[2603.25901] Decoding Defensive Coverage Responsibilities in American Football Using Factorized Attention Based Transformer Models

Computer Science > Machine Learning arXiv:2603.25901 (cs) [Submitted on 26 Mar 2026] Title:Decoding Defensive Coverage Responsibilities in American Football Using Factorized Attention Based Transformer Models Authors:Kevin Song, Evan Diewald, Ornob Siddiquee, Chris Boomhower, Keegan Abdoo, Mike Band, Amy Lee View a PDF of the paper titled Decoding Defensive Coverage Responsibilities in American Football Using Factorized Attention Based Transformer Models, by Kevin Song and 6 other authors View PDF HTML (experimental) Abstract:Defensive coverage schemes in the National Football League (NFL) represent complex tactical patterns requiring coordinated assignments among defenders who must react dynamically to the offense's passing concept. This paper presents a factorized attention-based transformer model applied to NFL multi-agent play tracking data to predict individual coverage assignments, receiver-defender matchups, and the targeted defender on every pass play. Unlike previous approaches that focus on post-hoc coverage classification at the team level, our model enables predictive modeling of individual player assignments and matchup dynamics throughout the play. The factorized attention mechanism separates temporal and agent dimensions, allowing independent modeling of player movement patterns and inter-player relationships. Trained on randomly truncated trajectories, the model generates frame-by-frame predictions that capture how defensive responsibilities evolve from pre...