We see a steep decline after round one and an increase from the fourth to the fifth round. When we look at career Pro Bowls, will we see the same?

In this graph, we see a drop of almost 0.80 Pro Bowls from the first round to the second round. The difference of just 0.06 Pro Bowls from the fourth to the seventh round again suggests that there’s not much variation in the latter rounds. From what we’ve seen so far, it would be wise for teams with multiple fourth round picks (such as San Diego, with three) to trade up in the draft for a second or third round pick.

Years Starting and Fantasy Points

But the way the NFL trade value charts are set up suggests that there’s a logarithmic or exponential curve to the value of each pick or round. The next three graphs back this up.

The correlation between draft round and years started is almost perfect—0.998. The formula 6.88 x 0.72 ^draft round produces a near-perfect estimate of years as a starter.

The graph below shows the chances of starting five or more years or one or less years by draft round. Again, the line showing five or more years follows an exponential trend as well, and the line showing busts is nearly linear.

There’s just one more stat to look at: fantasy points. I found the number of fantasy points (with half a point per reception) for quarterbacks, running backs, and wide receivers only—tight ends were excluded because some are drafted solely for blocking—and then found the average career total for each of them.

Although there’s still the obvious exponential trend, the slope isn’t nearly as steep as the NFL draft pick value chart created by Jimmy Johnson. I took the median value in each round as graphed it with career fantasy points; you can see the resulting graph here.

Both value the first round equally, but the chart has every remaining round significantly lower than the career fantasy points. Luckily, however, there’s an almost even relationship between career Pro Bowls and pick value, with the exception of the fifth round.

What Does All of This Mean?

1. When it comes to defensive backs, there’s a big drop from the first to the third rounds, but there’s no huge difference between the fourth round and the seventh round.

2. Fifth rounders perform better than fourth rounders—and nearly as good as third rounders—but they start quite less than both of them, because they have less chance to shine due to their draft round.

3. It turns out that draft round does in fact affect the career performance of every player. There’s a clear correlation between draft round and Pro Bowls, years starting, and fantasy points. The latter two do not have as large as a downward slope as the draft pick value chart, but the graph of Pro Bowls versus pick value is very similar.

How Draft Round Affects a Wide Receiver's Career Performance

Zach Fein — Thu, 23 Apr 2009 00:44:51 -0400

In the first three parts of this series, we've seen how the round in which a quarterback, lineman, or running back is drafted severely affects his career production.

Today, I'll be looking at the careers of wide receivers. With Jerry Rice, Tim Brown, Marvin Harrison, and Randy Moss all drafted high, will it be a runaway victory for the first round?

For players that had no receptions in their career, I changed their stats to those of Troy Walters, a fifth-rounder drafted in 2000 who had 102 receptions and 1,135 yards in his 98 career games. He had the third-lowest yards-per-reception average of all players with over 100 receptions in the sample.

Pro Bowls

The graph below shows the probability of a player selected in each round making one or more, two or more, or three or more Pro Bowls in his career.

There's a steep drop after the first round, and after that the data is wildly inconsistent, especially the blue line, which shows one or more Pro Bowls.

But I prefer to look at the other two lines in this case. Because wide receivers are less consistent than other positions on a year-to-year basis, a player whose true talent level is 800 yards in a season can earn a Pro Bowl slot with one 1,200-yard season. Two or more Pro Bowls shows consistency and true success.

Tracking the number of career Pro Bowls by draft round, as shown below, results in a cleaner version of the graph above.

On average, first-round wide receivers are selected to just under one more Pro Bowl than second rounders in their career.

The difference between second rounders and sixth or seventh rounders is about three-tenths of a Pro Bowl, which means that out of 10 wide-outs drafted in both the second and seventh rounds, the higher drafted players will earn a combined three more Pro Bowl bids in their careers.

That isn't supported, however, by any of the other graphs below.

Receiving Stats

Receptions, yards, and touchdowns are all highly correlated with one another. I looked at the average number of each in a wide receiver's career, and I found that the resulting graphs all look suspiciously alike.

Below, I have shown just that, with yards and touchdowns adjusted to a receptions scale (yards are divided by 12 and touchdowns are multiplied by 14).

We see that the performance of a second rounder is about 35 to 40 percent less than a first rounder and a third's is 30 percent of a second rounder's. The difference between a fifth and seventh rounder is next to nothing.

There is more than a 99 percent correlation between draft round and every one of the stats above.

In other words, we are about 98 or 99 percent confident that we can predict the future career stats of the average of all first rounders in a certain draft, all the second rounders in that draft, etc.

Because of the differences in each player's career, we cannot predict their stats, but rather the player's baseline stats based on his draft round.

The table below shows the above stats, prorated to a 16-game season. Fantasy points are equal to yards divided by 10 plus touchdowns multiplied by six plus receptions.

If we graph these stats, the result is essentially the same trend as the above graph showing career stats.

Years Starting

The below graph shows the average number of years as a starter for each draft round.

As with the receiving stats, the number of years started by draft round shows a gradual decrease from the first to the second round, but the declines from each of the remaining rounds to the next is larger than the drops of the receiving stats.

What Does All of This Mean?

1. First rounders are elected to one more Pro Bowl in their careers than second rounders. Based on Pro Bowls alone, there's a steep slope after the first round and a gradual decrease in the remaining rounds.

2. By looking at receiving stats, we can show that the difference between the first two rounds isn't as big as previously thought. The graph of the average career numbers of each draft round shows an almost perfect function, which can be expressed in the following formulas:

Receptions:

draft round

Yards:

draft round

Touchdowns:

draft round

Fantasy points

draft round

3. In a season's worth of play, second rounders perform about 25 percent worse than first rounders. In their careers, they are about 35 to 40 percent worse, which suggests that second rounders play less games than first rounders but are better on a per-game basis than on an aggregate.

4. Essentially, we have backed up our prior hypothesis of the decrease from each round to the next, save for maybe the finding that the difference isn't as steep as thought.

How Draft Round Affects a Running Back's Career Performance

Zach Fein — Sat, 18 Apr 2009 09:19:54 -0400

You know the busts: Curtis Enis, Lawrence Phillips, Blair Thomas, Ki-Jana Carter. But can you name those sixth- and seventh-round steals, or even the guys picked in the first round who panned out as planned?

I've gone through quarterbacks and linemen so far. Next up in this series, I'll be looking at running backs. Do all those first-round busts mean that all first-round backs are off-limits?

(Note: For those running backs that were picked but did not play, I filled in their missing stats with those of Anthony Thompson, a second-round pick in 1990 with the fifth-worst yards-per-attempt average of all backs with more than 250 attempts; coincidentally, he had 251 career attempts.)

Pro Bowls

The graph below shows the percentage of players who made at least one, two, or three Pro Bowls in their career.

After the second round, the pickings are slim.

Notice the spikes at the fifth round. The number of players who made at least one Pro Bowl increased, but nobody made at least three. My thought is that in the later rounds, there's a chance for someone to come out of nowhere, surprise everybody, and then fade into oblivion.

By the way, one of those Pro Bowl selections was Bobby Joe Edmonds, who only made it as a punt returner.

The next graph shows the average Pro Bowl appearances by draft round. I'm including it to make a point.

After the first two rounds, there is absolutely NO difference between rounds three and four and rounds six and seven. The average backs in the third and fourth rounds make 0.25 and 0.20 Pro Bowls respectively, while the average backs in the sixth and seventh rounds make about one-tenth less than that—0.15 and 0.07, respectively.

Rushing and Receiving Stats

Don't believe me?

This first table shows the average stats of each round, prorated to 300 attempts.

See how the average stats of the fifth round are better than every other round? That may be because fifth-rounders perform the best when looking at yards and touchdowns per attempt.

But what really matters is how each round does each and every year—and that includes the number of attempts and receptions the average player gets. A seventh rounder will never get the same number of attempts as a first rounder, so the better way to look at the rushing and receiving stats is by per-year stats.

I included fantasy points, or total yards divided by 10-plus total touchdowns multiplied by six. If you graph draft round and fantasy points, the resulting line mirrors a more flattened exponential curve.

What that indicates is that the decline in production from each round to the next is lessened after every round—or in other words, after the first three rounds, all the remaining running backs are the same.

Years Starting

Still don't believe me? Check out the next graph, which shows the number of years the average running back starts from his team.

A near linear relationship from the first round to the third round (losing about one year starting after each round), and then a gradual decrease of half a year starting over three rounds. Do you really think it's worth it to take a running back in the fourth round, when you can get slightly lower production three rounds later?

(Sample size note: No round had less than 56 running backs drafted, and the average for each round was 61.)

What Does All of This Mean?

1. If you're a franchise wanting to draft a running back, you should aim to get one in the first three rounds, if not the first or second.

2. After the third round, every running back is the same. Seriously! Either draft a back in the first three rounds, or wait until rounds six and seven to draft your running back.

3. Teams looking to draft players like Mike Goodson or Jeremiah Johnson in the middle rounds should just wait to draft Ian Johnson or P.J. Hill in the last round.

How Draft Round Affects an Offensive and Defensive Lineman's Career Performance

Zach Fein — Fri, 17 Apr 2009 23:14:11 -0400

Yesterday, I examined how quarterbacks perform based on the round in which they were drafted. Today, I'll be looking at offensive and defensive linemen, to see if first rounders have a better, longer career than third or fourth-round linemen.

With Jason Smith, Eugene Monroe, Andre Smith, Aaron Maybin, and Tyson Jackson all projected to go in the top half of the first round, I chose to review linemen before running backs. Plus, there's not much to look at for either offensive or defensive linemen, so I fit them both into one article.

Offensive Linemen

Pro Bowls

The first graph below shows the chances of an offensive lineman making one, two, or three Pro Bowls in his career, by the round in which he was drafted.

After the first round, there's not much talent to be found. This is supported by the next graph, which shows the average number of career Pro Bowls for each draft round.

There's one notice, however: Pro Bowls for offensive linemen are usually based on reputation only. Since there aren't any popular stats for these players, Pro Bowl votes go to those who are thought to be the best. For example, Orlando Pace and Walter Jones were elected to 16 Pro Bowls between them, which is most likely double of what they actually were worth, based on their performance.

Also, only 10 players were elected as a first-team All-Pro, with four of them—Alan Faneca, Steven Hutchinson, Jones, and Pace—combining to have 17 of the 23 All-Pro votes in the sample. Twenty of the 23 votes were by first rounders, too.

Years Starting

How long can you expect your newly-drafted offensive lineman to be a starter for your favorite team? The following graph illustrates this.

Somewhat surprisingly, a seventh rounder starts more than a year-and-a-quarter in the league, although this may be due to the three or four spots on the defensive line available for a starter. As with the two previous graphs, the line drops steadily with a slight increase in the latter rounds. But there's no real trend regarding which round (five, six, or seven) yields the better offensive lineman.

(Sample size note: Every round had at least 34 offensive linemen drafted with an average of 43.)

Defensive Linemen

Pro Bowls

Not to reuse the same information over and over...but the next graph shows the chances of a defensive lineman making the Pro Bowl based on draft round.

There's another downward trend, but a spike in the fifth round. That said, it shouldn't be too big of a notice.

Only eight percent of fifth-round defensive linemen are selected to at least one Pro Bowl—if the average number from 1985-2004 are selected in that round this year (4.35), that means it would take about three years for one fifth-round defensive lineman to be elected to one Pro Bowl (0.35 would be elected in one year, or about one after three years). On average, every sixth fifth rounder makes a Pro Bowl; one makes 0.16 Pro Bowls in his career.

Sacks and Years Starting

Though sacks may not be the best tool for measuring defensive linemen productivity, tackles weren't included in the data set I was working with. The following graph shows the average number of career sacks by draft round.

There's a clear correlation between sacks and draft round. Even if I look at it per year, the trend still stands, except for another increase in the fifth round.

Finally, the graph below shows the number of years a defensive lineman is expected to be a primary starter, by the round in which he was selected.

I'm growing tired of saying the same thing: It's safe to say that the lower in the draft a lineman is selected, the worse he should perform in his career.

(Sample size note: All rounds had at least 87 defensive linemen drafted, with an average of 104.

What Does All of This Mean?

1. For both offensive and defensive linemen, the round in which a player is drafted significantly correlates with his career performance.

2. Players drafted in the first round (the drop from the first to the second round in each graph is quite large, in fact), on average, are elected to more Pro Bowls, have more sacks, and start more years for their teams than do those drafted later on.

3. Fifth-round defensive linemen perform better than fourth-rounders, and slightly worse than third-rounders, with Pro Bowls and sacks in mind. But the difference between them isn't significant at all.

4. Teams should not be scared to draft Andre Smith (or Jason Smith, or Eugene Monroe) high in the first round. He'll do much better than what they can get in the third and fourth rounds.

5. I guess Andy Reid is correct in drafting linemen high every year. From 2003 to 2006, the Eagles drafted a lineman with their first-round pick, and in the last two years—in both of which they had no first-rounders —they selected one with one of their two second-round picks.

How Draft Round Affects a Quarterback's Career Performance

Zach Fein — Thu, 16 Apr 2009 23:05:05 -0400

In one week, the Detroit Lions will likely draft quarterback Matthew Stafford as the No. 1 overall pick.

USC quarterback Mark Sanchez and Kansas State's Josh Freeman are both candidates to be drafted in the top 15 picks.

It's no sure thing, however, that any of them will pan out as analysts believe. For every Peyton Manning, there's a Tim Couch or Ryan Leaf, a highly-touted prospect who is called a bust in three years.

In this series, I'll be looking at the 20 drafts from 1985 to 2004 to see how a player's draft round correlates (if at all) to their expected career performance. In the first installment, I'll be examining quarterbacks—is a quarterback selected in the first round any better than one selected two or three rounds later?

All data comes from Pro-Football-Reference.com. For players that never played a game but were still drafted, I did not just throw them out of the sample; instead, I took the stats of Mike McMahon—who had a career passer rating of 55 (below replacement level) with just over 500 pass attempts—for their hypothetical career lines.

Pro Bowls

The graph below shows the likelihood of a quarterback being selected to one or more, two or more, or three or more Pro Bowls in his career. In the first instance, there is almost a perfect linear relationship after the first four rounds.

There were only seven quarterbacks drafted after the first round who made three or more Pro Bowls; 167 were drafted. As you can tell, there weren't enough QBs to make three Pro Bowls to be statistically significant.

Quarterback Rating

The graph below shows the average passer rating (weighted by attempts, of course) of a quarterback selected in each round. Notice that there's a steady decrease after almost every round.

The rating for the last round was 61.8, so I didn't show it in order to allow a better close-up of the first six rounds. The graph shows the difference between first and second round quarterbacks is minimal, but after that the production is much worse.

The next (and my favorite) graph shows the probability of a quarterback having either a career passer rating above 80 or below 75; use this as a bust-or-not detector. Only quarterbacks with more than 500 career attempts were included, but the percentages were found using the total number of QBs in the denominator (as opposed to just those with 500 attempts).

The blue line, which shows players with a rating over 80, is almost identical to the graph above (save for the different values in the second round). The red line, which essentially shows the percentage of busts picked, has no pattern whatsoever—in fact, quarterbacks picked in the first and second rounds have the greatest chance of having a passer rating under 75, while those in the fourth round have never done so (with a significant 34 QBs picked in the fourth round).

Pay attention to whether the blue line is above the red; if so, that means there's a better chance of a quarterback having a solid career than that same QB having a sub-par career. The first, fourth, and sixth rounds—in that order—had the biggest positive difference; all other rounds either had the same number or a one player difference of good and bad picks.

Other Stats

How does the average quarterback fare in a full season's worth of play? The following table shows the stats (per 550 attempts) of the average QB in each round, as well as the overall average quarterback.

Yardage and completion wise, there's an obvious trend—the stat in question drops steadily through the first five rounds, increases in the sixth, then drops off in the seventh. As for touchdowns and interceptions, there's nothing to take away, as both are wildly inconsistent.

The final graph below shows the years as the primary starter (as denoted by P-F-R) for a quarterback drafted in each round.

This graph backs up what all the others have before: There's a near-continuous downward trend from the first through the fourth rounds, before a small increase to the fifth and a larger one to the sixth, and then a big drop to the seventh. If you go back to the very first graph, the percentage of quarterbacks with one or more Pro Bowl appearances looks virtually the same as this graph.

What Does All of This Mean?

1. Despite the high number of infamous busts, quarterbacks selected in the first round have the best chance of having a passer rating over 80 and the biggest difference in successful/non-successful picks.

2. Second-rounders have the second-highest passer rating on average, but they also have the most risk, with more quarterbacks with a passer rating less than 75 than quarterbacks with a passer rating over 80.

3. Sixth-round quarterbacks perform almost as well as third-rounders in their career. They have a better chance of making the Pro Bowl, less risk, and an equal chance of panning out than third-rounders. The QBs selected in the sixth round, though, had a lower average passer rating by one point and just over one-third of a year less as the starter than third-rounders.

