GenTac: Generative Modeling and Forecasting of Soccer Tactics
Jiayuan Rao, Tianlin Gui, Haoning Wu, Yanfeng Wang, Weidi Xie

TL;DR
GenTac is a diffusion-based generative framework that models and forecasts soccer tactics as stochastic multi-agent trajectories, capturing variance, style, and strategic outcomes from historical data.
Contribution
It introduces a novel probabilistic approach to soccer tactics modeling that captures diverse future trajectories and supports controllable, style-aware simulations.
Findings
Achieves high geometric accuracy and structural consistency.
Distinguishes stylistic nuances across teams and leagues.
Enables controllable counterfactual tactical simulations.
Abstract
Modeling open-play soccer tactics is a formidable challenge due to the stochastic, multi-agent nature of the game. Existing computational approaches typically produce single, deterministic trajectory forecasts or focus on highly structured set-pieces, fundamentally failing to capture the inherent variance and branching possibilities of real-world match evolution. Here, we introduce GenTac, a diffusion-based generative framework that conceptualizes soccer tactics as a stochastic process over continuous multi-player trajectories and discrete semantic events. By learning the underlying distribution of player movements from historical tracking data, GenTac samples diverse, plausible, long-horizon future trajectories. The framework supports rich contextual conditioning, including opponent behavior, specific team or league playing styles, and strategic objectives, while grounding continuous…
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