Baseball isn't played on spreadsheets, but to an extent, it can be evaluated on them.
Free agency is just one aspect in which knowing the numbers can be a way for teams to make better decisions and even find the occasional value signing.
As the sport has grown statistics-savvy over the past decade or so, finding said values has become challenging. It can—and does—happen, though. And in free agency, while getting great value (that is, better-than-expected production per dollar) is important, so is simply getting a quality return on investment (that is, not whiffing on a big-money deal).
That's where it pays for teams to know some alternative numbers, statistics and metrics that are being used in evaluations.
Otherwise, teams are just, well, paying.
To be sure, because of the many, many ways to splice and parse statistics, there are more complex metrics than the ones that follow. But in the interest of time, space and decision-making, here are six stats that should be among the most prioritized when evaluating free agents.
For each, we'll point out the 2013 leaders among players on the open market. And because this is also about the money, we'll highlight which of them might be the best value.
For Position Players
ISO (Isolated Power)
Power is at a premium. You might've noticed over the past two or three seasons that the balance in baseball has shifted from offense to pitching. Gone are the days of players bashing 50 and even 60 home runs. Now, just 30 homers is an acceptably attractive number.
As a measure of a hitter's raw power or ability to smack extra-base hits, ISO is a good number to look at for a team in search of a little more oomph.
wOBA (Weighted On-Base Average)
It was all the rage about a decade ago, but on-base percentage is old hat by now. (It's still a very suitable, sturdy hat, though.)
The new-age OBP is wOBA, because it's scaled to look like OBP except it's a much more all-inclusive offensive metric.
In short, wOBA accounts for all the various forms of a player's production at the plate, from hit type (single, double, triple, home run) to walk to hit by pitch.
wRC+ (Weighted Runs Created Plus)
This one puts all aspects of a player's offensive contributions into the context of runs created compared to league average, which is 100. As an example, if a player's wRC+ was 112 this past season, that means he created 12 percent more runs than league average.
Better yet, this stat is park- and league-adjusted, meaning players can be compared across teams, leagues, parks and eras.
FIP-/xFIP- (Fielding Independent Pitching Minus)
OK, so I may be cheating a bit by lumping two stats into one, but there's a reason for it.
If you're unfamiliar with plain old FIP, here's the quick rundown: It indicates how well a hurler fares in the traditional outcomes of the pitcher-hitter duel that the pitcher has the most control over—strikeouts, walks and home runs. FIP is scaled to look like ERA.
So FIP- and xFIP-, then, are an easy way to tell how well a pitcher performed compared to league average, which is scaled to 100, and lower is better. As an example, if a pitcher has an FIP- of 78, that means his FIP was 22 percent better than average.
The other good thing about these two? They factor in league and park adjustments, so comparisons are free and easy. That's handy dandy for trying to figure out how a starter in a pitcher's park might fit in a hitter's park.
The one difference between FIP- and xFIP- is that the latter is based off xFIP, which normalizes home run rate.
The key takeaway is that while ERA alone is descriptive, FIP- and xFIP- are much more predictive. That's extremely useful in the case of free agency, because any team that signs a hurler doesn't want to know how he has pitched as much as they want to get an idea of how he will pitch.
|PLAYER||FIP-||PLAYER||xFIP-||MLB AVERAGE (For both)|
|A.J. Burnett||76||A.J. Burnett||77||100|
|Bartolo Colon||84||Scott Kazmir||85|
|Hiroki Kuroda||88||Hiroki Kuroda||91|
|Ubaldo Jimenez||90||Roberto Hernandez||91|
|Ricky Nolasco||90||Ubaldo Jimenez||91|
K% and BB% (Strikeout Percentage and Walk Percentage)
Again, here's two metrics roped into the same bundle, but that's because they work best when utilized together to get a full sense of a pitcher's ability to strike out batters while limiting walks.
K% and BB% are very similar to K/9 and BB/9 (strikeouts and walks per nine), except that instead of the results being on a per-inning basis, they're on a per-batter one.
The reason this is better? A hurler who is giving up hits and walks or pitching in front of a poor defense is essentially getting more opportunities to strike out hitters, because of the very fact that he's not getting them out in other manners.
Such a pitcher could still post a high K/9, but his K%—again, the percentage of all batters faced that he strikes out—would be lower, thus revealing how much less frequently he is whiffing the batters he does face.
Same goes for BB% compared to BB/9, and as we know, putting as few men on base freely tends to be a handy skill for pitchers.
|PLAYER||K%||MLB AVERAGE||PLAYER||BB%||MLB AVERAGE|
|A.J. Burnett||26.1%||19.9%||Bartolo Colon||3.8%||7.9%|
|Ubaldo Jimenez||25.0%||Bronson Arroyo||4.1%|
|Scott Kazmir||24.1%||Dan Haren||4.3%|
|Dan Haren||21.1%||Hiroki Kuroda||5.2%|
|Matt Garza||20.9%||Chris Capuano||5.3%|
GB% (Ground-Ball Percentage)
While strikeouts remain the most preferable form of out-getting a pitcher can achieve, ground balls are, for the most part, preferable to fly balls.
The reason, of course, is that even though flies turn into hits less often than grounders do, they are much more damaging (in the form of extra bases) when they do.
Ground balls, on the other hand, are very often either an out or a single.
The intention in all of this is to show how a few key statistics that are a layer or two below the mainstream (i.e., beyond batting average and runs scored, ERA and WHIP) can be rather useful when it comes to evaluating players on the open market.
By listing the top free agents in each metric, as well as highlighting the one who might be the best value (i.e., production per dollar) based on 2013 output, the goal is to open some eyes to show how the lesser names can actually, in some unconventional aspects, stack up against the bigger ones.
All statistics come from FanGraphs.