Modeling NFL Team Wins using Multiple Linear Regression to Determine What Makes Teams Win

By Max Mulitz


Brian Burke was the first to use multiple linear regression to do exploratory analysis on what makes teams win, however he hasn’t revisited model recently and the game has changed a little bit in the past 5 or 6 years, so I thought I would take a crack at it.

I looked at team data from the 2011 through 2015 regular seasons. I got my data from Pro Football Reference, except defensive forced fumble data, which is from ESPN though I did have to convert some gross stats to rate stats (fumbles divided by total plays to get fumble rate, for instance). Below are my variables and their significance:

Coefficients: Estimate Std. Error t-value Pr(>|t|)
(Intercept) 12.1054 3.105 0.000147 ***
D Rushing Yards/Attempt
0.34397 0.006679 **
D Passing Yards/Attempt -1.87068 0.23377 3.37E-13 ***
D Sack Rate 0.23648 0.11047 0.033947 *
D Interception Rate 0.85697 0.16471 6.48E-07 ***
O Rushing Yards/Attempt 0.71702 0.30678 0.020777 *
O Sack Rate -0.28892 0.0792 0.000366 ***
O Passing Yards/Attempt 1.65931 0.21368 1.28E-12 ***
O Interception Rate -0.82088 0.16853 2.84E-06 ***
O Fumble Rate -1.14288 0.26673 3.29E-05 ***
Team Penalty Rate -0.25935 0.07812 0.001136 **
Opponent Penalty Rate 0.3174 0.10658 0.003394 **
D Forced Fumble Rate 0.2584 0.28161 0.360335

My adjusted R squared was .745, almost identical to what Burke found almost 10 years ago. All “Rate” stats are based on per 100 plays, so Team Penalty Rate is the number of penalties the team took per 100 plays. Don’t worry about the intercept term. The way to read the estimates is that, holding everything else fixed, a team that increased its rushing yards/attempt by 1 would expect to win 0.71702 more games. If a team’s defense increased it’s sack rate from 6/100 plays to 8/100 plays, holding all other stats constant, it could expect to win 2 * 0.23648 = ~.47 more games. Every variable was statistically significant at P=.05 except Defensive Forced Fumble Rate.




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