NL West Projected Standings: Will Manny Lead the Dodgers to the Playoffs Again?

Zach Fein — Sun, 05 Apr 2009 19:49:10 -0400

Note: All team projections were found using the FEINs projections. An explanation behind projected wins, losses, and other stats can be found here.

1. Arizona Diamondbacks

After April, it looked like a foregone conclusion that Arizona would represent the NL in the World Series. After all, the Diamondbacks were 20-8 and three-and-a-half games over their closest competitor.

But the D-Backs lost seven-and-a-half games in the standings after a 12-17 May and were four games behind the Cubs.

Arizona then went 10-17 in June to fall to .500, and by then they were eight games back of the Cubs. From the beginning of May to the All-Star break, the D-Backs went 27-40 and lost 13.5 games in the NL standings.

Brandon Webb was superb in March and April, posting a 1.98 ERA and a 1.05 WHIP in 41 innings, allowing just one home run in those two months.

He had a .255 BABIP in those 41 innings, suggesting he was due for a downturn. He was—in his remaining 186 innings, Webb had a 3.59 ERA and a 1.23 WHIP, with a more common .306 BABIP.

Webb's forecast calls for slightly worse numbers than his 2008 stats. FEIN projects a 3.35 ERA, a 1.23 WHIP, and 164 strikeouts in 205 innings, which makes him the No. 8 pitcher and No. 36 overall, a late third-rounder in standard leagues.

Fellow starter Dan Haren once again faded down the stretch last year. In 2008, Haren had a 2.62 ERA and a 0.96 WHIP in 148 pre-August innings; In August and September, Haren had a 4.87 ERA and a 1.49 WHIP.

This pattern isn't all that surprising—in the last three years, Haren's pre-August numbers (2.93 ERA and 1.06 WHIP) dwarf his post-August numbers (4.80 ERA and 1.46 WHIP).

Haren is projected to have 186 strikeouts, a 3.46 ERA, and a 1.16 WHIP in 197 innings, which makes him the No. 4 pitcher.

But considering that he'll most likely give you a 2.90 ERA in the first four months and an ERA almost two runs higher in the final two, Haren owners should be ready to sell high after the All-Star break.

Fifth starter Max Scherzer will be the Diamondbacks' X-factor this year. Scherzer had a 3.05 ERA in 56 innings (starting six games and appearing in nine others) in 2008, and he had a 10.6-strikeouts-per-nine rate with 66 strikeouts.

That latter number is no fluke, as he's had a K/9 rate of 11.8 in two years in the minors. Scherzer's projected to have 77 strikeouts and a 3.71 ERA in 74 innings, but if he does put up those rates, he'll certainly pitch more innings than that.

FEIN projects a major breakout season for 21-year-old Justin Upton. Upton set career highs last year with a .250 AVG and a .816 OPS, but he struck out 121 times in 417 PA.

He's projected to have a .287 AVG and a .872 OPS in 317 PA, and in a full season with 600 PA, his projected stat line is 79 runs, 21 homers, 66 RBI, and 142 strikeouts.

The Diamondbacks' nine best pitchers in April had a 16-3 record with a 2.40 ERA and a 1.02 WHIP in 169 innings. In every other month of the season, however, those same pitchers had a 40-41 record with a 4.05 ERA and a 1.29 WHIP.

Much of Arizona's late-season demise was due to their lack of pitching in August and September. With their great, young core of hitters, they have a serious shot of winning the division if they can just stay consistent the whole year.

2. Los Angeles Dodgers

In any other division, 84 wins is no big deal. In the other five divisions last year, 84 wins would have equaled an average of 11 games back of the division leader. In addition, it would have placed you fourth in the division. But in the NL West, 84 wins puts you on the top of the standings.

The Dodgers were 54-54 when they acquired Manny Ramirez. They went 11-16 in their next 27 games and then 19-8 to close out the season. Ramirez had a .396 AVG, an insane .743 SLG, and a 1.232 OPS in his 53 games with the Dodgers; he had 17 homers and 53 RBI, which prorate to 45 homers and 139 RBI in 600 PA.

Ramirez is projected to decline significantly next year, with a forecasted .287 AVG and .925 OPS. Still, 81 runs, 31 homers, and 95 RBI would make him the No. 13 OF and No. 43 overall.

Matt Kemp had 18 homers, 93 runs, 76 RBI, and 35 steals with a .290 AVG—even though his BABIP was .363—in 2008, his first full season in the majors. FEIN's projection is low, but that's because he's projected to have just 456 PA.

In the 657 PA he had last year, Kemp's projected line is a .292 AVG, 20 homers, 97 runs, 82 RBI, and 32 steals, possibly making him a top-three outfielder in fantasy.

(As a comparison, the top outfielder, Grady Sizemore, is projected to have a .284 AVG, 26 homers, 112 runs, 80 RBI, and 29 steals in 723 PA; Sizemore's projected stats are worse than Kemp's if Sizemore had 657 PA—he would have six points less in AVG, two less homers, four more runs, seven less RBI, and three less steals.)

Catcher Russell Martin has had over 18 steals each of the past two years, and his total of 39 is 27 more than any catcher in that time. In fact, he's the only catcher with more than 12 steals in the last two years. Martin's also averaged 87 runs, 16 homers, and 78 RBI in that time.

Martin's projected to have a .281 AVG, 81 runs, 14 homers, 72 RBI, and 17 steals in 591 PA next year. He's ranked No. 1 overall among catchers.

Staff ace Chad Billingsley had a 3.14 ERA and 201 strikeouts in just under 201 innings in '08, after a 3.31 ERA and 141 strikeouts in 147 innings in '07. FEIN projects a 3.46 ERA, a career-low 1.31 WHIP, and 178 strikeouts in 186 innings. He's ranked No. 19 among starters and No. 78 overall.

In his first season in the majors after 11 in Japan, Hiroki Kuroda made a nice debut, posting a 3.73 ERA in 183 innings. But Kuroda was wildly inconsistent—he allowed one earned run or less in 12 starts (pitching six innings or more in all but one), but allowed four or more runs eight times.

That means he had an ERA less than two or more than six 21 times in 31 starts (he had one start in which he allowed three earned runs in four innings).

In three June starts, he gave up six earned runs in less than three innings twice...and had a complete game, 11-strikeout shutout in the other.

Kuroda's projected to have a 4.00 ERA and a 1.34 WHIP with 99 strikeouts in 145 innings in 2009, which is by no means spectacular, but it's not terrible either.

The Dodgers are forecasted to be two games out of the Wild Card at season's end. But with their powerful lineup, nasty bullpen (their top three relievers are projected to have a 3.32 ERA in 209 innings), and decent rotation, Los Angeles could be the one team no one wants to face in October.

That is, if they make it that far.

3. Colorado Rockies

A year removed from their first World Series appearance and second playoff appearance in franchise history, the Rockies went 74-88 and allowed almost a half a run more than they scored per game.

With Matt Holliday looking to become a free agent next winter, the Rockies looked to trade him in order to start a rebuilding process; in return, the Rockies got Huston Street, Carlos Gonzalez, and Greg Smith.

Street will take over for Brian Fuentes in the closer role, as Fuentes was signed by the Angels this offseason. Street spent his first four years in Oakland, compiling 94 saves with a 2.88 ERA and 1.07 WHIP.

Street's transition from the AL to the NL is somewhat diminished by the fact that he's moving into Coors Field, but it's nevertheless a positive switch.

Street is projected to have a 3.15 ERA and a 1.16 ERA, as well as 79 strikeouts and 34 saves, in 70 innings. He's ranked No. 8 among relievers (though No. 7 if you exclude Carlos Marmol, who was projected to have 34 saves before he lost the closer role), and he's currently being drafted as the No. 24 reliever in ESPN leagues and No. 18 in Yahoo! leagues.

No. 1 starter Ubaldo Jimenez set a career low last year with a 3.99 ERA, and after the All-Star break, he had a 8-3 record with a 3.68 ERA in 86 innings, including a 3.40 ERA after May. FEIN projects a 3.89 ERA and 1.38 WHIP in 173 innings, which are very good numbers for his average draft position (No. 228 in ESPN leagues).

Third baseman Garrett Atkins had a down year last year, with a .286 AVG, a .780 OPS, 21 homers, and 99 RBI, all his lowest since 2005. But as Brian Joura points out, Atkins had a .307 AVG and a .823 OPS while playing third base and significantly worse numbers while playing first.

Atkins is projected to have a .299 AVG, a .838 OPS, 21 homers, 99 RBI, and 84 runs. He's ranked No. 4 among third basemen and No. 35 overall, but he's being drafted as the No. 8 third basemen in ESPN and Yahoo! leagues.

It should be noted, however, that at 29 and with just a one-year contract, Atkins may be on the move if the Rockies continue their rebuilding process.

Any move hurts him, of course, as Coors may be the best hitters park in the majors, and especially if he moves to the AL.

Todd Helton had over 620 PA in every year since 1999 before an injury limited him to just 83 games and 361 PA last year, and the aforementioned Atkins had to fill in for him at first base.

Last year was also the first year Helton had an AVG under .300 since 1998 and the first time in his career he had an OPS under .800.

FEIN is projecting a .290 AVG and a .856 OPS, accompanied by 12 homers, 63 runs, and 60 RBI in 504 PA. He's nothing to get excited about next year.

The Rockies' outfield is lead by Brad Hawpe, who had 25 homers and a .879 OPS last year, after 29 and .926 respectively in 2007.

Hawpe is being drafted as the No. 37 outfielder in ESPN leagues and No. 41 in Yahoo! leagues, but FEIN ranks him No. 26 with a projected line of 22 homers, 65 runs, 83 RBI, a .282 AVG, and a .869 OPS in 535 PA.

Whether or not this team finishes strong in 2009 makes no difference for the Rockies—they may even concede 2009 as another rebuilding year and try to trade Atkins, Hawpe, and Helton.

Colorado has secured its young stars—Street, Jimenez, Greg Smith (projected 4.08 ERA and 1.37 ERA), Troy Tulowitzki (.275 AVG and .772 OPS), Ian Stewart (.268 and .790), and Jeff Baker (.262 and .773)—for the future, and though 2009's outlook make be bleak, the Rockies will be back to the top of the division in three years or less.

4. San Francisco Giants

The Giants scored under four runs per game last year for the first time since 1992. Not coincidentally, 1992 was the last time they had been without Barry Bonds, whom they let go after the 2007 season.

Without Bonds, San Francisco's batting struggled mightily. In 2008, the Giants had no regular starter with an OPS over .800 and no one with more than 16 homers (in fact, they had the fewest team home runs since the 1995 Phillies). They had just one player with more than 70 RBI and only two with more than 60 runs.

San Francisco has no player that is projected to have a .800 OPS this year, either. Randy Winn's .754 OPS is the highest among starters on the team.

In his three full years with the Giants, Winn's averaged 12 homers, 80 runs, 17 steals, and a .290 AVG.

Winn's projected to have 13 homers, 77 runs, 16 steals, and a .280 AVG, which ranks him No. 29 among outfielders. He's being drafted outside the top 65 in ESPN leagues.

Meanwhile, the Giants found a gem in Tim Lincecum last year. The 24-year-old had a 2.62 ERA, 1.17 WHIP, and 265 strikeouts in 227 innings, earning him the Cy Young award. Lincecum's performance was worth just under $34 million, according to Fangraphs.

Lincecum should have another spectacular year in 2009. He's projected to have a 3.08 ERA, 1.21 WHIP, and 225 strikeouts in 203 innings (just under 10 per nine innings). That 3.08 ERA is second-lowest among starters, behind Rich Harden's 3.00 ERA.

He's ranked No. 3 among pitchers, mostly because he's projected to pitch less innings than the two ahead of him (Johan Santana and CC Sabathia).

Perhaps No. 2 starter Matt Cain is undervalued in fantasy leagues only because of his 15-30 record the last two years, but he shouldn't be.

Cain has had just 3.17 runs of support in his 66 starts, even though the Giants have scored 4.32 runs per game in every other game in those two years.

Cain pitched 200 innings each of the past two years, posting ERAs of 3.65 and 3.76 with over 160 strikeouts in both years. FEIN projects a 3.67 ERA, a 1.34 WHIP, 178 strikeouts, and a 13-11 record in 205 innings, which makes him the No. 29 starter.

After two more years in Arizona, Randy Johnson signed a one-year deal with the Giants in an attempt to reach 300 wins (he's at 295). FEIN thinks he'll reach that and have his lowest ERA since 2004.

Johnson is projected to have a 3.72 ERA, a 1.16 WHIP, 156 strikeouts, and a 9-8 record in 154 innings in 2009, which would arguably make him one of the best No. 3 starters in the league.

Johnson ranks him No. 11 among starters and No. 50 overall; he's being drafted outside the top 40 starters in ESPN leagues. Snag him in the 13th round with confidence.

The Giants are forecasted to score 4.36 runs per game, fifth-least in the majors. With their superb pitching—projected to be the best in the league—however, 4.60 runs per game would be all that is needed to win 87 games and the division title. Just one more bat could jump them from fourth in the division to the top of the NL West.

5. San Diego Padres

Though the biggest change from 2007 to 2008 was the loss of Marcus Giles, the Padres went from 89 wins and tied for the Wild Card to 63 wins and 21 games behind the Dodgers. The Padres were remarkably 31-39 and just six-and-a-half games out in mid-June before finishing the year 32-60.

It's certainly never good when your offense is the worst since 2004, scoring just 3.93 runs per game. Or when your second baseman and shortstop combine to hit .221, with a .274 OBP, a .324 SLG, and a .597 OPS in 753 PA. Or when your catchers have a .203/.268/.296 line in 633 PA.

Regardless, the Padres still had a nice rotation, with three starters that had a sub-4.00 ERA: Jake Peavy, Chris Young, and the now-retired Greg Maddux.

Peavy has had an ERA below three in four of the last five years, posting a 2.85 ERA in 174 innings last year. FEIN isn't a fan of his high line drive rates (an average of 18.9 percent the last three years and over 21 percent last year), though, and predicts that his BABIPs of .285 and .286 the last two years will rise to .303 next year.

Accordingly, FEIN thinks his ERA will rise to 3.45 next year, with a 1.21 WHIP and 172 strikeouts in as many innings. Peavy is ranked 10th among starters and No. 48 overall.

Chris Young had a 3.96 ERA last year, his lowest as a Padre, and he only pitched 102 innings after being hit in the face by a line drive in late May. In 2007, Young had a 3.13 ERA and 1.10 WHIP with 167 strikeouts in 173 innings.

But Young's had suspiciously-low BABIPs after moving to San Diego (.237, .255, and .266), and he's projected to have a .306 BABIP next year, as well as a 3.91 ERA, a 1.34 WHIP, and 112 strikeouts in 122 innings.

San Diego's bullpen had the sixth-highest ERA last year (4.45), but FEIN forecasts a 3.74 ERA and 1.30 WHIP for the Padres' relievers.

Heath Bell, now their closer with Trevor Hoffman gone, had a 3.58 ERA in 78 innings last year. In 2007, Bell pitched 94 innings in long relief and had a 2.02 ERA and a 0.96 WHIP with 102 strikeouts. FEIN projects a 3.53 ERA and 1.25 WHIP, with 24 saves and 74 strikeouts, in 79 innings. He's ranked No. 16 among relievers.

Three more relievers are projected to have a sub-3.80 ERA for San Diego. Mike Adams—who had a 2.48 ERA in 65 innings in 2008—is projected to pitch 60 innings with a 3.22 ERA and 1.19 WHIP; Justin Hampson—who had a 2.93 ERA in 31 innings last year and a 2.70 ERA in 53 innings in 2007—is projected to have a 3.64 ERA in 45 innings; and Cla Meredith—who's had a 3.09 ERA in 201 innings the last three years—is projected to have a 3.78 ERA in 74 innings.

The Padres' best hitter is first baseman Adrian Gonzalez, who was voted to his first All-Star game last year after hitting 36 homers, 103 runs, 119 RBI, and a .879 OPS. Gonzalez also had 30 homers and over 100 runs and RBI the year before.

FEIN projects 25 homers, 79 runs, 85 RBI, and a .282 AVG in 601 PA next year, which prorates to 29, 92, and 99, respectively, in 700 PA. Gonzalez ranks No. 9 among first basemen and No. 93 overall.

The Padres definitely aren't as bad as their 63-99 record last year. Until they can get their offense back to the caliber it was two or three years ago, however, don't expect 80 wins out of San Diego any time soon.

NL East Projected Standings: Will Atlanta Spoil the Phillies' Title Defense?

Zach Fein — Sun, 29 Mar 2009 01:25:29 -0400

Note: All team projections were found using the FEINs projections. An explanation behind projected wins, losses, and other stats can be found here.

