用图神经网络分析足球数据，自动识别进攻阶段与战术意图。
Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning
- 构建三层战术意图模型，结合时序图注意力网络捕捉球员互动与时间演化。
- 帧级F1达0.87，阶段划分的时序重叠率提升至0.67，显著改善边界误判。
- 适合足球战术分析、自动标注与球队风格建模，尤其擅长识别反击等复杂阶段。
理解足球战术需识别由动态战术意图驱动的持球阶段，而非仅依赖空间模式。本研究提出一种意图驱动框架，基于7场德甲比赛（25 Hz TRACAB数据）进行分析。定义三类战术意图（进攻对方区域、控球、得分）与六类阶段（组织进攻、推进、反击、维持、持续威胁、射门）。采用时序图注意力网络（T-GAN），融合帧级球员交互图、上下文特征与Transformer时序建模。评估使用帧级F1与时序交并比（tIoU）。T-GAN在意图层平均帧级F1为0.87，进攻相关阶段0.76，得分阶段0.79。过滤后，意图类tIoU从0.44升至0.67，阶段类从0.40升至0.57，表明时序评估能捕捉帧级指标遗漏的片段化与边界误差。模型对比显示，Transformer时序建模促进分割连贯性，图模型对反击阶段最有益。错位分析揭示常见问题：组织/推进模糊、组织/维持混淆、反击/推进不一致、射门前段过短。整体框架将追踪数据转化为可解释的战术阶段表示，支持自动化标注、战术分析与风格画像。
原文摘要 · Abstract (English)
Understanding tactical organisation in association football requires identifying in-possession match phases that are shaped by evolving tactical intentions rather than by spatial patterns alone. This study proposes an intention-driven framework for identifying phases from tracking data. Seven German Bundesliga matches recorded at 25 Hz with TRACAB were analysed. A hierarchical model was defined with three tactical intentions (Invade Opponent Space, Keep Possession, Scoring) and six phases (Build Up, Progression, Counter Attack, Maintenance, Sustained Threat, Finishing). A Temporal Graph Attention Network (T-GAN) combined frame-level player-interaction graphs, contextual features, and Transformer-based temporal modelling. Performance was evaluated using frame-level F1 and temporal Intersection over Union (tIoU). T-GAN achieved macro-average frame-level F1 scores of 0.87 at the intention level, 0.76 for invasion-related phases, and 0.79 for scoring phases. After filtering, mean class-wise tIoU increased from 0.44 to 0.67 for intentions and from 0.40 to 0.57 for phases, showing that sequence-level evaluation captured fragmentation and boundary errors missed by frame-level metrics. Model comparisons indicated that Transformer-based sequence modelling drove coherent segmentation, while graph-based relational modelling was most beneficial for Counter Attack. Misalignment analysis revealed common sequence level misalignments, mainly Build Up/Progression ambiguity, Build Up/Maintenance confusion, Counter Attack/Progression inconsistency, and shortened pre-shot Finishing segments. Overall, the framework translates tracking data into tactically interpretable phase representations for automated annotation, tactical analysis, and playing-style profiling.
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