American Football Scores: Using Partially Regularized Ordinal Regression to Adjust for Strength of Opponents, Within-Team Complementary Unit Performance

American football is unique in that offensive and defensive units typically consist of separate players who don’t share the field simultaneously, which tempts one to evaluate them independently. However, a team’s offensive and defensive performances often complement each other. For instance, turnovers forced by the defense can create easier scoring opportunities for the offense. Using drive-by-drive data from 2014 to 2020 Division-I college football (Football Bowl Subdivision, FBS) and 2009–2017 National Football League (NFL) seasons, we identify complementary football features that impact scoring the most. We employ regularized ordinal regression with an elastic penalty, enabling variable selection and partially relaxing the proportional odds assumption. Moreover, given the importance of accounting for strength of the opposition, we incorporate unpenalized components to ensure full adjustment for strength of schedule. For residual diagnostics of our ordinal regression models we apply the surrogate approach, creatively extending its use to nonproportional odds models. We then adjust each team’s offensive (defensive) performance to project it onto a league-average complementary unit, showcasing the effects of these adjustments on team scoring. Lastly, we evaluate the out-of-sample prediction performance of our selected model, highlighting improvements gained from incorporating complementary football features alongside strength-of-schedule adjustments.