1. Atlanta Braves

Atlanta allowed 4.86 runs per game last year. But the Braves let Jo-Jo Reyes, Charlie Morton, Tim Glavine, Chuck James, and Blaine Boyer pitch a combined 353 innings, and in those, they allowed 254 runs, or a 6.48 runs-per-nine average.

If you take out their stats from Atlanta's team totals, the Braves' remaining pitchers allowed just 4.33 runs per nine and had an ERA of 3.93.

The Braves made two of the most underrated moves in the offseason, signing pitchers Javier Vazquez and Derek Lowe. Those two have a combined projected runs-per-nine average of 3.98 in 375 innings—60 percent of the runs allowed average of the above five pitchers.

If you fill in their projected stats into the Braves' remaining totals, Atlanta's runs allowed average is lowered from 4.33 to 4.24 and their ERA from 3.93 to 3.86.

Vazquez has always been an enigma. In his three years with the Chicago White Sox, his ERA based on his components (which is the formula I use to predict ERA after I project the player's hits allowed, home runs, etc.) was 4.02—and his actual was 4.40 ERA.

Now, remember that Vazquez is also moving from the AL to the NL and from a slight hitters' park to a neutral park in Atlanta. In Atlanta's park, Vazquez's 4.40 ERA in Chicago is lowered to 3.68.

If I do the same for his adjusted ERA, the 4.04 lowers to 3.58. His strikeouts-per-nine is 9.30 in Atlanta, compared to 8.56 in Chicago, and his WHIP is 1.19 in Atlanta, compared to 1.25 in Chicago.

You can see why Vazquez's projected 3.65 ERA is not crazy at all. Nor is his projected 200 strikeouts and 9.56 strikeouts-per-nine rate; nor is his projected 1.17 WHIP or his projected 0.91 homers-per-nine rate, which would be the lowest of his career.

Those stats rank him No. 6 among pitchers—currently, he's being drafted as the No. 35 starting pitcher in ESPN.com leagues and as the No. 28 starter in Yahoo! leagues. He's by far the biggest bargain of your draft.

As for Derek Lowe, he's had a 3.59 ERA and a 1.23 WHIP in his four years in Los Angeles, posting a 3.24 ERA and a 1.13 WHIP in 211 innings last year. Moving from the Dodgers to Atlanta won't affect his stats, though; the biggest difference is that Turner Field allows slightly more home runs than Dodger Stadium.

Lowe is projected to have a 3.69 ERA and 1.23 WHIP in 187 innings. He's ranked 14th among starters by FEIN; he's going around the same spot as Vazquez in drafts on ESPN.com and Yahoo!.

Along with Vazquez and Lowe, the Braves have two more pitchers projected to have a sub-4.00 ERA: Tim Hudson and Jair Jurrjens. Hudson has had an ERA under 3.55 in every year since 2001 except for a 4.86 ERA in 2006.

FEIN projects a 3.78 ERA and a 1.29 WHIP in 151 innings—of course, however, Hudson won't be back from Tommy John surgery until mid-August.

Jurrjens, in his rookie season, had a 3.68 ERA and finished third in the NL Rookie of the Year voting. Jurrjens' projected ERA is 3.99, and he makes the Braves one of only two teams (along with the Red Sox) to have four starters with a sub-4.00 ERA.

In the lineup, third baseman Chipper Jones flirted with a .400 batting average last year and ended the year at .364 with a 1.044 OPS, his highest since 1999.

Despite that, he finished as the No. 6 third baseman last year in Yahoo! leagues. He's projected to have a .313 AVG, a .941 OPS, 21 homers, and under 80 RBI and runs to finish as the No. 4 third baseman.

Catcher Brian McCann hit .301 with 23 home runs and 87 RBI last year, numbers you'd expect from your first baseman. His projected .298 AVG, 19 homers, and 78 RBI make him the No. 3 catcher, behind Russell Martin and Joe Mauer, and worth the No. 40 overall pick.

The Phillies are the reigning World Series champs, and the Mets have been in contention for the NL East title the past two years—but Atlanta is scary good. The Braves are returning nearly the exact same offense, plus Garrett Anderson.

They lost John Smoltz, but the signings of Javier Vazquez and Derek Lowe should escalate the Braves to the Wild Card, if not the NL East crown.

2. Philadelphia Phillies

Philadelphia won the World Series last year, and they arguably may have improved during the offseason, essentially trading Pat Burrell for Raul Ibanez. They won 92 games last year and outscored their opponents by almost three-fourths per game.

They had four hitters with an OPS over .860 (minimum 300 PA), had the second-lowest bullpen ERA (3.22), had one pitcher with a 3.09 ERA, and another with a 3.71 ERA.

But in the last 11 years, the winner of the World Series has lowered its win total from their championship year to the next by an average of nine, and nine of those 11 teams saw their wins decrease.

This may be natural regression to the mean, but in that same span, the loser of the World Series has only had 5.6 wins taken off their record the next year, with only seven of the 11 teams seeing a decline in their wins (three improved, and one stayed the same).

Second baseman Chase Utley has been one of the best players in the past three or four years. Since 2006, Utley has been the second-best in the field—at 46 runs above the average second baseman, behind only Pedro Feliz—and has been worth over $94 million, second-highest behind only Albert Pujols.

In fact, he's earned over $32 million from his production each of the past two years.

Like the Phillies, however, Utley will see a decrease, according to FEIN. Utley had 33 home runs, 113 runs, and 14 steals in 2008, and he's had over 100 RBI and a .900 OPS in every year since 2005. Utley is projected to have 28 homers, 106 runs, 99 RBI, 13 steals, and a .897 OPS next year, which makes him worth the No. 5 overall pick.

Philadelphia will get the majority of their homers and RBI by Ryan Howard, who's had over 47 homers and 135 RBI every year since 2006; but his AVG and OPS have both fell every year, from a .313 AVG and 1.048 OPS in 2006 to a .251 AVG and .881 OPS last year.

FEIN projects 43 homers, 124 RBI, a .944 OPS, and a .272 AVG in 2009, and though his counting stats seem down, his low PA (projected for 625) may account for the lowliness—in 700 PA, he's projected to have 48 homers, 139 RBI, just under 100 runs, and 198 strikeouts.

He's ranked No. 12 overall and third among first baseman, behind Albert Pujols and Miguel Cabrera.

Shortstop Jimmy Rollins is the third Phillies hitter ranked in the top 10 among hitters. Rollins is ranked No. 10 overall (No. 9 among hitters, one spot ahead of Howard) and the third shortstop, behind Hanley Ramirez and Jose Reyes. In 2007, Rollins had a career year, setting new highs in home runs (30), runs (139), RBI (94), and AVG (.296).

Last year, however, Rollins failed to reach 700 PA for the first time in four years; still, he set a career high in steals with 47—59 steals if he had the number of PAs he had in 2007—but only hitting 11 homers—14 if he had 778 PA. Rollins is projected to have 18 homers, 104 runs, 36 steals, and a .285 AVG in 2009.

World Series MVP Cole Hamels is coming off a year in which he posted a 3.09 ERA and a 1.08 WHIP—career lows—and finished as the No. 7 pitcher in Yahoo! leagues. FEIN thinks Hamels will decline significantly, projecting a 3.60 ERA and a 1.19 WHIP with 190 strikeouts in 205 innings.

Those numbers, nonetheless, make him the No. 5 pitcher and No. 19 overall; he's being drafted as the No. 4 pitcher in Yahoo! leagues and No. 6 in ESPN.com leagues.

Philadelphia will regress from their 92 wins last year, but the defending World Series champions will be in the hunt to win the NL East title all year long. They are forecasted to win just one-tenth of a game less than the Braves—that's how tight the division race will be next year.

3. New York Mets

For the second straight year, the Mets were near the top of the NL East race late in the year...and proceeded to fade in September. New York was 40-42 after June, but they went 36-19 in the next two months to take a one-game lead in the division over the Phillies.

At one point, they were 86-67 (after a 10-6 September) and half a game ahead of Philadelphia—but they managed to go 3-6 in their last nine as the Phillies went 6-2, and they lost the division in the final weeks for the second year in a row.

Unfortunately, they won't be in the division race this year—or, fortunately, they won't be in the division race this year to blow a lead.

Across the board, the hitting projections for the Mets are somewhat low, but there's an explanation for that. In the FEIN projections, there's a step for adjusting to the player's park.

Shea Stadium had a factor of around .97 or .98 for every stat I adjusted, meaning that from a neutral park to Shea Stadium, you would multiply a player's stats by .97 or .98 to appropriately for the park factor, or in other words, Shea Stadium was a pitcher's park.

The Mets are moving into Citi Field this year, and though we don't know how the park will play out until the season starts, early predictions say that Citi Field will allow about 10 less runs over 81 home games than Shea Stadium.

That difference would lower the Mets' park factor from around .97 or .98 to .95 or .96; I chose a park factor of .95 for every stat.

David Wright has had a .300 AVG, a .900 OPS, 26 home runs, and 100 RBI in each of the last four years. FEIN projects 102 runs, 27 homers, 106 RBI, 21 steals, and a .297 AVG in 2009. Despite the low numbers, Wright ranks No. 4 overall and tops among third basemen.

Shortstop Jose Reyes has 37 more steals than anyone else in the past three years; he's averaged 66 steals per year in those three. He's also had over 110 runs in each of those three years, with an average of 118. He's projected to have 109 runs, 14 homers, and 61 steals next year. He ranks one spot ahead of Wright at No. 3 overall.

The Mets acquired Carlos Beltran four years ago, and the 31-year-old's numbers have been declining since his 41-homer season in 2006.

He's seen a drop in his home runs and RBI every year (per plate appearance), as well as in his SLG and OPS. Strangely, however, his average and number of steal attempts (both raw and per PA) have improved each year.

What does FEIN see? Beltran is projected to have a .267 AVG, a .834 OPS, 28 home runs, 101 RBI, and 20 steals—only he and teammate David Wright are projected to have at least 20 homers, 20 steals, and 100 RBI. FEIN ranks Beltran at No. 15 overall and No. 3 among outfielders.

As well as their core of Reyes, Wright, and Beltran, the Mets have the best pitcher in baseball, Johan Santana. Santana's had a sub-3.00 ERA in four of the last five years, including a 2.53 ERA in his first season in New York.

Even after switching to the NL, Santana posted his first season with a less than a strikeout per inning since 2001, though he still had over 200. FEIN projects a 3.26 ERA, a 1.16 WHIP, 15 wins, and 212 strikeouts in 207 innings, which makes him the No. 1 pitcher and No. 8 overall.

With Billy Wagner out for the 2009 season with elbow surgery, the Mets elected to sign free agent reliever Francisco Rodriguez to take over the closer position. Rodriguez had 62 saves and a 2.24 ERA last year, and he's had a a strikeouts-per-nine rate of 10 or more in every season since 2004.

He's projected to have 37 saves (third-highest in the majors), with a 2.83 ERA, 1.21 WHIP, and 89 strikeouts in 69 innings. He's ranked No. 5 among relievers and No. 18 among all pitchers.

Though the Mets are forecasted to win just 83 games, there's no doubt that Mets fans would rather be three or four games out of the division crown heading into September and end six games out than be tied at the top or leading the division and blowing it in the final week.

Fool me once, shame on you. Fool me twice, shame on me. Fool me three times...Well, there won't be a third time for the Mets.

4. Washington Nationals

The Nationals had the worst record in the majors last year. They scored less than four runs per game, the first time a team had done that since 2005—when the Nationals scored 639 runs.

But with the acquisitions of Adam Dunn and Josh Willingham and the return of Nick Johnson (he had only 147 PA in 2008), Washington has a decent lineup that is projected to score 4.83 runs per game.

The problem, however, is that the Nationals have no pitching whatsoever. Although eight teams are projected to allow more runs than them, Washington's top three starters—John Lannan, Scott Olsen, and Daniel Cabrera—are projected to have a combined 4.62 ERA and 1.42 WHIP in 520 innings.

Their closer, Joel Hanrahan, had a 3.95 ERA and 93 strikeouts in 84 innings in his first season as a reliever in 2008. FEIN projects a 4.21 ERA and 76 strikeouts in 77 innings; the 4.21 ERA is the third-worst among closers, behind Brandon Lyon and Mark Lowe.

Adam Dunn has had exactly 40 home runs in the each of past four years, but FEIN projects just 33, due to regression to the mean. Dunn had a .236 AVG last year, and he's projected to improve that to .254. His projected 94 RBI and 86 runs make him the No. 16 outfielder.

Third baseman Ryan Zimmerman had a .283 AVG and a .774 OPS in 466 PA last year, and his 51 runs and RBI would equate to just 79 in 722 PA, his 2007 total; he had over 90 runs and RBI in '07. In 487 PA, Zimmerman is projected to have 16 home runs, 63 runs, and 66 RBI, with a .281 AVG and a .796 OPS.

In his last full season of play in 2006, Nick Johnson had 23 homers, 100 runs, 77 RBI, 10 steals, a .290 AVG, and a .948 OPS. He missed 2007 with a broken leg, and in 2008 only had 147 PA, and his production was down all around, with a .220 AVG and .846 OPS.

Johnson is projected to have only 343 PA, and in 600 PA, he'd be projected to have 21 home runs, 80 runs and RBI, five steals, a .285 AVG, and a .909 OPS. He's worth a flier in the late rounds of your draft.

Outfielder Elijah Dukes is no lock to stay healthy all year; the most games he's played in a season is 81. But, sometime, he's due for a breakout—he had 13 steals and home runs last year in 334 PA; in 600 PA, that's equal to 23 of each, plus 86 runs and 79 RBI.

FEIN projects just 290 PA, but here are his per-600 PA projections: .275 AVG, .873 OPS, 25 homers, 17 steals, 79 runs, and 74 RBI.

In comparison, here's the projection of one player next year: .276 AVG, .805 OPS, 23 homers, 15 steals, 79 runs, and 83 RBI; more or less, the two players are equal, and Dukes may have a slight edge. The second player is Torii Hunter, who's ranked No. 19 among outfielders.

Washington may seem like a 60-win team—what with their terrible pitching and still-haven't-broken-out offense—but they are capable of winning 75 or 80 in 2009.

Maybe just not conventionally.

5. Florida Marlins

Florida may be the trendiest sleeper pick of the year aside from Kansas City. What's not to like? They won 84 games last year, they found a gem in Ricky Nolasco, and they have arguably one of the best players in the league in Hanley Ramirez.

What's not to like is the departures of Mike Jacobs and Joe Nelson, and the trade of Kevin Gregg, Josh Willingham, and Scott Olsen for Emilio Bonifacio and a minor league. What's not to like is that the Marlins have no veteran pitchers—they have only two pitchers projected have have over 100 innings.

Nolasco was one of the big surprises for the Marlins last year, posting a 3.52 ERA, a minuscule 1.10 WHIP, and 186 strikeouts in 212 innings. In 2006, though, Nolasco had a 4.82 ERA, a 1.41 WHIP, and 99 strikeouts in 140 innings. (He only had 21 innings in 2007.)

FEIN projects a 4.01 ERA, a 1.24 WHIP, and 136 strikeouts in 170 innings. He's ranked No. 25 among starters.

After Tommy John surgery cut short his 2007 season, Josh Johnson came back strong in 2008, putting up a 3.61 ERA in 14 starts. It was only three years ago that Johnson had a 3.10 ERA and 133 strikeouts in 157 innings in his rookie season, and he's projected to have a 3.78 ERA, a 1.33 WHIP, and 82 strikeouts in 95 innings in 2009.

Shortstop Hanley Ramirez had 33 homers, 125 runs, 35 steals, a .301 AVG, and a .940 OPS in 2008, after 29 home runs, 125 runs, 51 steals, a .322 AVG, and a .948 OPS the year before. It's impossible to say whether he's more of a threat to hit 40 homers or steal 40 bases, because he's capable of both.

He's projected to have 26 homers, 39 steals, 116 runs, 70 RBI, a .310 AVG, and a .907 OPS in 2009. His 26 homers and 39 steals give him a forecasted Power/Speed number of 31.2, highest in the league. FEIN ranks him No. 1 overall.

Second baseman Dan Uggla has floated around 30 homers, 90 RBI, and 100 runs in his three years in the majors, but his AVG has fluctuated from .282 to .245 to .260 last year, and his strikeout rate has increased by six percent each year.

FEIN sees a slight drop from those numbers, with a projected line of 28 homers, 85 RBI, and 94 runs, with a .260 AVG and .815 OPS. He's ranked No. 5 among second baseman and No. 52 overall.

As the Marlins' offseason showed, they aren't looking to contend in 2009. With Matt Dominguez, Cameron Maybin, Ryan Tucker, Jose Ceda, Chris Volstad, Emilio Bonifacio, and all their other young prospects, Florida will be at the top of the NL East in four or five year.

It's just not their time right now.

NL Central Projected Standings: Will the Chicago Cubs Break the Curse?

Zach Fein — Mon, 23 Mar 2009 00:16:34 -0400

Note: All team projections were found using the FEINs projections. An explanation behind projected wins, losses, and other stats can be found here.

