Google DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK.
Liverpool FC, AXA Training Centre, Simonswood Lane, Kirkby, Liverpool, L33 5XB, UK.
Nat Commun. 2024 Mar 19;15(1):1906. doi: 10.1038/s41467-024-45965-x.
Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose TacticAI, an AI football tactics assistant developed and evaluated in close collaboration with domain experts from Liverpool FC. We focus on analysing corner kicks, as they offer coaches the most direct opportunities for interventions and improvements. TacticAI incorporates both a predictive and a generative component, allowing the coaches to effectively sample and explore alternative player setups for each corner kick routine and to select those with the highest predicted likelihood of success. We validate TacticAI on a number of relevant benchmark tasks: predicting receivers and shot attempts and recommending player position adjustments. The utility of TacticAI is validated by a qualitative study conducted with football domain experts at Liverpool FC. We show that TacticAI's model suggestions are not only indistinguishable from real tactics, but also favoured over existing tactics 90% of the time, and that TacticAI offers an effective corner kick retrieval system. TacticAI achieves these results despite the limited availability of gold-standard data, achieving data efficiency through geometric deep learning.
识别竞争对手球队所采用战术的关键模式,并制定有效的应对策略,是现代足球的核心。然而,通过算法来实现这一点仍然是一个开放的研究挑战。为了解决这一未满足的需求,我们提出了 TacticAI,这是一款由利物浦足球俱乐部的领域专家密切合作开发和评估的人工智能足球战术助手。我们专注于分析角球,因为它们为教练提供了最直接的干预和改进机会。TacticAI 结合了预测和生成组件,允许教练有效地为每个角球常规采样和探索替代球员阵容,并选择那些预测成功率最高的球员阵容。我们在一些相关的基准任务上验证了 TacticAI:预测接球手和射门尝试,并推荐球员位置调整。我们在利物浦足球俱乐部进行的一项定性研究验证了 TacticAI 的实用性。我们表明,TacticAI 的模型建议不仅与真实战术难以区分,而且在 90%的情况下优于现有战术,并且 TacticAI 提供了一种有效的角球检索系统。TacticAI 实现了这些结果,尽管黄金标准数据的可用性有限,但通过几何深度学习实现了数据效率。