The original goal of building a model for football forecasting was to weigh the importance of each facet of the game. In particular, I wanted to know if offense was more important than defense or if defense really did win championships.

“What’s more important?” is a tricky question. You would think that in a symmetric zero-sum sport like football, offense and defense are equally important to winning. For every yard or point gained by an offensive squad, there is a defensive squad that has surrendered an equal yard or point.

And that’s true, but only at the game level. When we aggregate squad performance by team, we find that the total number of yards or points gained and surrendered is indeed symmetric, but the distribution of offenses is wider. In other words, there are more really good and really bad offenses, and there are more average defenses.

We can use advanced metrics of team performance like Expected Points Added (EPA) and Win Probability Added (WPA) to measure the spread in performance. The standard deviation of a distribution tells us how wide a statistic is distributed — Is the bell curve wide or narrow?