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

• URL: http://arxiv.org/abs/2603.25901v1
• Date: Thu, 26 Mar 2026 20:43:30 GMT
• Title: Decoding Defensive Coverage Responsibilities in American Football Using Factorized Attention Based Transformer Models
• Abstract summary: This paper presents a factorized attention-based transformer model applied to NFL multi-agent play tracking data.<n>Our model enables predictive modeling of individual player assignments and matchup dynamics throughout the play.<n>Our models achieve approximately 89%+ accuracy for all tasks, with true accuracy potentially higher given annotation ambiguity in the ground truth labels.
• Score: 0.22485007639406518
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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-snap through pass arrival. Our models achieve approximately 89\%+ accuracy for all tasks, with true accuracy potentially higher given annotation ambiguity in the ground truth labels. These outputs also enable novel derivative metrics, including disguise rate and double coverage rate, which enable enhanced storytelling in TV broadcasts as well as provide actionable insights for team strategy development and player evaluation.

Related papers

• TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics [60.9005633936196]
  TacticGen is a generative model for adaptable and scalable tactic generation.<n>Training with over 3.3 million events and 100 million tracking frames from top-tier leagues, TacticGen achieves state-of-the-art precision in predicting player trajectories.<n>A case study with football experts confirms that TacticGen generates realistic, strategically valuable tactics.
  arXiv  Detail & Related papers  (2026-04-20T12:57:11Z)
• GenTac: Generative Modeling and Forecasting of Soccer Tactics [66.35532694378146]
  GenTac conceptualizes soccer tactics as a process over continuous multi-player trajectories and discrete semantic events.<n>It supports rich contextual conditioning, including opponent behavior, specific team or league playing styles, and strategic objectives.<n>It can be successfully trained to generalize to other dynamic team sports, including basketball, American football, and ice hockey.
  arXiv  Detail & Related papers  (2026-04-13T17:53:49Z)
• A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football [3.418921713486739]
  We introduce a co-dependent Hidden Markov Model (CDHMM) tailored to corner kicks in football games.<n>Our model infers time-resolved man-marking and zonal assignments directly from player tracking data.<n>We propose a novel framework for defensive credit attribution and a role-conditioned ghosting method for counterfactual analysis of off-ball defensive performance.
  arXiv  Detail & Related papers  (2026-01-02T17:10:36Z)
• ScoutGPT: Capturing Player Impact from Team Action Sequences Using GPT-Based Framework [5.36551574565267]
  We introduce EventGPT, a player-conditioned, value-aware next-event prediction model built on a GPT-style autoregressive transformer.<n>Our model treats match play as a sequence of discrete tokens, jointly learning to predict the next on-ball action's type, location, timing, and its estimated residual On-Ball Value.<n>By substituting learned player embeddings into new event sequences, we can simulate how a player's behavioral distribution and value profile would change when placed in a different team or tactical structure.
  arXiv  Detail & Related papers  (2025-12-19T06:30:11Z)
• Expandable Decision-Making States for Multi-Agent Deep Reinforcement Learning in Soccer Tactical Analysis [6.8055385768376615]
  Invasion team sports such as soccer produce a high-dimensional, strongly coupled state space as many players interact on a shared field.<n>Traditional rule-based analyses are intuitive, while modern predictive machine learning models often perform pattern-matching without explicit agent representations.<n>Here, we propose Expandable Decision-Making States (EDMS), a semantically enriched state representation that augments raw positions and velocities with relational variables.
  arXiv  Detail & Related papers  (2025-10-01T04:01:51Z)
• Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models [64.47869632167284]
  Conventional language model (LM) safety alignment relies on a reactive, disjoint procedure: attackers exploit a static model, followed by defensive fine-tuning to patch exposed vulnerabilities.<n>This sequential approach creates a mismatch -- attackers overfit to obsolete defenses, while defenders perpetually lag behind emerging threats.<n>We propose Self-RedTeam, an online self-play reinforcement learning algorithm where an attacker and defender agent co-evolve through continuous interaction.
  arXiv  Detail & Related papers  (2025-06-09T06:35:12Z)
• Robust Anti-Backdoor Instruction Tuning in LVLMs [53.766434746801366]
  We introduce a lightweight, certified-agnostic defense framework for large visual language models (LVLMs)<n>Our framework finetunes only adapter modules and text embedding layers under instruction tuning.<n>Experiments against seven attacks on Flickr30k and MSCOCO demonstrate that ours reduces their attack success rate to nearly zero.
  arXiv  Detail & Related papers  (2025-06-04T01:23:35Z)
• Unveiling Hidden Pivotal Players with GoalNet: A GNN-Based Soccer Player Evaluation System [8.957579200590988]
  Soccer analysis tools emphasize metrics such as expected goals, leading to an overrepresentation of attacking players' contributions.<n>We introduce a GNN-based framework that assigns individual credit for changes in expected threat (xT)<n>Our pipeline encodes both spatial and temporal features in event-centric graphs, enabling fair attribution of non-scoring actions.
  arXiv  Detail & Related papers  (2025-03-12T18:36:55Z)
• Toward Optimal LLM Alignments Using Two-Player Games [86.39338084862324]
  In this paper, we investigate alignment through the lens of two-agent games, involving iterative interactions between an adversarial and a defensive agent. We theoretically demonstrate that this iterative reinforcement learning optimization converges to a Nash Equilibrium for the game induced by the agents. Experimental results in safety scenarios demonstrate that learning in such a competitive environment not only fully trains agents but also leads to policies with enhanced generalization capabilities for both adversarial and defensive agents.
  arXiv  Detail & Related papers  (2024-06-16T15:24:50Z)
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.