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 spatial dynamics into a 15-class tactical event space. Extensive evaluations on our proposed benchmark, TacBench, demonstrate four key capabilities: (1) GenTac achieves high geometric accuracy while strictly preserving the collective structural consistency of the team; (2) it accurately simulates stylistic nuances, distinguishing between specific teams (e.g., Auckland FC) and leagues (e.g., A-League versus German leagues); (3) it enables controllable counterfactual simulations, demonstrably altering spatial control and expected threat metrics based on offensive or defensive guidance; and (4) it reliably anticipates future tactical outcomes directly from generated rollouts. Finally, we demonstrate that GenTac can be successfully trained to generalize to other dynamic team sports, including basketball, American football, and ice hockey.

翻译：摘要：对开放性比赛足球战术进行建模是一项艰巨挑战，这源于比赛本身的随机性和多智能体特性。现有计算方法通常生成单一、确定性的轨迹预测，或聚焦于高度结构化的定位球场景，从根本上无法捕捉真实比赛演化中的内在方差与分支可能性。在此，我们提出GenTac——一种基于扩散的生成框架，将足球战术概念化为连续多智能体轨迹与离散语义事件上的随机过程。通过从历史跟踪数据中学习球员运动的潜在分布，GenTac能够采样多样化、可信且长时域的未来轨迹。该框架支持丰富的上下文条件输入，包括对手行为、特定球队或联赛的技战术风格以及战略目标，同时将连续空间动态映射至15类战术事件空间。在我们提出的基准测试TacBench上进行的广泛评估展示了四项关键能力：（1）GenTac在严格保持团队集体结构一致性的同时实现高几何精度；（2）它能准确模拟风格差异，区分特定球队（如奥克兰FC）和联赛（如澳超联赛与德甲联赛）；（3）其支持可控的反事实模拟，能够基于进攻或防守引导显著改变空间控制与预期威胁指标；（4）它可直接从生成的推演中可靠预测未来战术结果。最后，我们证明GenTac可成功训练并泛化至其他动态团队运动，包括篮球、美式橄榄球和冰球。