1. Chicago Cubs

The Cubs' 97 wins last year were a National League high and the Cubs' most since 1945. But the so-called curse still stands, after yet another playoff defeat. Last year was supposed to be the year.

And this year is supposed to be the year, too. Will it?

FEIN's projected 93 wins for the Cubs is five more than any other NL team and the third-highest in the majors. It appears that the Cubs are the favorites by far in the NL—appears is the keyword.

The Cubs have lost six of their last seven postseason series and 22 of their last 31 postseason games, and all preconceptions about the Cubs' chances are thrown out the window in October.

Chicago's offense is lead by five guys projected to have an OPS over .830: Milton Bradley (.927), Aramis Ramirez (.883), Alfonso Soriano (.867), Derrek Lee (.857), and Geovany Soto (.833).

In his first season with 500 PA since 2004, Bradley put up an OPS of .999, fourth-highest in the league, and an AVG of .321, sixth-highest in the league. His projected .927 is a significant drop, but would still be good for sixth in the league.

FEIN projects 18 home runs, but just 56 RBI and 57 runs, in 400 PA. That makes him ranked No. 238 overall and not worth a slot in standard 12-team leagues.

Second in OPS on the Cubs is third baseman Aramis Ramirez. Ramirez has had at least 26 home runs in each of the past six years, over 100 RBI in five of the past six years, and a .898 OPS or higher in the past five years. His projected 29 home runs, 102 RBI, and 80 runs makes him worth a third-round pick, No. 29 overall.

But Ramirez isn't the best fantasy pick on the Cubs. Outfielder Alfonso Soriano has seen his stats go down since he joined the Cubs, but an average of 31 homers, 87 runs, and 19 steals isn't bad. FEIN projects 33 homers, 86 runs, and 21 steals, along with a decent .277 AVG. He's ranked two spots ahead of Ramirez at No. 27 overall.

The Cubs have one of the best pitching staffs in the league, too. Their rotation is headed by Carlos Zambrano (projected ERA of 3.91), Ryan Dempster (3.65), and Rich Harden (3.00).

Together, they are forecasted to have a 3.56 ERA and a 1.29 WHIP in 477 innings. And their fourth starter, Ted Lilly, isn't too bad either: He's projected to have a 4.12 ERA in 185 innings.

Incredibly, Carlos Marmol is the best pitcher on the Cubs, at least according to fantasy value. His 3.18 ERA and 1.19 WHIP, plus 90 strikeouts and 34 saves, in 82 innings would make him No. 3 reliever and No. 61 overall.

Chicago has all the pieces it needs to overcome their 101 years of losing and compete for the World Series title.

Then again, it's the Cubs.

2. St. Louis Cardinals

Just two-and-a-half years removed from a World Series win, the Cardinals are forecasted to have the ninth-best record in the NL—yet their 82 wins would be just one less than the 83 wins accumulated in their 2006 season.

The leader of that World Series team was Albert Pujols, who, in 2006, hit 49 home runs with a .331 AVG and 137 RBI. FEIN projects 36 homers, over 100 runs and RBI, a .326 AVG, and a 1.025 OPS in 2009.

It's no shock that he's the best player in baseball. According to Fangraphs, his production has been worth over $190 million since 2002, the highest in the league—and he's only been paid $60 million in that time. Pujols has also been worth the most money in the past year, the past two years, and the past three years.

Pujols isn't the only slugger on the Cardinals. Ryan Ludwick hit 37 bombs last year with a .966 OPS. He's projected to hit just 22, but considering that's just in 427 PA, he'd hit 32 in the 617 PA he had last year, as well as just under 100 RBI and a .875 OPS.

Third baseman Troy Glaus, in his first year as a Cardinal, hit 27 homers and had 99 RBI and a .856 OPS. His .270 AVG last year was the second-highest of his career. He's projected to have 27 home runs once again, as well as 86 RBI, 70 runs, a .264 AVG, and a .854 OPS.

Since 2005, Glaus' OPS has been anywhere from .839 to .885, and his OPS hasn't been below .800 this decade, so you know you can trust him. FEIN ranks him No. 7 among third basemen.

Adam Wainwright has had a combined 3.50 ERA in 324 innings the past two years as a starter, plus a 3.12 ERA in 75 innings as a reliever in 2006. FEIN's projection gives Wainwright a 3.67 ERA and a 1.32 WHIP in 145 innings, and if he can stay healthy, he'd be a very good No. 2 pitcher in fantasy leagues.

Currently fourth in the Cardinals' rotation is the former Cy Young winner Chris Carpenter. Carpenter has pitched just 21 innings in the last two years due to forearm and shoulder injuries. He had a 3.09 ERA the year before his first injury, which was in 2006.

But because FEIN's pitcher projections look at only the last three years of stats, all that is seen is a 3.09 ERA and then two years with under 25 innings pitched. Carpenter is projected to have a 3.86 ERA in just 76 innings, but he is a nice, low-risk, high-reward pick late in your draft.

2009 looks to be to first year in some time that the Cardinals won't be fighting for the playoffs come late September. Of course, that's what the experts said last year, too, when the prognostication for St. Louis was under 80 wins.

They won 86.

3. Milwaukee Brewers

For the first time since 1982, the Brewers advanced to the postseason, though their stint was cut short by the eventual World Series champions.

Midway through the season, they acquired CC Sabathia from the Indians, but contrary to public belief, he wasn't so much of a help as previously thought: Milwaukee was 49-40 when Sabathia made his first start as a Brewer, and from then on Milwaukee went 41-32. The upgrade was just a difference of one game in those 73 games.

Sure, they would've been tied with the Mets for the Wild Card had they not gotten Sabathia, but in retrospect, would you rather have a playoff appearance and early exit with your top prospect (Matt LaPorta)—as well as the player you traded for in the first place—off of your team, or a one-game playoff for the postseason with your future intact?

Of course, there's more to the Brewers' future than LaPorta. Outfielder Ryan Braun had 37 homers, 14 steals, 92 runs, and 106 RBI last year (after a Rookie of the Year award the year before). His projection gives him a .901 OPS, a .299 AVG, 32 home runs, 13 steals, 92 runs, and 99 RBI in 589 PA.

If there's one flaw in Braun's game, it has to be his batting eye—Braun is projected to have only 37 walks in 2009. He's ranked No. 9 overall and No. 2 among outfielders, behind only Grady Sizemore.

Prince Fielder's hit 84 home runs the last two years, and admittedly his forecast his somewhat pessimistic. He's projected to hit just 30 homers and 89 RBI, but his low projected playing time (580 PA) may lead to his low counting stats; in 694 PA—his total last year—his projected stats become 36 homers, 106 RBI, and 95 runs, with a .284 AVG.

In comparison, Mark Teixeira's ranked No. 18 overall, and he's projected to hit 31 homers, 112 RBI, and 93 runs, with a .292 AVG. More or less, Fielder's around Teixeira's area in a full season of play, yet Fielder's currently going a round after Teixeira in ESPN.com and Yahoo! drafts.

The Brewers' rotation may be one of the worst in the league. The top four starters projected to pitch the most innings (Braden Looper, David Bush, Jeff Suppan, and Manny Parra) all have a projected ERA over 4.42, with the average being 4.56.

On their actual depth chart, though, their No. 1 pitcher is Yovani Gallardo. Gallardo only had four starts last year after an ACL injury that forced him to miss most of the year.

Still, he had a 1.88 ERA, a year after a 3.67 ERA in 110 innings. Gallardo is projected to have a 3.82 ERA and a 1.31 WHIP in just 77 innings this year, but the latter number should rise.

Milwaukee hopes new manager Ken Macha will get the Brewers back to the playoffs again. The Brewers have returning all of their key offensive pieces—they're forecasted to score the same number of runs as last year.

But even the signing of Trevor Hoffman can't make up for the departures of CC Sabathia and Ben Sheets, as Milwaukee's projected runs allowed per game is more than half a run higher than last year's 4.25.

4. Cincinnati Reds

Almost three-fourths of the season past them, the Reds were looking at a 52-66 record and their eighth below-.500 season in as many years. With outfielder Adam Dunn looking to cash in on free agency at the end of the year, the Reds decided to trade Dunn to Arizona in exchange for Micah Owings and two minor leaguers.

In the end, the trade turned out to be great for Cincinnati. They went 22-21 after the trade and allowed Chris Dickerson to show his talent in the majors. In his 122 PA, Dickerson had six homers, 20 runs, and 15 RBI, as well as a .304 AVG and a 1.021 OPS.

His projection calls for a .288 AVG and .877 OPS, with slightly lower counting stats than last year. However, Dickerson had an OPS under .800 in two of three years in the minors and 34 home runs in his minor league career—in over 1400 PA.

Second baseman Brandon Phillips regressed from 2007, lowering his 30-30 season to just 20-20 in 2008, plus a decrease in runs, RBI, AVG, and OPS. FEIN projects 22 homers, 25 steals, 81 runs, 79 RBI, a .277 AVG, and a .782 OPS.

Phillips is one of only six players projected to have more than 20 home runs and 20 steals. Those numbers make him the No. 3 second baseman and No. 38 overall.

The best pure hitter on the Reds is Joey Votto. Votto hit 24 home runs last year with a .874 OPS and a .297 AVG. Votto is projected to have a .880 OPS and a .302 AVG and hit 16 home runs in 403 PA. In a full season of play (600 PA), Votto would be projected to have 71 runs scored, 24 homers, 86 RBI, and have six stolen bases.

The most comparable player is Adrian Gonzalez, who's projected to have a .282 AVG, 79 runs, 25 home runs, 85 RBI, and no steals. Votto's per-600 stats are much better than Gonzalez's, who's ranked No. 9 among first basemen.

One of the main reasons for Cincinnati's poor pitching last year was Aaron Harang, who, after three years of sub-3.85 ERAs, went 6-17 with a 4.78 ERA in 184 innings. FEIN thinks Harang will bounce back, with a projected 4.16 ERA, 1.28 WHIP, and 158 strikeouts in 180 innings.

No. 2 in the Reds rotation is 25-year-old Edinson Volquez. Volquez, in his first full year in the majors, pitched 196 innings and had 206 strikeouts and a 3.21 ERA. His projected 3.81 ERA, 1.31 WHIP, and 163 strikeouts in 165 innings make him the No. 29 starting pitcher.

The Reds have a great young core, and though Votto and Co. won't do so well this year, they'll be at the top of the NL Central standings in the next few years at the least.

5. Houston Astros

Houston may be the one team that does the least with the most talent. With Lance Berkman, Carlos Lee, Miguel Tejada, Roy Oswalt, et al, their win total should be near 85, not 75.

At least from a fantasy perspective, the Astros as an above-.500 team.

Lance Berkman's home runs have dropped each of the past two years, but 29 home runs, over 100 runs and RBI, 18 steals—double his previous career high—and a .312 AVG made him the seventh-most valuable player last year, according to Last Player Picked.

FEIN projects 31 homers, 90 runs, 101 RBI, nine steals, a .287 AVG and a .920 OPS in 633 PA in 2009. He's ranked No. 26 overall and fifth among first basemen.

Outfielder Carlos Lee had just 28 home runs last year—his first season under 30 since 2002—and missed significant playing time due to injury for the first time in his career. If he had kept his home run pace and had 697 PA (his 2008 total), he would have eclipsed 40 homers for the first time in his career.

Currently, he's projected to hit 29 home runs in 579 PA, with 98 RBI, 73 runs, 10 steals, a .291 AVG, and a .869 OPS. Those numbers are worth a fourth-round pick, No. 39 overall, and he's worthy of being a No. 1 OF in standard leagues (Lee's ranked No. 11 among outfielders).

Hunter Pence's rate stats dropped dramatically from his rookie season to last year, as his AVG dropped 53 points and his OPS fell more than 110 points.

But his BABIP decreased from .378 to a more common .303 BABIP, and his other stats declined accordingly. FEIN projects a .285 AVG and a .811 OPS, in addition to 21 homers, 70 runs, 75 RBI, and 11 steals.

Through the first two months of the year (including March), Roy Oswalt's ERA was a paltry 5.45. From then on, however, Oswalt had an ERA three full runs lower, at 2.44, to give him a 3.54 ERA on the year, the lowest of his career.

Oswalt's projected ERA is slightly better than last year's, however. He's projected to go 12-10 with a 3.50 ERA, a 1.26 WHIP, and 149 strikeouts in 192 innings. Despite those numbers, though, he ranks just No. 15 among starting pitchers, or six spots ahead of where he's going on average in ESPN.com drafts.

Things will only get worse for Houston. Of the players noted above, only Hunter Pence is below 30. Pence and J.R. Towles are the only players projected to have an OPS above .700 and are under 30; the other six with an OPS over .700 are 30 or older.

The Astros have seven guys projected to have more than 75 innings; none are younger than 30. That means they have no one younger than 30 in their current rotation.

Enjoy your playoff appearances, Houston. You won't see it for a while.

6. Pittsburgh Pirates

What is there to say? Sixteen consecutive seasons under .500, under 70 wins each of the past three years, under 76 every year this decade...seriously?

Pittsburgh has a whopping 22 pitchers on their team or now in the minors that had 10 or more innings pitched last year. Only one is projected to have an ERA under 4.00. That pretty much sums up the Pirates' pitching staff.

Paul Maholm had a 3.71 ERA in 206 innings last year but had just nine wins (thanks to just three runs of run support in more than half his starts) and 139 strikeouts. Maholm's projected to have a 4.30 ERA and a 1.33 WHIP in 188 innings, with 10 wins and 124 strikeouts.

He's ranked just No. 66 among starting pitchers.

Closer Matt Capps is the pitcher with a sub-4.00 projected ERA. He's had a combined 3.04 ERA and 1.05 WHIP the past three years, and FEIN projects a 3.40 ERA and a 1.20 WHIP (plus 27 saves) in 64 innings in 2009.

Outfielder Nate McLouth broke out last year, with 26 home runs, 113 runs, 94 RBI, and 23 steals; he received a three-year, $14.5 million deal for his production. He's projected to have 15 homers and 17 steals in 466 PA; in the 685 PA he had last year, his full projected stat line becomes 22 homers, 109 runs, 76 RBI, 25 steals, and a .269 AVG.

Adam LaRoche—not to be confused with brother Andy LaRoche—had a .841 OPS with 25 homers and 85 RBI in 2008, after a .803 OPS in 2007 and a .915 OPS in 2006. FEIN thinks he'll regress from his 2008 stats and projects a .818 OPS, 22 homers, and 81 RBI with a .271 AVG.

It would the wonderful if Pittsburgh could be the next Tampa Bay and win 80 or 90 games next year. It just won't happen next year.

Or in the next three.

NCAA Tournament Trends of the Day: Sweet Sixteen and Final Four

Zach Fein — Wed, 18 Mar 2009 14:57:49 -0400

We've gotten through the first two rounds and the Final Four . Now, let's get to the Sweet Sixteen and look at the Final Four again.

Like last time, all statistics are since 1989, unless otherwise noted.

Sweet Sixteen

Which seeds win?

The following table shows the wins and losses of each seed in the Sweet Sixteen. The #/Year column shows the number of wins per year or, in other words, the number of Elite Eight teams per year.

Seed	W-L	W%	#/Year
1	56-14	0.800	2.8
2	36-13	0.735	1.8
3	22-21	0.512	1.1
4	13-25	0.342	0.65
5	5-21	0.192	0.25
6	8-21	0.276	0.4
7	5-9	0.357	0.25
8+	15-36	0.294	0.75

• About three No. 1 seeds make the Elite Eight every year. This means that one should lose in the Sweet Sixteen.

• About two No. 2 seeds make the Elite Eight. Since we had one No. 2 seed losing in the second round, we should expect one more losing in the Sweet Sixteen. (Note: The exact number of No. 2 seeds losing in the second round was 1.6. If you went "bold" and chose two No. 2 seeds to lose in the second round, you should move your remaining two seeds into the Elite Eight.)

• One No. 3 seed makes the Elite Eight. Two were projected to lose in the second round, so of the remaining two No. 3 seeds, we should advance one.

• Only one No. 4 or 5 seed should reach the Elite Eight. In theory, it's easy to understand: One No. 1 seed will lose. They will most likely play the No. 4 or 5 seed. Of course, that means that only one No. 4 or 5 seed will advance (if a No. 12 or 13 seed faces the top seed, the No. 1 seed will win every time—see below). This reasoning also explains the number of No. 6-and-below seeds in the Elite Eight.

• The table above shows that the winning percentage of the No. 5 through 7 seeds increases as the seed gets worse. It seems unfeasible, but think about the matchups: The No. 5 seed would most likely play the No. 1 seed, No. 6 would play No. 2, and No. 7 would play No. 3. The lower the seed, the easier the matchup.

Which brings us to the next point. How do we quantify the individual matchups for each seed? Is there a clear winner in the No. 2-vs-No. 3 matchup, or any other matchup?

