用图神经网络分析足球传球决策，精准预测最佳接球人。
Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection
- 将球员看作节点，传球路径为加权边，构建动态比赛图模型。
- 在专业比赛中准确识别实际接球者，前3个建议中达到顶尖水平。
- 可量化每种传球选择的威胁与创意，助力快速战术分析。
足球决策过程涉及空间位置、防守压力与球员意图的复杂交互。本文提出一种图神经网络（GNN）框架，通过将场上互动建模为动态图来预测传球接收者。每个球员作为节点，携带位置和上下文特征；潜在传球线路作为加权边，由距离、角度和压力指标定义。基于优化版Needleman-Wunsch算法构建的稳健数据对齐管道，融合了职业比赛的追踪数据与事件数据，训练出消息传递神经网络（MPNN）。模型在识别实际接球人方面表现优异，在前三个推荐选项中达到当前最优精度。此外，模型能量化每个传球选择的可能概率、威胁值与创意度，使分析师可在数秒内评估超过1000次传球。
原文摘要 · Abstract (English)
The process of decision-making in football is characterized by a complex interplay between spatial positioning, opponent pressure, and player intent. This work introduces a Graph Neural Network (GNN) framework designed to predict Receiver Selection, the optimal passing target, by modeling on-field interactions as dynamic graphs. Each player is represented as a node with positional and contextual features, while potential passing lines form weighted edges characterized by distance, angle, and pressure metrics. A Message-Passing Neural Network (MPNN) has been developed and trained using a combination of tracking data and event data from professional matches, synchronized through a robust pipeline based on an optimized version of the Needleman-Wunsch Algorithm. The model achieves competitive accuracy in identifying the actual chosen receiver and state-of-the-art accuracy within its top three suggestions. Our model further offers quantification of each option's likelihood, threat, and creativity, enabling performance analysts to evaluate over 1,000 passes in seconds.
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