TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics

TL;DR

TacticGen is a scalable generative model that creates adaptable football tactics by modeling multi-agent movements conditioned on game context, validated through expert case studies.

Contribution

It introduces a novel multi-agent diffusion transformer for tactical generation, bridging the gap between predictive analysis and tactical design in football.

Findings

Achieves state-of-the-art trajectory prediction accuracy.

Enables tactic generation tailored to specific objectives.

Validated by expert case studies demonstrating practical utility.

Abstract

Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep learning have enabled predictive analyses like trajectory forecasting, the development of tactical design remains limited. Bridging this gap is essential, as prediction reveals what is likely to occur, whereas tactic generation determines what should occur to achieve strategic objectives. In this work, we present TacticGen, a generative model for adaptable and scalable tactic generation. TacticGen formulates tactics as sequences of multi-agent movements and interactions conditioned on the game context. It employs a multi-agent diffusion transformer with agent-wise self-attention and context-aware cross-attention to capture cooperative and competitive dynamics among players and the ball. Trained 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. Building on it, TacticGen enables adaptable tactic generation tailored to diverse inference-time objectives through classifier guidance mechanism, specified via rules, natural language, or neural models. Its modeling performance is also inherently scalable. A case study with football experts confirms that TacticGen generates realistic, strategically valuable tactics, demonstrating its practical utility for tactical planning in professional football. The project page is available at: https://shengxu.net/TacticGen/.