Looking at Individual Matchups

Matchup	W-L	W%
1 vs 4	23-9	0.719
1 vs 5	19-5	0.792
1 vs 12/13	14-0	1.000
2 vs 3	16-10	0.615
2 vs 6	16-3	0.842
3 vs 7	5-2	0.714
3 vs 10	7-3	0.700

• Here's one way to make use of this table: In my bracket, I have a No. 1 seed facing two No. 4 seeds, a No. 5, and a No. 12. I can move ahead the team playing the No. 12 seed automatically.

For the other three matchups, I find the weighted winning percentage of the No. 1 seed (by weighing the winning percentage against No. 4 seeds twice as high as the winning percentage against No. 5 seeds); this is .739.

Then, with three matchups, they'd be expected to lose 0.784 of them. With that number, combined with the fact that 1.2 No. 1 seeds are expected to bow out in the Sweet Sixteen no matter whom they face, I can be certain that one of my three No. 1s will lose.

Final Four

Which seeds win?

The first article in this series was determining which seeds make the Final Four. Now, I'll look at the results of those matchups to see which seed most often makes the Finals.

This table shows the record of each seed in the Final Four. These exclude matchups where one seed plays the same seed, such as two No. 1 seeds facing each other.

Seed	W-L	W%
1	12-8	0.600
2	7-8	0.467
3	8-4	0.667
4	2-7	0.222
5	2-2	0.500
6	1-0	1.000
8	0-2	0.000
11	0-1	0.000

• The Final Four is unpredictable. No. 1 seeds are only 12-8, No. 2 seeds are under .500, and No. 3 seeds do better than No. 1s.

• By the way, no No. 7 seed has reached the Final Four, let alone the National Championship game.

The table above doesn't show any conclusions to take away from it. How about looking at the resulting matchups in the Finals?

Looking at National Championship matchups

The table below shows the frequency of each matchup in the Finals since 1989, looking at the top seed, the lower seed, and the combined matchup.

Matchup	Freq.
1, _	14
2, _	5
3, _	1

_, 3	7
_, 1	5
_, 2	3

1, 1	5
2, 3	5
1, 2	3
1, 4	2
1, 5	2
1, 3	1
1, 6	1
3, 3	1

• Now this is a tough decision. The top seed has been a No. 1 seed 70 percent of the time, while the lower seed has been a No. 3 seed 35 percent of the time. But a matchup of those teams has occurred just once, while a matchup of two No. 1 seeds and a matchup of a No. 2 and No. 3 occurring five times each.

• I'll stick to the latter part of the table, which shows the actual matchups. A No. 1-vs-No. 1 or No. 2-vs-No. 3 matchup is the most probable.

Putting it all together

I won't go through the trends and upsets I've shown in the first two articles; you can read those for the advice.

But I've put all my advice together and made a bracket based on historic trends. You can see my bracket below (click for larger view).

Based on the trends, Syracuse should have beaten Gonzaga, but I think the Bulldogs are a much better team.

The hardest games to pick were definitely UCLA versus Villanova and West Virginia versus Michigan State. I like all the No. 6 seeds this year except Marquette (I could see Arizona State upsetting Syracuse). I think UCLA is slightly better than Villanova, but in Philadelphia, the Wildcats should advance.

As for the second game, West Virginia is rated high by most rating systems, including a No. 8 rankings in Pomeroy's ratings, five spots higher than Michigan State. But the difference in their ratings (.015 in winning percentage) equates to about a two-point difference in an actual game. That difference wasn't enough for me to choose the upset.

NCAA Tournament Trends of the Day: First and Second-Round Upsets

Zach Fein — Tue, 17 Mar 2009 17:38:58 -0400

The majority of bracket-fillers know the age-old advice to pick at least one No. 12 seed upset in the first round. This article will look at other predictable first-round upsets and how those teams compare in the second round of play.

All statistics are in the past 20 years, unless noted otherwise.

First-round upsets

The top three seeds

• No. 1 seeds have never lost in the first round.
• No. 15 seeds have beaten No. 2 seeds four times and only once since 2000.
• No. 14 seeds have upset No. 3 seeds 11 times in 80 tries, but only twice in 36 tries since 2000.

No. 13 over No. 4

• No. 4 seeds have lost 16 times in 80 games, a one-in-five rate. They've won 28 of their 36 matchups since 2000, a winning percentage that is slightly higher than the historical rate.
• No. 13 seeds won twice last year and have upset No. 4 seeds four times since 2005, or once per year. Since 2001, there has been at least one upset in every year but two (2002 and 2007).

No. 12 over No. 5

• The so-called "inevitable" upset, the No. 12 seed has upset the No. 5 seed 28 times, or a .350 winning percentage, and they've won 13 times this decade, a .361 winning percentage.
• Since 2000, a No. 12 seed has won in every year except 2000 and 2007. Excluding those years, the No. 12 seed is 13-15 in the first round. In five of the seven years an upset occurred, two or more No. 12 seeds won.

No. 11 over No. 6

• You can say that this game deserves more popularity as being an upset pick. The No. 11 seed has won 25 games, or 1.25 per year, and since 2000, they've won exactly one-third of their games, or 1.33 per year.
• This upset has occurred in every year since 2005, a total of six times.

No. 10 over No. 7

• The No. 10 seed has upset No. 7 32 times, or two out of every five matchups. They've won just one-third of the time since 2000.
• After seven upsets from 2000 to 2003, the No. 10 seed has won just five times since 2004, or once per year.

No. 9 over No. 8

• Surprisingly, the No. 9 seed is 45-35 against the No. 8 seed, or 2.25 upsets per year. Since 2000, however, the No. 9 seed is only 17-19.
• Since 2005, the No. 9 seed has gone 9-7, in line with their 1980-2008 record. They've won at least once in every year since 2003.

Summary

• Advance the first three seeds in your bracket to the second round without thinking. The No. 4 seed has won 80 percent of their games—if you're feeling lucky, go ahead and pick a No. 13 over a No. 4, but it's best to play it safe and go with the No. 4 seeds, because if you choose wrong, your bracket is dead.
• Pick at least one upset of a No. 5 seed, but no more than three. Pick one No. 11-over-No. 6 upset and one No.10-over-No. 7 upset. For the matchups between the No. 8 and No. 9 seed, pick either one or two upsets, but not three.

Second-round upsets

• Here's a table showing the results of the top five seeds in the two possible matchups in the second round.

Matchup	1980-on	2000-on
1 vs 8	28-7	15-4
1 vs 9	42-3	16-1
2 vs 7	34-13	15-9
2 vs 10	15-14	4-7
3 vs 6	25-22	12-10
3 vs 11	18-4	9-4
4 vs 5	24-18	7-11
4 vs 12	14-7	7-3
5 vs 13	7-2	4-1

No. 1 seeds

• As common knowledge supports, No. 1 seeds are locks to win in the second round and to make the Sweet Sixteen.

No. 2 seeds

• This is unexpected. Against No. 7 seeds, No. 2 seeds are 15-9 since 2000, or only 2.5 wins per four matchups. In three matchups (that is, if a No. 7 seed is upset in the first round), No. 2 seeds would be expected to lose one game.
• And this is even more unexpected. No. 2 seeds are 4-7 against No. 10 seeds since 2000, but due to the small sample size, the "real" results are about 50-50 (15-14). Using the data from above, we know that one No. 10 seed will advance to play the No. 2 seed, which means there's a 50-50 shot that one No. 10 seed makes the Sweet Sixteen.
• Together, we should expect 2.4 No. 2 seeds to make the Sweet Sixteen. (15 divided by 24 times three, plus 15 divided by 29, equals 2.39.) That's certainly lower than previously thought.

No. 3 seeds

• Against No. 6 seeds, the No. 3 seeds have won 55 percent of its games. In three matchups versus No. 6 seeds, the No. 3 seed should be expected to lose 1.4 of them, just under half.
• Against No. 11 seeds, however, the winning percentage of No. 3 seeds rises dramatically, to 69 percent.
• Together, we should expect 2.3 No. 3 seeds to make the Sweet Sixteen. (12 divided by 22 times three, plus nine divided by 13, equals 2.33.)

No. 4 and 5 seeds

• No. 4 seeds have losing records against No. 5 seeds since 2000, winning just 39 percent of their matchups. Against No. 12 seeds, however, they've won 70 percent of their games.
• No. 5 seeds, since 1980, have won 78 percent of their games against No. 13 seeds.
• We should expect 1.9 No. 4 seeds to make the Sweet Sixteen if only one No. 5 seed loses in round one, but 2.2 if two lose in the first round (this is on the assumption that all four No. 4 seeds win their first game).
• We should expect 1.8 No. 5 seeds to make the Sweet Sixteen if only one loses in the first round, and only 1.2 if two lose in the first round.

Summary

• The second round is where you can make the upset picks no one else will. You should pick one No. 7 seed to beat a No. 2 seed, and a matchup between the No. 10 seed and No. 2 seed is a flip of the coin.
• You should also select one No. 6 seed to upset the No. 3 seed and two if you're feeling lucky, but always take the No. 3 over the No. 11.
• No. 5 seeds should win approximately two of three or one of two (depending on how many No. 5 seeds lose in the first round) matchups against No. 4 seeds. No. 4 seeds should win all of their games against the No. 12 seed.

Tomorrow, I'll look at the Elite Eight and unveil my bracket.

NCAA Tournament Trends of the Day: Final Four

Zach Fein — Tue, 17 Mar 2009 00:46:44 -0400

Number of No. 1 Seeds

• Last year was the first year all four No. 1 seeds made the Final Four.

• In the last 20 years, a No. 1 seed has made the Final Four every year except 2006.

• Only four times in those 20 years have three or more No. 1 seeds made the Final Four: 1993, 1997, 1999, and 2008. That means that in 15 of those 20 years, one or two No. 1 seeds made the Final Four.

• Of those 15 instances, nine times did only one No. 1 seed make the Final Four, and six times did two No. 1 seeds make the Final Four.

Which Seeds Make It?

• In 14 of 20 years, a No. 2 seed has made the Final Four.

• But in only three of those 14 did two No. 2 seeds make it. (Never have three made the Final Four.)

• A No. 1 seed and No. 2 seed have made the Final Four together 12 times.

• In nine of 20 years, a No. 3 seed made the Final Four.

• In three of those nine did two No. 3 seeds make it.

• The No. 4 seed has almost an equal shot of making the Final Four as a No. 3 seed. In eight of 20 years, a No. 4 seed made the Final Four, but only once did more than one make it.

• In six of 20 years, a seed lower than No. 4 made the Final Four; in 2000, three of the four Final Four teams were lower than No. 4, but in the other five years, only one made it.

Here are the distributions of each seed making the Final Four:

Seed	#/Year	Freq.
1	1.70	95%
2	0.85	70%
3	0.60	45%
4	0.45	40%
5	0.20	20%
6	0.05	5%
7	0.00	0%
8	0.10	5%
9	0.00	0%
10	0.00	0%
11	0.05	5%

Sum of Seeds

• One way to make sure you are not picking too many upsets or too many favorites is to sum up the seed numbers of all the teams predicted to make the Final Four. The lowest total in the past 20 years was four, when all four No. 1 seeds made it last year. The highest was 22, in 2000, when two No. 8 seeds and one No. 5 seed made the Final Four.

• In the last 20 years, the average sum has been 9.7, but since 2000 that number rises to 10.7. (But if you take out the two years with a sum of 20 or more, in 2000 and 2006, those two numbers are 8.4 and 7.7, respectively.)

• Nine out of 20 years has the sum been seven, eight, or nine. Another two times the sum was 11. No other total has occurred more than once.

Combination of teams

• Here are the number of times each combination of seeds has reached the Final Four, looking at the top two, the top three, and all four seeds.

Seeds	#
1, 1	10
1, 2	7
1, 3	1
1, 5	1
2, 3	1

1, 1, 1	4
1, 1, 2	4
1, 2, 2	3
1, 2, 3	3
1, 1, 4	2
1, 2, 4	1
1, 3, 4	1
1, 5, 8	1
2, 3, 4	1

1, 2, 3, 3	3
1, 1, 1, 4	2
1, 1, 2, 3	2
1, 1, 4, 5	2
1, 2, 2, 3	2
1, 1, 1, 1	1
1, 1, 1, 2	1
1, 1, 2, 2	1
1, 1, 2, 5	1
1, 2, 2, 4	1
1, 2, 4, 6	1
1, 3, 4, 4	1
1, 5, 8, 8	1
2, 3, 4, 11	1

• In (only) 12 of 20 years did both a No. 1 and No. 2 seed make the Final Four.

• In the nine times only one No. 1 seed made the Final Four, a No. 2 seed has made it seven times, and three of those times did two No. 2 seeds make it. In six of those nine, a No. 3 seed made it, and in three of those six did two No. 3 seeds make it.

• In the six times only two No. 1 seeds made the Final Four, a No. 2 seed made it, too, four times, and five teams ranked No. 4 or No. 5 made the Final Four (but it only happened twice, since both made it in 1996 and 2005).

• In 15 of 20 years, either a No. 3 or No. 4 seed has made the Final Four, but only twice have they both made it.

• In the eight years a No. 4 seed made the Final Four, another No. 4 seed or lower also made the Final Four five times.

Summary

• Don't pick more than two No. 1 seeds to make the Final Four. There's a higher chance of only one making it than only two making it.

• Pick only one No. 2 seed.

• Pick a No. 3 or No. 4 seed to make the Final Four, but don't pick both to make it.

• The optimal Final Four combination should consist of one of the following:

•

Those three combinations have occurred in seven of the 20 years.

AL West Projected Standings: Why the Angels Are Down and the Rangers Are Rising

Zach Fein — Mon, 16 Mar 2009 21:00:22 -0400

Note: All team projections were found using the FEINs projections. An explanation behind projected wins, losses, and other stats can be found here.

1. Los Angeles Angels

Angels fans will say that the projected 83 wins is too low, but consider this: The Angels won 100 games last year, but their run differential suggested they were an 88-win team. Sixty-one of their wins were by one or two runs, by far the most in the league.

In games decided by two runs or less, the Angels went 61-28 (.685 win percentage); but in games decided by more than three runs, the Angels went 39-34 (.534 win percentage). Only they and Tampa Bay had a win percentage over .576 in one- or two-run games, and FEIN is also down on the Rays, who had a .667 win percentage in such games.

Over the offseason, the Angels lost key pieces to their 100 wins, first baseman Mark Teixeira and closer Francisco Rodriguez. The Angels traded Casey Kotchman for Teixeira, so they are stuck with Kendry Morales at first. Morales is projected to have a .747 OPS and .268 AVG, both below league average.

The Angels added Brian Fuentes to take over for Rodriguez' vacated closer role. In a neutral park and league, Fuentes' projected ERA is about 30 points higher than Rodriguez', so the drop off is somewhat big. Nevertheless, Fuentes' 3.46 projected ERA is still valuable, though his 8.48 strikeouts per nine would be a significant decrease from last year.

The Angels also added Bobby Abreu to start at left field. FEIN is very down on Abreu, projecting a .276 AVG and .799 OPS, with 16 HR and 23 SB in 676 PA. But for one year and $5 million, you've got to take the chance, right?

Designated hitter Vladimir Guerrero lost a year on his age when he unintentionally admitted to reporters that he was actually 34. Vlad's projection—.305 AVG, .890 OPS, 27 home runs, and 101 RBI in 608 PA—is actually an increase in his 2008 stats and warrants the No. 22 pick in fantasy leagues.

Pitcher Joe Saunders had a 3.41 ERA and 1.21 WHIP in 198 innings last year, a year after a 4.44 ERA and 1.52 WHIP. FEIN thinks he'll essentially split the difference, with a projected 4.08 ERA and 1.34 WHIP in 171 innings. He, John Lackey (projected ERA of 3.78), Ervin Santana (4.10), and Jered Weaver (3.91) anchor one of the best rotations in baseball.

2. Texas Rangers

The Rangers have one of the best lineups in the league, projected to score the eight-most runs. But their pitching is horrendous. Projected to allow the second-most runs, the Rangers would be 86-76 if they had a league average pitching staff. Essentially, their pitching is costing them nine wins.

The Rangers' best starter is Kevin Millwood. He's projected to have a 4.70 ERA. Let's move on.

Josh Hamilton continued his comeback story last year, with 32 home runs, 130 RBI, and a .901 OPS. FEIN sees him lowering those numbers, but mainly because of the 145 projected PA drop. Adjusting his projected stats to the 704 PA he had last year, Hamilton's projected line becomes 33 home runs, 116 RBI, 97 runs, and a .860 OPS. His projected stats as is make him the No. 40 hitter in fantasy.

First baseman Chris Davis is going to be a star one day. His current projection gives him a .900 OPS, a .304 average, 16 home runs, and 48 RBI in 303 PA; prorated to 600 PA, that's 32 HR and 95 RBI, as well as 85 runs.

