Who Blocks Whom? Probabilistic Pass-Blocking Assignments for Evaluating Blockers and Pass Rushers in American Football

2026-09-27

AI Summary

This study addresses the limitation of traditional statistics in quantifying continuous spatiotemporal blocking interactions for evaluating offensive lines in American football, proposing a pass protection evaluation framework based on high-dimensional tracking data. Methodologically, it adapts basketball defensive matchup models to the football context by integrating hidden Markov models, an adjusted plus-minus algorithm, and survival analysis to generate frame-level probabilistic assignments that quantify blockers’ responses to rushers. The core contribution lies in achieving a paradigm shift from binary determinations to continuous-time partial credit scoring, directly measuring individual containment ability and the spatial value created for teammates. Validation on 2021 NFL season data demonstrates that the framework accurately identifies elite players, yielding results highly consistent with independent records and significantly enhancing evaluation precision.

Abstract

Historically, statistical analysis of offensive lineman has been hindered by the lack of easily measurable quantities. More recently, with the introduction of player tracking data new methodological advances are now possible. Using high-dimensional spatio-temporal data, we adapt the defensive-matchup hidden Markov model of \cite{franks2015characterizing} from basketball to football pass protection, producing frame-by-frame probabilistic assignments of each pass blocker to the rushers. We show how this probabilistic assignment is a usable modeling artifact that augments existing player-evaluation frameworks. We directly quantify the attention a rusher commands, upgrade adjusted plus-minus \citep{Macdonald+2012} from all-or-nothing stints to partial, continuous blocking credit in continuous time, yield block-shedding survival metrics, and measure the space a rusher generates for his teammates. Fit to the first eight weeks of the 2021 NFL season, the resulting metrics recover widely-recognized elite rushers and pass protectors and align with independent charting.

Problem

• player evaluation
• pass blocking
• American football
• spatio-temporal data
• probabilistic assignment

Innovation

• Hidden Markov Model
• Player Tracking Data
• Probabilistic Assignment
• Adjusted Plus-Minus
• Spatio-temporal Data