用可解释的低维模型分析足球传球，提升战术决策效率
Interpretable Low-Dimensional Modeling of Spatiotemporal Agent States for Decision Making in Football Tactics
- 基于球员与球距、空间得分等直观变量建模
- 在西甲2023/24赛季数据上预测传球成功率
- 适合教练和分析师快速理解战术关键因素
理解足球战术对教练和分析师至关重要。以往研究依赖空间与运动学方程，计算成本高；强化学习方法虽用位置速度但缺乏可解释性且需大量数据；规则模型符合专家经验，却未充分考虑所有球员状态。本研究探索基于时空数据的低维规则模型是否能有效捕捉足球战术。通过与教练讨论，定义了持球者及潜在接球者的关键状态变量，聚焦传球选项。利用StatsBomb事件数据与SkillCorner追踪数据（2023/24赛季西甲），训练XGBoost模型预测传球成功率。结果表明，球员与球的距离及空间得分是决定传球成功的核心因素。该可解释的低维模型以直观变量支持战术分析，具备实际应用价值。
原文摘要 · Abstract (English)
Understanding football tactics is crucial for managers and analysts. Previous research has proposed models based on spatial and kinematic equations, but these are computationally expensive. Also, Reinforcement learning approaches use player positions and velocities but lack interpretability and require large datasets. Rule-based models align with expert knowledge but have not fully considered all players' states. This study explores whether low-dimensional, rule-based models using spatiotemporal data can effectively capture football tactics. Our approach defines interpretable state variables for both the ball-holder and potential pass receivers, based on criteria that explore options like passing. Through discussions with a manager, we identified key variables representing the game state. We then used StatsBomb event data and SkillCorner tracking data from the 2023$/$24 LaLiga season to train an XGBoost model to predict pass success. The analysis revealed that the distance between the player and the ball, as well as the player's space score, were key factors in determining successful passes. Our interpretable low-dimensional modeling facilitates tactical analysis through the use of intuitive variables and provides practical value as a tool to support decision-making in football.
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