For a comparison, David Ortiz is projected to have 33 HR, 105 RBI, 88 runs, and a .291 AVG. Davis is just slightly worse than Ortiz in 600 PA; Ortiz is projected to be No. 28 overall in fantasy. Take Davis with confidence in the sixth round of your draft.

Ian Kinsler is projected to be the fourth-best 2B this year, with 18 home runs, 20 steals, 87 runs, and a .289 AVG. Despite that, he's ranked only No. 49 overall. In Yahoo! fantasy leagues, for example, the No. 4 second baseman is going inside the top 30.

FEIN thinks new third baseman Michael Young—who moved from shortstop to make room for Elvis Andrus—should increase his production from 2008, with a projection of 92 runs, 89 RBI, a .303 AVG, and a .787 OPS. FEIN also projects 14 home runs, which he hasn't reached since 2006. Young ranks exceptionally high at No. 21 overall, good for a second-round pick in 12-team leagues. That he'll have both 3B and SS eligibility—and that he's currently going outside the top 85 overall in both ESPN an Yahoo! league—makes him a nice sleeper pick in the seventh round.

With their core of twenty-something hitters, the Rangers should be successful for the next three or four years—that is, if they can just find some pitching.

3. Oakland Athletics

At first, it looked like Oakland was stocking up for the future. They traded Rich Hardin, Chad Gaudin, and Joe Blanton at the trade deadline last year for a wealth of minor leaguers. Then, in mid-November, they were the winners of the Matt Holliday sweepstakes, giving up Carlos Gomez, Greg Smith, and Huston Street to the Colorado Rockies in exchange for Holliday. They seemed to be playing for 2009 now and in the remaining months of the offseason signed Jason Giambi, Orlando Cabrera, and Nomar Garciaparra.

In Colorado, Holliday was a monster, batting over .320 with a .947 OPS or higher in each of the past three years. But FEIN thinks the switch from Coors Field to McAfee Coliseum and from the NL to the AL will severely depress Holliday's stats. FEIN projects a .291 AVG and a .836 OPS, with 22 home runs, 15 steals, and 94 runs and RBI, in 621 PA. Though his stats seem low, he ranks 19th among hitters and 25th overall in fantasy.

Fellow acquisition Jason Giambi still has power but is lacking the ability to hit anything else; he's projected to hit 23 home runs with a .247 AVG. Nevertheless, his .374 OBP and .470 SLG are greatly above league average.

The A's pitching staff is predominantly unproven. Ace Justin Duchscherer had a 2.54 ERA and 1.00 WHIP last year in his first year as a starter. His projected 3.58 ERA would be 12th among starters, but in just 125 projected innings, that 3.58 ERA isn't that valuable; indeed, Duchscherer will miss the start of the season due to a setback in his rehab from his season-ending elbow injury.

No. 2 pitcher Dana Eveland's 2008 season was also his first as a full-time starter. Eveland had a 3.34 ERA heading into August, but from then on, he had a 5.70 ERA. His post-July performance is one for concern, and FEIN projects a 4.61 ERA and 1.45 WHIP in 2009.

Joey Devine and Brad Ziegler may be the best one-two bullpen combination in the league. They combined for a 0.85 ERA in 2008, and FEIN thinks they'll do worse but still have a very good ERA. Devine is forecasted to have a 2.93 ERA, the sixth-lowest in the league, and Ziegler's projected to have a 3.41 ERA. Together, their 2009 line looks like this: 102 innings, 3.25 ERA, 8-4 record, and 35 saves.

4. Seattle Mariners

Predicted by some to win 85 games, the Mariners failed to live up to expectations, going 61-101 and becoming the first team eliminated from playoff contention. Their offense was lead by the four-headed tag team consisting of Kenji Johjima, Richie Sexson, Wladimir Balentien, and Jose Vidro. Together, they batted .222, had a .300 OBP, a .346 SLG, and a .646 OPS in over 1,200 PA.

FEIN has the Mariners duplicating their offensive failures, forecasting just four more runs scored than the 617 they had last year.

Seattle's offense wasn't the only problem last year—they allowed more than five runs per game. You could say Erik Bedard was one cause (even though he had a 3.67 ERA), because the Mariners only got 81 innings out of their pitcher for whom they traded Adam Jones and George Sherrill in the offseason. Bedard is projected to have a 3.79 ERA, and if he can pitch 170 innings, he'd be more than worth the players given up for him.

Brandon Morrow's 3.34 ERA last year allowed the Mariners to get rid of J.J. Putz, but reports are that the Mariners are trying to convert Morrow to a starter; their depth chart confirms that. But FEIN projects Morrow as a closer, and gives him a 3.70 ERA and 71 strikeouts in 70 innings for 2009. If he is a starter, he projected numbers rise to around a 3.95 ERA and 8.00 strikeouts per nine. (Note: Those numbers have not been calculated; they are estimations based on the stats of the average projected reliever and the average projected starter.)

Outfielder Ichiro Suzuki has been the model of consistency for the Mariners. He's had over 200 hits, 100 runs, 30 steals, and 700 PA with a .300 average in every year of his eight-year major league career. FEIN sees the same this year, projecting a .305 average, 102 runs, and 36 steals in 727 PA. Those numbers make him No. 34 overall and a late third-rounder in standard leagues.

Third baseman Adrian Beltre has been underrated from a fantasy standpoint in the last few years. He puts up very good numbers year in, year out but gets no love by fantasy owners. He's averaged 25 home runs, 83 runs, 88 RBI, 11 steals, and a respectful .270 average the past three years. FEIN projects 23 homers, 77 runs, 82 RBI, nine steals, and a .265 AVG, which makes him worth the No. 85 overall pick, an eighth-rounder.

AL East Projected Standings: Will New York's Offseason Spending Spree Pay Off?

Zach Fein — Sun, 15 Mar 2009 01:41:21 -0400

Note: All team projections were found using the FEINs projections. An explanation behind projected wins, losses, and other stats can be found here.

1. Boston Red Sox

Two years removed from their second World Series championship in four years, the Boston Red Sox are projected to win the most games, score the most runs, and allow the second-fewest in the major leagues. Presumably they are FEIN's World Series favorites.

This offseason, the Red Sox spent $12.5 million on Takashi Saito, Brad Penny, John Smoltz, and Rocco Baldelli. According to FEIN, that may have been the best $12.5 million spent this offseason.

FEIN projects Saito and Smoltz to combine for 10 wins and 128 strikeouts, as well as a 3.48 ERA and a 1.24 WHIP, in 137 innings. Smoltz will be out till early June, and a loaded bullpen and rotation may hinder his return to the roster—Brady Penny has the worst projected ERA of the current staff (4.84), but the Sox wouldn't send him down to the minors after just signing him to a $5 million deal; Tim Wakefield (projected ERA of 4.44) is the current No. 4 pitcher and hasn't been sent to the minors since 1995; and Justin Masterson (3.80) and Manny Delcarmen (3.51) are unlikely to give up their spots in the bullpen.

Baldelli is projected to have an OPS of .806, which would incredibly rank sixth on the team. David Ortiz's projected OPS of .968 is the highest on the Red Sox and would rank third in the league, and FEIN thinks he'll bounce back drastically from last year's disappointment.

Outfielder Jason Bay—who was acquired mid-season for Manny Ramirez—is forecasted to be 15th among batters in fantasy dollars earned in 2009, with a .853 OPS, 27 home runs, almost 100 of each runs and RBI, and 10 steals. Bay's line is one of the best in the league, but FEIN still believes that Ramirez will outperform him in almost every stat, including an OPS 70 points higher and four more home runs in 45 less plate appearances.

In the pitching staff, the Sox have a three-headed rotation headed by Josh Beckett. But FEIN isn't such a fan of Beckett, projecting a 3.89 ERA (but a 1.21 WHIP) in 169 innings. FEIN is higher on Jon Lester and Daisuke Matsuzaka (3.75 and 3.81 respectively), however, and collectively, the top three starters are projected to have a 3.81 ERA and a 1.30 WHIP in an average of 171 innings.

Closer Jonathan Papelbon is projected to have the lowest ERA in the league, at 2.69, the second-lowest WHIP, at 1.10, and the most saves, at 40. He's the anchor to the best bullpen in the league (a projected 3.71 ERA).

2. New York Yankees

The Yankees' projected stats are with Alex Rodriguez's injury factored in. I prorated A-Rod's stats to somewhere around 500 to 520 plate appearances (I forgot the exact number) and used that in their calculations. If I remember correctly, the Yankees lost about half a win with this adjustment, but they wouldn't have jumped the Red Sox either way.

After the Yankees spent $423 million in the offseason (and still came short of their 2008 payroll), the major question heading into the 2009 season is whether the Yankees' spending spree will leap them into the playoffs and maybe even the World Series.

FEIN thinks they are clearly the second-best team in the league, projected to win the second-most games, score the second-most runs, and allow the third-most runs. In each of those categories, they just so happen to trail the Red Sox by one spot.

The first two of the Yankees' free agent signings were a pair of initialed starters, CC Sabathia and A.J. Burnett. Sabathia's forecast is spectacular, even after factoring his move from the NL to the AL: a 16-9 record, 3.40 ERA, 1.19 WHIP, and 198 strikeouts in 222 innings. That line was good for the second-most projected fantasy dollars among pitchers.

Burnett's projection isn't quite as good (a 4.04 ERA), but his peripherals—8.90 strikeouts per nine, 3.38 walks per nine, 0.90 home runs per nine, and a .300 BABIP—show he could improve on his projection.

The third major signing by New York was first baseman Mark Teixeira. Teixeira's projected line of 93 runs, 31 home runs, and 112 RBI is good enough to rank him as the 14th-best hitter in fantasy, and his .904 projected OPS is almost 90 points ahead of third on the Yankees (Nick Swisher, at .817).

Alex Rodriguez is projected to have a .949 OPS, but after adjusting his stats down 20 percent due to his injury, Rodriguez's home run total is projected to be just 30, along with 85 runs and 91 RBI. Before his injury, he was a top-three fantasy pick. Now, even after the 20 percent drop, he's still a late second- or early third-round pick.

Joba Chamberlain will start this year, and FEIN thinks highly of him, with a projected 3.12 ERA, 1.21 WHIP, and 118 strikeouts in 107 innings. Those numbers may fall slightly, however, with a full year in the rotation.

3. Tampa Bay Rays

That the Rays' hopes to make the playoffs are impaired by playing in the AL East is an understatement.

Projected to win the third-most games in the AL, the Rays are also projected to miss the playoffs—even though the other two division winners in the AL are forecasted to win three games less than the Rays.

Tampa Bay is projected to score the third-most runs and allow the third-least in the AL, behind only their AL East counterparts, Boston and New York. But, unfortunately, the defending AL champions have little to no chance of making the playoffs this year.

Reigning AL Rookie of the Year Evan Longoria is projected to surpass his 2008 numbers, with a forecasted .297 average, although his RBI should fall some. His projected .907 OPS is the highest among all players younger than 26.

Pat Burrell was acquired in the offseason from the World Series winners, the Philadelphia Phillies. Burrell's had an OPS of .875 or greater in each of the past four years, but FEIN realizes that his moving from a hitter's park to a slight pitcher's park, in addition to his move from the NL to the AL. FEIN's projection for Burrell is a .812 OPS and a dreadful .248 batting average.

In the rotation, FEIN thinks the Rays' best pitcher is not James Shields, but Scott Kazmir. Kazmir's been spectacular since 2005—742 strikeouts and a 3.51 ERA in 690 innings—and he's projected to have a 3.66 ERA and 177 strikeouts in 162 innings in 2009.

Except for Kazmir, however, FEIN is down on all the Rays' starters. James Shields, who had a 3.56 ERA last year, is projected for a 4.01 ERA; Matt Garza, who had a 3.70 ERA last year, is projected to have a 4.05 ERA this year; and Andy Sonnastine, who had a 3.54 base runs ERA (an estimated ERA based on hits, home runs, and walks and how FEIN projects ERA) last year, has a 4.56 projected ERA.

As of now, the Rays' closer situation is up in air, but the Rays are leaning towards Troy Percival (projected ERA of 4.13), Dan Wheeler (3.90), and Jason Isringhausen (4.60) for the closer position. FEIN, though, believes that Grant Balfour (3.30) and Chad Bradford (3.83) are the best candidates.

4. Toronto Blue Jays

You could call 2008 a success for the Blue Jays. They allowed only 610 runs—only one team since 2004 has allowed less—had a better run differential than the Tampa Bay Rays, and, if not for their poor record in one-run games, would have gone 95-67 and tied the Red Sox.

Yet the Blue Jays don't seem to be able to replicate that success in 2009. Shaun Marcum, who had a 3.39 ERA in 151 innings last year and was projected to have a 4.29 ERA, is out for the year after Tommy John surgery. (Note: The above projected wins and losses include Marcum.) Dustin McGowan may be out till May or June; he had a 4.37 ERA last year and is projected that this year, too.

Roy Halladay is one of the five best pitchers in the league, but even he can't reproduce his 206 strikeouts and 2.78 ERA. FEIN sees a spectacular season nonetheless, with a 3.42 ERA, 1.21 WHIP, 14 wins, and 154 strikeouts in 212 innings. Fantasy owners should note that his projected line makes him just the No. 7 pitcher next year.

Another surprise for the Blue Jays last year was Jesse Litsch's 3.58 ERA in 176 innings. (Though, he did have a 3.81 ERA in 111 innings a year prior.) But Litsch's peripherals were unimpressive, and his strikeouts, walks, and home runs suggested a 4.29 ERA. FEIN projects for Litsch a respectable 4.15 ERA with a depressing 2.04 strikeout-to-walk ratio.

Toronto's offense isn't too remarkable, either. Only two hitters are projected to have an OPS over .800. One of them is Alex Rios.

Rios' OPS has declined each year since 2006 when it was .865, but FEIN forecasts a .811 OPS, higher than last year's .798. His home runs should increase from last year to 18, and his stolen bases should decline to 20.

FEIN doesn't buy the Blue Jays' 2008 season. Or their 2007 season. FEIN has them winning less than 80 for the first time since 2004.

5. Baltimore Orioles

If you wanted a two-word prediction of the Orioles' 2009 season, it could best be summed up as, "No chance." Baltimore is projected to win just 70 games—only two teams are projected to win less—and allow the most runs, one of only two teams projected to allow five or more runs per game.

But surprisingly, their offense isn't half-bad. Well, actually, they are, forecasted to score just under the league average runs per game (4.70). Their lineup is lead by Nick Markakis, who had a .897 OPS last year. Markakis' projection calls for an OPS of .858, an average of .305, 19 home runs, and 10 steals. His 91 projected runs and 84 RBI lead to his being worth the No. 37 pick in standard fantasy leagues.

An even better pick than Markakis, however, is Brian Roberts. Roberts is projected for 35 steals after 40 last year and 50 in 2007, as well as 96 runs after over 100 in both 2007 and 2008. That makes him worth the No. 33 pick, a third-rounder, in standard 12-team fantasy leagues, which makes him the No. 2 second baseman after Chase Utley.

As for pitching, the Orioles have none. And I mean that. They currently have only two pitchers projected to reach 100 innings (which is solely based on past playing time): Jeremy Guthrie and Mark Hendrickson. Hendrickson is projected to have a 5.50 ERA, but Guthrie's a little better.

Guthrie has had a 3.70-or-lower ERA in each of the past two years. But his very low BABIPs (.267 and .277) in those years shows he may very well regress some, and FEIN believes he will. FEIN projects a 4.25 ERA and a 1.34 WHIP in 175 innings, with 9.31 hits per nine innings. (He's allowed 8.39 per nine in the last two years.)

AL Central Projected Standings: Is Kansas City a Contender?

Zach Fein — Mon, 09 Mar 2009 18:21:54 -0400

Note: All team projections were found using the FEINs projections. An explanation behind projected wins, losses, and other stats can be found here.

1. Cleveland Indians

Last year, Cleveland went 24-7 in Cliff Lee's starts and 57-74 in everyone else's. Will Lee provide such an asset this year?

FEIN doesn't think so, projecting Lee with a 3.67 ERA and 1.24 WHIP in 184 innings, essentially regressing to his 2005 form.

In the bullpen, the Indians replaced closer Joe Borowski with Kerry Wood, and the move was backed up by FEIN—Wood's projected ERA is more than a full run better than Borowski's.

In middle relief are Rafael Perez and Rafael Betancourt, both of whom are projected to have an ERA below four. Betancourt blew up last year, with an ERA of 5.07 in 71 innings, but his BABIP of .323 shows that he was the product of misfortune.

On the offensive side, the Indians should hope Travis Hafner returns to his old self, and not to his 2008 performance (where his production on the field was worth minus-$3.8 million). FEIN certainly believes he can turn it around, with a projection of 20 home runs in 420 plate appearance (or 31 in the 661 PA he had in 2007) and a .895 OPS.

