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

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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.

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