TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics
Sheng Xu, Guiliang Liu, Tarak Kharrat, Yudong Luo, Mohamed Aloulou, Javier L\'opez Pe\~na, Konstantin Sofeikov, Adam Reid, Paul Roberts, Steven Spencer, Joe Carnall, Ian McHale, Oliver Schulte, Hongyuan Zha, and Wei-Shi Zheng

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…
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