Grady Sizemore, with a projected .868 OPS, is one of the best hitters in the majors right now, and his projection of 26 homers and 29 stolen bases can only help that claim.

Fangraphs says that Sizemore's 33 home runs and 38 stolen bases last year earned him more than $30 million in actual value; he's a top 10 pick in fantasy leagues.

The emerging Shin-Soo Choo should also provide a boost to the Indians' lineup. Choo had a .946 OPS in 370 PA last year and proved that he was worth the playing time; FEIN gives Choo a .818 OPS for next year.

2. Detroit Tigers

Detroit's offense is one of the best in the league—on paper.

With Miguel Cabrera, Placido Polanco, Carlos Guillen, Magglio Ordonez, and Curtis Granderson, Detroit should be scoring more than five runs per game, but they are projected to score just 4.86 per game.

The Tigers do, however, sport two hitters with very high home run rates: Mike Hessman and Marcus Thames.

I've written about Hessman before; he hit 34 home runs in the minors last year, and in his major league career, Hessman has hit 42 home runs per 600 PA.

Thames, though, has hit over 40 home runs per 600 PA in the past three years, yet hasn't had over 400 PA in any of those three years. He's a solid bet for 30 or 35 homers, if he gets regular playing time, which unfortunately is unlikely.

As for pitching, FEIN doesn't believe any Tigers' pitcher will have an ERA below 4.00, with Joel Zumaya's 4.00 being the lowest. But FEIN does have Justin Verlander turning it around, with a projected 4.17 ERA, two-thirds of a point better than his 4.84 last year.

Armando Galarraga had a spectacular rookie season, going 13-7 with a 3.73 ERA in 178 innings, but his .247 BABIP and his poor peripherals (3.07 walks and 1.41 home runs per nine innings) show that he should decline in 2009. He will, according to FEIN, which projects him with a 4.27 ERA in 145 IP.

3. Minnesota Twins

Minnesota fared well last year, after losing Johan Santana, Matt Garza, and Torii Hunter, finishing just one game away from the playoffs.

Francisco Liriano was a cornerstone of the Twins' success. After a 2.16 ERA in 2006, Liriano was subjected to Tommy John surgery, and he came back like his old self.

Liriano started off rough, giving up 13 runs in 10 innings in his first three starts of the year, but after a two-month stint in the minors, he had a 2.05 ERA in his first 10 starts back in the majors before a rough outing to end the year.

FEIN projects a 3.87 ERA and 1.29 WHIP for Liriano, just slightly better numbers than his 2008 campaign.

Liriano is joined in the rotation by two youngsters who both had sub-4.00 ERAs last year: Scott Baker and Kevin Slowey. FEIN projects them each with respectable ERAs—4.11 and 4.16, respectively—and collectively, the top three spots in the Twins' rotation are projected to have a 4.05 ERA.

Joe Nathan anchors the Twins' bullpen. Nathan's projected 2.85 ERA is fourth-best in the majors, and his 1.59 ERA in the past three years is the best among all pitchers with 150 IP in that time. Along with Nathan, the Twins' bullpen is secured by Craig Breslow and Pat Neshek, with projected ERAs both under 3.60.

The Twins' offense is led by Joe Mauer and Justin Morneau. Mauer is projected by FEIN to hit .311, third-highest in the majors, with a .840 OPS. Morneau's 112 projected RBI is also third-highest, and his projected 27 home runs and .493 slugging percentage lead the Twins.

Denard Span had 70 runs and 18 stolen bases in 93 games last year, and FEIN projects him to have 59 and 15 this year in 391 plate appearances; in a full season with 650 PA, Span would be projected to have 98 runs and 25 stolen bases.

Along with that, FEIN believes Span's .818 OPS in 2008 will regress to .798, but his slugging percentage should say nearly the same (just four points lower than last year's).

4. Kansas City Royals

The Royals went 75-87 last year, but finished the year on a 13-3 run, outscoring opponents by 54 runs (3.4 runs per game). They should continue to improve this year.

New first baseman Mike Jacobs is projected to break out, with a .824 OPS and 22 home runs in 440 PA, after an OPS of .812 along with 32 home runs in Florida. His .247 batting average last year was the lowest of his career, but FEIN thinks that he'll bounce back with a projected .268 average.

FEIN also thinks Alex Gordon will have a career year. His projected .809 OPS would be the highest of his career, and his .276 average is very optimistic, considering his career high, set last year, is .260. Gordon's 2009 season could be his breaking out party.

It's also noteworthy that the immortal Kila Ka'aihue is projected to have a .820 OPS in 29 PA.

After two seasons with sub-3.70 ERAs, Zack Greinke showed that he's the Royals' ace. FEIN is somewhat pessimistic, projecting Greinke to pitch 181 innings with a 3.86 ERA, but his 1.27 WHIP would be his highest since 2004. His projected strikeouts-per-nine rate, however, of 7.99 would be his second-highest of his career.

Second in the Royals' rotation is Gil Meche. Meche was lights out, after the All-Star break last year, going 8-2 with a 3.00 ERA and 95 strikeouts in 90 innings. His projected 4.09 ERA is respectable, but it isn't good enough for someone who signed a five-year, $55 million contract.

At the end of the bullpen is Joakim Soria, who is coming off of a spectacular year. His 1.60 ERA and 0.86 WHIP proved that he's an elite closer, and FEIN projects him with a 2.95 ERA, fifth-lowest in the majors. Even on a 75-win team, Soria racked up 42 saves last year and is projected to have 34 on an 80-win team this year.

With such young talent, Kansas City has what it takes to win the AL Central for the first time since 1985, when they won the World Series.

5. Chicago White Sox

Power. That's the White Sox motto this year.

FEIN says the White Sox will lead the majors with 230 home runs and projects three guys to hit 27-plus homers: Jermaine Dye (30), Jim Thome (29), and Paul Konerko (27), as well as Carlos Quentin, who is projected to hit 19 in 404 PA (28 in 600 PA).

Those first three are all over 33 years of age, and it appears that the White Sox won't make the playoffs in a few years.

Second baseman Alexei Ramirez's rookie season was certainly a "Hello, world" campaign, but FEIN thinks his .290 batting average will fall to .277. Ramirez's batting eye needs some help, too; his projection of 23 walks in 482 PA gives him one of the worst rates in the majors.

The White Sox found a gem in pitcher John Danks, who went 12-9 with a 3.32 ERA and 159 strikeouts in 195 innings last year. FEIN realizes that that performance came one year after a 5.50 ERA in 139 innings, and it projects Danks with a 4.09 ERA and 145 strikeouts in 182 innings.

Twenty-five-year-old Gavin Floyd also showed his talent last year, posting a 3.84 ERA in over 200 innings.

But FEIN is weary of his suspect peripherals—a 2.07 K/BB ratio (less than the league average), 1.31 homers per nine, a very low .268 BABIP last year, and 4.67 runs allowed per nine innings, showing that he was lucky in his 19 unearned runs—and projects Floyd for a 4.89 ERA, which is half a run higher than the league average ERA of 4.32.

While Chicago waits on its young pitchers to develop into stars, thirty-year-old Mark Buehrle returns for his 10th season in Chicago. In the past eight years, Buehrle has double-digit wins and over 200 innings, and as FEIN projects, a 4.09 ERA in just under 200 innings (as well as 11 wins).

Buehrle certainly has value in fantasy leagues. The 4.09 ERA may not look flashy, but in 200 innings, that's enough to bring your fantasy team's ERA down a few points.

Fantasy Baseball: How To Use Strand Rate To Find Draft-Day Steals

Zach Fein — Sat, 21 Feb 2009 23:39:01 -0500

There are two statistics that can be used to verify if a pitcher's stats are genuine or a result of luck or misfortune: Batting average on balls in play and strand rate. Most know that the league average BABIP is around .300, and that a pitcher's BABIP tends to regress to that benchmark if his is above or below it.

I'd guess that, however, the majority of fantasy players do not know or have never heard of strand rate.

Strand rate, or left on base percentage (LOB%), is the percentage of baserunners allowed that do not score a run. The formula is (H + BB + HBP - R) / (H + BB + HBP - 1.4 x HR).

It may sound like a pitcher's LOB% is a function of his skill or ability to get out of jams, but most of the time, it isn't. (See graph below.)

Among all pitchers with 300 batters faced in back-to-back years, the r-squared of LOB% from one year to the next is only 0.048.

The reason why this issue is so important is that LOB% is highly correlated with ERA. Using the same group as before, the correlation of LOB% and ERA is -0.76. The higher the strand rate, the lower the ERA. You can see the significant relationship here.

I then grouped players based on their LOB%. I rounded a player's LOB% to the nearest 0.5 percent, and added up the stats of all players in each group with a LOB% between 65 percent and 80 percent (anything outside of that had less than 50 players in the group).

I found the combined ERA of each group. The correlation of the group LOB% and the group ERA? Almost a perfect relationship: minus-0.995.

The quick-and-dirty way to predict a player's ERA based on LOB% is -14.8 x LOB% + 14.5, which obviously has a 0.76 correlation with actual ERA.

(I also tested the correlation of LOB% with the difference of ERA and FIP, which is an ERA estimator that only uses strikeouts, walks, and home runs allowed to estimate ERA and assumes an average LOB%. This turned out to be -0.77, which is almost the same as LOB% with ERA. The amount of luck associated with a player's statistics correlates with LOB% slightly more than ERA itself!)

Putting two and two together, we can make these two assumptions:

The league average strand rate hovers around 72 percent. Any deviation from that number can be caused by random fluctuation or pure chance.

(Star pitchers and most relievers, however, may be able to control their strand rates—Johan Santana's career LOB% is over 77 percent, and Octavio Dotel, to name an average middle reliever, has a career LOB% of 75.5 percent.)

Justin Verlander's 2008 campaign can be described as a disappointment. So can Javier Vazquez's. But the truth lies in LOB%: Verlander's was 65.4 percent, and Vazquez's was 68.3 percent; each had a LOB% just under 75 percent in 2007, and both players' FIPs were 0.65 points less than their actual ERA. They are due for a turnaround.

As is Nate Robertson. Robertson's LOB% was 64.3 percent, seven percent less than his 2007 rate. As expected, his ERA was exceptionally high, at 6.35, but his FIP was only 4.99. His ERA should fall to near 4.76, his ERA in 2007.

Ian Snell is another example of someone negatively affected by a low LOB%. His strand rate last year was 69.3 percent, almost six points less than a year before, and his FIP was 0.85 lower than his actual ERA.

Just a year ago, he had a 3.76 ERA—why wouldn't you take a stab at him in the later rounds of your draft?

On the other end, Daisuke Matsuzaka had a strand rate over 80 percent after a 73.9 LOB% in 2007. His FIP was more than a full point higher than his ERA—expect a decline from him.

Joe Saunders' LOB% was 75.7 percent, three more percentage points than his 2007 strand rate; his FIP was 0.96 points higher than his ERA, and—get this—was a tenth of a point higher than his 2007 FIP (even though his 2008 ERA was a point lower than the year before).

Take the time to look through strand rates to find late sleepers. You may just find the next Cliff Lee—his 2007 strand rate was 62.5 percent.

The Ultimate Super Bowl XLIII Preview: Key Stats, Keys to Winning, and Much More

Zach Fein — Thu, 29 Jan 2009 23:14:09 -0500

Let me be the first to remind you that the Super Bowl is Sunday.

The Arizona Cinderella Cardinals continue their run to the championship, after a 9-7 season in which they finished the year on a 2-4 stretch. The Pittsburgh Steelers are seven-point favorites, their No. 1-ranked defense providing strong reason for their case as winners.

Here are key notes, tips, and analyses of Super Bowl XLIII. Because you can't get enough Super Bowl coverage.

Key Stats

• A new rushing attack? Arizona's yards per carry in the playoffs is 3.33, much less than their 3.60 in the regular season. The difference is that Arizona is rushing 33 times per game, 14 more than their regular season average of 19 per game.

• Since 1991, of the 12 Super Bowl games with a spread of seven or greater, favorites have won nine games and are 4-6-2 against the spread, with an average score of 29-21; the over/under in games since '91 is 4-4 when 47 or less points are predicted.

• After allowing 156 rushing yards in their last five regular season games, the Cardinals are giving up just 77 yards per game in the playoffs, more than half of that 156.

• Arizona outscored opponents by just one point and had a turnover differential of exactly zero in the regular season; in the playoffs, they are winning by 11 points per game and have a turnover differential of plus-three per game.

• The team with the better turnover differential in this year's playoffs has won eight of nine games; the San Diego Chargers beat the Indianapolis Colts after a minus-two differential, and the Philadelphia Eagles beat the Minnesota Vikings after each team had two turnovers.

• Pittsburgh wasn't much better than the Cardinals. The Steelers had just four more takeaways than turnovers all year, before reaching a total turnover differential of plus-five in the playoffs.

• Since 2000, the team with more points scored in the regular season won three of seven games; the team will less points allowed in the regular season won five of seven games. (In two separate games, both teams had the same number of points scored or points allowed in the regular season.)

• The Cardinals outscored the Steelers by five points per game, and the Steelers allowed 12.7 less points per game than the Cardinals.

Key Matchups

When Arizona has the ball...

Kurt Warner vs. Troy Polamalu

The pundits are saying that it is essential for the Steelers to blitz, and blitz often. Yet, Warner has been the best against the blitz on the year, throwing for 1,548 yards on 197 attempts (127 completions) with 14 touchdowns and four interceptions, a passer rating of 103.8.

Warner knows when and how to get the ball away during a blitz: According to this website, Warner went 9-for-10 for 97 yards with less than two seconds to throw, and 11-for-19 for 182 yards and two sacks in all other situations.

The Steelers cannot blitz too often; Warner knows what to do. Instead, they must drop back into coverage more often than not and be aware of when to and from where to blitz. Pittsburgh needs to let Troy Polamalu take care of the short, middle routes, and check downs (Warner was 20-for-21 when throwing the ball 10 yards or less and one-for-eight in all other attempts against Philadelphia).

With Polamalu covering the short routes, the Steelers have their choice of when to blitz him. Polamalu will be near the line on each and every play, his distance to Warner will not be as much as if he were in his normal position, and Warner will have a significantly tougher time deciphering when Polamalu will blitz. When he is rushing the passer, Pittsburgh can single-cover Larry Fitzgerald and put another man in the middle or in position to cover the running back.

In this defensive formation, Warner will not have time to throw to Fitzgerald, who is single-covered, and must throw to Anquan Boldin in the middle or Tim Hightower or Edgerrin James in the flat, and each of them will be covered by the man taking over for Polamalu.

Using Polamalu as bait for Warner to throw to the flat could provide Pittsburgh with key stops, especially if the Cardinals are faced with third-and-short.

When Pittsburgh has the ball...

Heath Miller vs. Adrian Wilson

The will-he-or-won't-he with Hines Ward may be getting more coverage than the Super Bowl itself.

I'm kidding, of course, but Ward's injury has been as scrutinized as Tom Brady's walking boot last year. Even Ward says his injury is a mystery to him ("I've never really had to deal with this specific injury"), so don't expect him to be his old self this Sunday.

Santionio Holmes will continue in his roll of the speedy, break-a-long-one receiver, so in Ward's place as possession wide out is tight end Heath Miller.

In his last four games, Miller's averaged five catches for 56 yards, and he also caught a touchdown in the Divisional round against San Diego. Miller has caught 73 percent of the 74 passes thrown to him on the year, more than guys such as Tony Gonzalez, Antonio Gates, Jason Witten, and Dallas Clark, among others.

In comparison, Holmes and Nate Washington have caught 46 percent and 52 percent of their targets, respectively, and Ward has a catch percentage of 66 on the year.
Miller is as big of a possession guy as anyone. It's Adrian Wilson's job to cover him.

Last week, Wilson had seven tackles, two sacks, and a forced fumble, but the Cardinals allowed Eagles' tight end Brent Celek to catch 10 balls for 83 yards and two touchdowns.

Arizona is addicted to blitzing Wilson and leaving the middle open, and that hole was exploited last week. The Steelers have had two weeks to plan against the safety blitz, and if the Cardinals don't change their ways, they'll be beat down the middle by Miller all day.

Yes, Arizona can have Wilson blitzing and have free safety Antrel Rolle cover Miller, but in that scenario, no one is back guarding the deep ball, a problem worsened by Holmes' and Washington's speed.

In the end, Wilson must be able to play defense and cover Miller. Thirty of Miller's 48 catches have come on passes thrown less than 10 yards ahead of the line of scrimmage, and Wilson must be aware of this. If all goes as planned, Arizona can establish Wilson as the Steelers should do with Polamalu, and that is let him cover the short routes and blitz him unexpectedly.

Keys to Winning

For Arizona...

