SPRING 2026

AUTHORS

Jonathan Pipping-Gamón, University of Pennsylvania
Maximilian Gebauer, University of Pennsylvania
Victoria Lee, University of Pennsylvania
Kenny Watts, University of Pennsylvania
Abraham J. Wyner, University of Pennsylvania

ABSTRACT

Evaluating offensive linemen and pass rushers at the player level is difficult because observable outcomes are sparse, opponent dependent, and strongly shaped by surrounding context.

Using 2021 regular-season Hudl tracking data, we construct a blocker–rusher interaction dataset and estimate two ridge-regularized Bradley–Terry paired-comparison models: a binary win/loss model aligned with the 2.5-second pass block win-rate definition and a four-class severity model over loss/win/hit/sack, with both models incorporating a double-team indicator. The final dataset contains 153,138 interactions across 33,283 pass plays in 266 games. On an ordered 80/20 holdout split (ntest = 30,628), both models improve on global baselines and modestly outperform stronger matchup baselines under log-loss evaluation, corresponding to relative log-loss reductions of about 0.24% to 1.21%.

Game-level bootstrap resampling indicates that these gains are most stable for the win model and for the severity model relative to the global baseline, while the severity-versus-matchup comparison remains directionally positive but less certain. External comparison to 2021 AP All-Pro selections provides additional face validation on the learned rankings, with the severity model showing the strongest alignment to expert recognition. Overall, ridge-regularized Bradley–Terry models provide an interpretable opponent-adjusted framework for evaluating NFL pass protection and pass rush at the interaction level.