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

(Photo by Elsa/Getty Images)

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.

You're currently logged into Facebook

Do you want to share your Bleacher Report activity on Facebook?