1. Establish a ground game

Arizona is 6-1 when rushing for more than 100 yards and 10-1 when rushing for more than 60 yards. They are 7-1 when Edgerrin James rushes 10 or more times.
Obviously, the Cards must mix up their play calling in order to win (they are also 7-1 when rushing for more than 40 percent of the time, and 11-2 at more than 30 percent).

Pittsburgh's run defense is regularly called tough and stout, but as the season wore on, their play went down. The Steelers' opponents gained, on average, just 66.5 rushing yards in the first 12 weeks, but have averaged 91.4 since then.

Arizona must start out the game pounding the ball as they did in the Wild-card round against the Atlanta Falcons. Doing so will slow down the Pittsburgh pass rush and their blitz.

2. Pressure Ben Roethlisberger

Just how important is Roethlisberger to Pittsburgh's success? The Steelers are 0-3 when Big Ben throws more than one interception, and 13-1 when he throws one or less.

That's the Steelers' biggest weakness, and everyone knows it.

Roethlisberger had a 53.6 completion percentage and a quarterback rating of 70.3 against the blitz, and 63.5 completions percentage and a 85.6 rating against all other defenses. He was sacked in 28 of 196 drop backs (attempts plus sacks plus runs) against the blitz, a 14.2 percent sack rate, but in only 18 of 353 drop backs in all other situations, a five-percent sack rate.

Arizona must blitz Roethlisberger, but not largely on safety blitz as I asserted above; the Steelers realize the Cardinals tendency to blitz Adrian Wilson in blitz situations, so the Cardinals will have to rely on their front seven, lead by Karlos Dansby and Bertrand Berry, to put pressure on Roethlisberger and force a turnover or interception.

3. Rely on short, quick, West-Coast-ish passes to maintain a quick pace

Larry Fitzgerald has a catch of 40 yards or more in each of the past five games, but with Troy Polamalu lurking around, you can never assume a long reception.

On their first possession, Arizona must play as if they are in a two-minute offense, throwing quick, 10-yard passes (with runs mixed in). The Cardinals must set an up-tempo pace. This will make the Steelers unable to put pressure on Warner, knowing that he'd get the ball off before a blitzer arrives.

They know that the Steelers cannot compete with them on the offensive side of the ball, and Arizona must hope for a shootout—that is their only chance of competing in this game.

If it turns into an old-school, time-of-possession battle that is the Steelers' specialty, Arizona can say goodbye to their Super Bowl chances.

For Pittsburgh...

1. Make Anquan Boldin beat them
Pittsburgh must double-team Larry Fitzgerald and keep him under 100 yards. They must make Anquan Boldin beat them—and, more specifically, Troy Polamalu—down the middle.

Boldin has not performed well against tough competition; he's averaged only 54.4 yards per game against teams in the top half of the league in pass defense (and 96.9 against all other teams). Granted, such conditions only included five games, but even K.C. Joyner agreed that he did poorly against top teams.

Boldin's specialty is a six-seven yard pass to the middle or left side of the field that he runs for eight or nine yards. (See his situational splits with yards-after-catch numbers.)

And ironically, that's Polamalu's, too. If the Steelers let Polamalu do what I suggested above—defending the short crossing routes down the middle—Boldin will have nowhere to go. Double-teaming Fitzgerald and keeping Boldin in check can go a long way for the Steelers.

2. Start off the game with many runs

The Steelers must run Willie Parker into the ground at the beginning of the game. Why?

Adrian Wilson won't be able to resist the urge to come in and try to stop the run. If Wilson sees the Steelers running on 10 of their first 12 plays, he and the Cardinals defense will be ready for the run and will not expect the pass.

Ben Roethlisberger will then have one-on-one coverage deep for either Santonio Holmes or Nate Washington. The Steelers could even run a flea-flicker in this situation, replicating the Cardinals' game plan against the Falcons. Moreover, there will be space for Heath Miller to get open and thread the coverage.

The Steelers will want to run this game plan not just at the start of the game, but the entire game. They must keep the Arizona offense off the field and control the ball for the majority of the game.

3. Force Kurt Warner to make bad decisions

I noted earlier that Warner was the best quarterback against the blitz all year, yet the Steelers must try to pressure him and force turnovers.

By blitzing Warner not too often, the Steelers will cause Warner to make quick decision that may lead to an interception or a fumble. On a blitz, Fitzgerald will have to be single-covered to make up for the lost man. Warner will either try to look for Fitzgerald and force it, or throw it down the middle to Boldin or to the flat to a running back.

Pittsburgh can only hope for a turnover in this situation. But most importantly, they must do what they have done all year long on defense. They were No. 1 in almost every defensive category, so they must not change their ways for one game.

Key Selection: Who Will Win?

Arizona should control the tempo of the game and make the score go into the 30s. The Steelers will not be able to stop Larry Fitzgerald, who will once again gain over 100 yards receiving.

The main aspect of this game will be the turnover battle, as it has been for virtually every game of the playoffs thus far.

Pittsburgh is 13-1 in games with a turnover differential better than or equal to zero, and 1-3 in all other games; the Cardinals are—get this—11-0 in such games, and 1-7 in all other matchups.

Which team will win the turnover battle? The winner, of course.

And the winner will be the Arizona Cardinals, by a score of 31-27.

Cracking the Code: How to Calculate Hollinger's PER Without All the Mess

Zach Fein — Mon, 19 Jan 2009 21:59:55 -0500

John Hollinger's Player Efficiency Rating (PER) is a one-number measure of a player's per-minute productivity, and it may be one of the best basketball stats out there.

The PER is computed using a detailed, complex formula that, in essence, adds for positive stats such as rebounds, assists, and steals, and subtracts for negative stats such as shots missed or turnovers.

But the formula is too complicated. I like recreating stats by myself (as if I made it), but I could never come around to PER—who would want to go through that formula?

I decided to calculate PER—just for once—and find an easier way to compute it, using only linear weights (or multiplying a player's stats by different weights and adding them up).

Naturally, it took me three times before I finally got down the correct formula in Excel.

(My "player" was the league average player's stats prorated to 82 games, his "team's" stats were the average of all 30 teams last year, and the "league" stats were last year's league stats.)

I found the weight of a stat by adding one of that stat to the player's season totals (but not to the team or league), and finding the affect of that addition on the PER.

I was shocked to find that the PER—before adjusting it to make the league average equal 15.00—is around 0.28, so the effects looked small, although in reality they weren't.

The following table shows these effects of all stats used in the PER formula.

Stat	Weight	Approx.
FGM	1.591	5
Stl	0.998	3.15
3PTM	0.958	3
FTM	0.868	2.75
Blk	0.726	2.3
Off. Reb	0.726	2.3
Ast	0.642	2
Def. Reb	0.272	0.85
Foul	-0.318	-1
FT Miss	-0.372	-1.15
FG Miss	-0.726	-2.3
TO	-0.998	-3.15

The column showing "Weight" is actual weight multiplied by 1,880 (explained below), and the "Approx." column shows the approximate weight if a field goal made was set to five.

So, what's up with the 1,880? That is the (approximate) adjustment used to set the weighting of the league average of above stats equal to the league average PER. In other words, using those weights to any one player will recreate his PER before you set the league average PER to 15.00.

We're almost there. The next step is the problem of making the league average PER 15.00. We can go through and actually calculate it—but that would be ironic, wouldn't it? We want to make the simplest formula to recreate PER, so we need a shortcut for this step.

Luckily, the league average hasn't changed the past few years—each adjustment has been around 54, with no real difference between them. So the final step is to multiply the weights by 54. You end up with these, the final weights of each stat.

Stat	Weight
FGM	85.910
Stl	53.897
3PTM	51.757
FTM	46.845
Blk	39.190
Off. Reb	39.190
Ast	34.677
Def. Reb	14.707
Foul	-17.174
FT Miss	-20.091
FG Miss	-39.190
TO	-53.897

Notice I've left out pace adjustments until now. The reason is that, although they are quite important in both the PER formula and evaluating players in different schemes, they are almost as tough as PER to calculate. You can find them on various Web sites, but that wouldn't be a simple calculation, would it?

Thus, the formula for the linear weights PER is

[ FGM x 85.910

+ Steals x 53.897

+ 3PTM x 51.757

+ FTM x 46.845

+ Blocks x 39.190

+ Offensive_Reb x 39.190

+ Assists x 34.677

+ Defensive_Reb x 14.707

- Foul x 17.174

- FT_Miss x 20.091

- FG_Miss x 39.190

- TO x 53.897 ]

x (1 / Minutes).

How does that formula match up with actual PER? Here are the PERs and linear weights PERs of the top 10 leaders in actual PERs.

Player	actPER	lwtsPER
LeBron James	32.09	30.73
Chris Paul	30.20	28.30
Dwyane Wade	28.87	28.06
Kobe Bryant	24.95	25.70
Dwight Howard	25.87	25.67
Carlos Boozer	24.77	25.34
Dirk Nowitzki	24.01	23.75
Tim Duncan	24.46	23.44
Devin Harris	23.63	22.95
Brandon Roy	24.60	22.90

Every one of the top 10 in actual PER were in the top 10 of the linear weights PER save for Devin Harris, who was beat by a hair by Shaquille O'Neal, whose lwtsPER was 23.65. Chris Paul and Brandon Roy have the biggest gaps in their PERs, because their teams have the slowest and second-slowest paces in the league, so they would have greatly benefited from the pace adjustment.

The lwtsPER is almost a perfect match of the actual PER, except that the lwtsPER underestimates by a slight bit (the average of the actual PER above is 26.35, and the average of the lwtsPER is 25.68). The league average of the lwtsPER is 14.98, which could be one reason as to why.

Looking to calculate PER, but don't want to go through all the mumbo jumbo?

Try the linear weights PER.

NFL Win Probabilities: Extra Point or Go for Two?

Zach Fein — Sat, 17 Jan 2009 17:14:15 -0500

One of my favorite Web sites is AdvancedNFLStats.com.

It has live, up-to-the-second win probabilities as games go on. For example, the Chargers' probability of beating the Steelers dropped greatly after Willie Parker's touchdown before the half.

Now, Brian Burke, who operates the site, has added a win probability calculator, where you can see the chances of winning given certain circumstances—the score differential, the time left in the game, the field position, and the down and distance (though it doesn't include timeouts). Check it out here.

Using this tool, I determined whether teams should kick the extra point or go for the two-point conversion during various late-game situations.

Let's take a circumstance under which there may be a question of what to do after scoring.

You're down by eight before a touchdown, with seven minutes left in the fourth quarter. Do you kick the extra point or go for two?

If you make the extra point (which we'll say is good every time), you're down one, with seven minutes left, and the opponent gets the ball on their own 20 (assuming a touchback).

In that case, your win probability is 42 percent.

If you make the two-point conversion, you're tied, with the circumstances above.

Your win probability is now 47 percent. That's if you make the two-point conversion.

Given that about 45 percent of all tries are good, we have to see what would happen with the other 55 percent.

Your win probability if you miss the conversion is now 37 percent. Thus, we can say that

WP(attempting the two) = (0.45 * 0.47) + (0.55 * 0.37) = 0.2115 + 0.2035 = 41.5 percent.

So, you have a 42 percent chance of winning after making the extra point, and a 41.5 percent chance of winning if you attempt a two-point conversion.

The probabilities are almost the same, so in this case, attempting the extra point or two-point try is dependent on how your defense has performed in the game.

Could you stop them and get one more possession? Kick the extra point.

Is your defense horrendous and you have no chance of getting back the ball? Go for two.

Now let's see what happens if we change the time left from seven minutes to five.

In this case, you have only a 32 percent chance of winning if you kick the extra point. Your win probability is 55 percent is you make the two, but only 29 percent if you miss.

Your chance of winning if you attempt the two-point conversion would be 40.7 percent, which is significantly higher than the 32 percent chance if you make the extra point.

The graph below shows the chance of winning after kicking the extra point or going for all 30-second increments from two minutes left to eight minutes left.

The break-even point would be seven minutes left, where the win probability is virtually the same if you go for two or make the extra point. With less than seven minutes on the clock, going for two should be the choice over kicking the extra point.

Note, however, that because the calculator does not include timeouts, you should add 30 seconds (the amount of time saved with a TO) to the time left in the game. So if a team has three timeouts and there's five minutes left, you should look at the 6:30 mark, which still says to go for two.

NFL Championship Round Preview: Two Birds in the Super Bowl?

Zach Fein — Fri, 16 Jan 2009 16:52:25 -0500

The Divisional round further proved that this could be the weirdest year in the NFL in a long time.

In May, Sports Illustrated had a "Bizarro Baseball" issue, in which Tom Verducci looked at the "mysteries of the '08 season."

But the 2008 MLB season doesn't come close to the 2008 NFL season. To wit:

The Tennessee Titans won their first 10 games, with Kerry Collins at quarterback—then promptly lost four of their next seven.
Two rookie quarterbacks with rookie head coaches each won 11 games and made the playoffs. One has gone farther than any other rookie QB in the playoffs.
Seven of ESPN.com's preseason power rankings missed the playoffs; of the five that did, three lost in their first game.
Average rank of the four remaining teams: 14.5.
Average rank of the 12 playoff teams: 14.8.
Tom Brady threw for 50 touchdowns last year, was injured seven minutes into the first week of the season...and the Patriots went 11-5 with Matt Cassel at QB.
Home teams had a 2.5-point home-field advantage, after a 2.9-point HFA last year.
Home teams went 111-138-6 against the spread this year, compared to 128-120-8 last year.

So to use historic trends like I did last week in order to pick the games? Puh-lease.

Here are my Championship round picks, based on absolutely nothing.

No. 6 Baltimore Ravens at No. 2 Pittsburgh Steelers (-6)

According the STATS, Inc., there have been 18 times since 1970 in which teams played each other three times and one team swept the regular season matchup; those teams won only 11 of those 18 games. The most recent of these matchups came last year: The Giants beat the Cowboys in the playoffs after losing both regular season matchups.

Will the overall trend continue, or will the Ravens beat the Steelers, as the Giants did last year?

The key to this game is the turnover battle. In the playoffs thus far, the team with the better turnover margin has won six of the seven games. (In Philadelphia's opening round game against Minnesota, both teams gave up two turnovers.)

The Ravens rank third in the league in turnover differential, and in their first two playoff games (against the two teams ranked higher in that stat), the Ravens forced eight turnovers and gave the ball away just once. After a minus four differential in their first seven games, the Ravens' turnover differential increased to a stunning plus 24 in their last 11 games.

On the other end, the Steelers rank just 11th in the league in TO differential. In their last 11 games, the Steelers have turned the ball over 19 times, which is two less than the Ravens' total all year.

Pittsburgh has been relying on its running game as of late, rushing 25 more times than they've passed in the past two games. But the Steelers won't be able to get by the Ravens with that offense: In their last nine meetings, the Steelers are averaging under 70 rushing yards per game.

Should the Ravens make the Steelers one-dimensional, Pittsburgh's chances of winning would go down dramatically. The Steelers have had under 100 rushing yards in eight games this year and are only 4-4 in those games; as well, their average of 16.6 points per game in those eight games in 10 less than their average of 26.8 in games in which they run for more than 100 yards.

I look for the Ravens to win the turnover margin and limit Willie Parker. I'm 4-4 in the playoffs so far, but I did pick the Ravens to reach the Super Bowl.

The pick: Baltimore 20, Pittsburgh 14

No. 6 Philadelphia Eagles (-4) at No. 4 Arizona Cardinals

Other than that 70-yard screen pass against the Vikings, Brian Westbrook's been terrible lately. He's going to play this week, but how effective will he be? The Cardinals are giving up 68 rushing yards per game in the playoffs, and Westbrook is averaging 44 rushing yards and 2.8 yards per attempt in his last five games.

Arizona must make the Eagles one-dimensional—which, honestly, isn't hard to do—and make Donovan McNabb beat them through the air.

When the Cards allow less than 100 rushing yards, they are 8-2; when the Eagles rush for more than 100 yards, they are 7-2. Something's gotta give.

The turnover battle may be even more important in this game than in the AFC Championship. For example, the Cardinals are 9-0 when they have three or more takeaways and 2-7 in all other games (that stat also applies to games where the Cardinals have a positive turnover differential); the Eagles are 6-1 with three or more takeaways and 5-5-1 in all other games.

Arizona must also give Edgerrin James the rock and pound the ball on the ground. The Cards are 6-1 when Edge has more than 10 carries or 50 yards rushing, and are an even better 9-1 when rushing for 60 yards.

Something to look for: The Eagles' defensive coordinator, Jim Johnson, loves to blitz. Arizona lost TE Stephen Spach due to injury; he had three catches for 34 yards in the Wild-card round. Without him, the Cards have no reliable receiving option in the middle of the field.

Why is that important? When the blitz is coming, Warner will have to throw up the ball and hope Larry Fitzgerald catches it (though that hasn't been a problem), instead of throwing a 15-yard curl route to Spach behind the linebackers, as he did at the end of the Atlanta game.

The pick: Arizona 28, Philadelphia 24