基于时空跟踪数据的足球比赛阶段自动检测：图神经网络方法

中文摘要 AI 辅助

时空跟踪数据为检测足球比赛中复杂战术模式提供了新可能，但对多名球员的互动运动进行建模仍具挑战性。本文提出一种结合图神经网络（GNN）与序列模型的框架，用于按秒级对足球比赛阶段进行分类，分类体系包含7个类别。比赛阶段分类具有战术意义，且203场比赛的规则标签可用性使其成为邻接构造与消息传递层系统比较的合适测试平台，该问题在现有研究中受关注有限。本文选定的GNN-LSTM模型优于所有聚合特征基线，包括XGBoost和长短期记忆网络（LSTM），最强基线的宏F1得分比该模型低4.6%。采用领域知识驱动的Delaunay三角剖分（近似传球路线）的图表示，结合自定义空间边增强卷积（SEAConv）层（将边属性直接注入消息），通过捕捉空间依赖关系同时限制冗余边的无信息消息，实现最佳性能。集成梯度归因表明，模型利用球员空间配置（尤其是水平位置），结合控球权与球状态指标。本研究为细粒度战术分析提供自动化解决方案，减少手动标记需求，为动态团队行为提供更深入见解。

英文摘要

Spatio-temporal tracking data has opened new possibilities for detecting complex tactical patterns in football, yet modeling the interactive movements of multiple players remains challenging. This paper proposes a framework combining graph neural networks (GNNs) with a sequential model to classify match phases on a second-by-second basis across a seven-class taxonomy. Match phase classification is tactically meaningful, and the availability of rule-based labels across 203 matches makes it a suitable testbed for a systematic comparison of adjacency constructions and message-passing layers, a question that has received limited attention in existing research. Our selected GNN-LSTM model outperforms all aggregated-feature baselines, including XGBoost and a Long Short-Term Memory (LSTM) network, as the strongest baseline scores 4.6% lower in macro F1. Graph representations using a domain-informed Delaunay triangulation that approximates passing lanes, paired with a custom Spatial Edge-Augmented Convolution (SEAConv) layer that injects edge attributes directly into messages, achieve the best performance by capturing spatial dependencies while limiting uninformative messages from redundant edges. Integrated Gradients attributions indicate the model uses spatial player configurations, particularly horizontal positioning, in combination with possession and ball-status indicators. This work offers an automated solution for fine-grained tactical analysis, reducing the need for manual tagging and providing deeper insight into dynamic team behavior.

发表机构

• Institute of Statistics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院统计研究所）
• Karlsruher Sport-Club（卡尔斯鲁厄体育俱乐部）
• Broad Institute of MIT & Harvard（麻省理工学院与哈佛大学生物医学 Broad 研究所）
• German Football Association (DFB)（德国足球协会）
• Chair for Sports Analytics, Saarland University（萨尔兰大学体育分析讲席）
• Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）

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