用图神经网络量化防守贡献，让隐形防守可见。
Better Prevent than Tackle: Valuing Defense in Soccer Based on Graph Neural Networks
- 基于图注意力网络建模进攻选择与防守责任
- 通过预期控球价值变化评估防守成效，相关性高
- 适合俱乐部转会评估与教练战术分析
评估足球防守表现仍具挑战，因有效防守常体现为阻止危险机会生成，而非抢断、铲球等显性动作。现有方法多聚焦于有球行为，忽视了防守者真实影响力。为此，我们提出DEFCON（DEFensive CONtribution evaluator）框架，全面量化每轮进攻中球员的防守贡献。该框架利用图注意力网络，估算每次进攻选项的成功概率与预期价值，并判断每位防守者的责任。由此得出进攻前后的预期控球价值（EPV），根据防守是否降低对手EPV，给予正负积分。模型在2023-24赛季和2024-25赛季荷甲事件与追踪数据上训练与评估，球员累计积分与市场估值呈现强正相关。此外，展示了多项实际应用：防守贡献的实时时间线、不同场区的空间分布分析，以及攻防对决的成对摘要。
原文摘要 · Abstract (English)
Evaluating defensive performance in soccer remains challenging, as effective defending is often expressed not through visible on-ball actions such as interceptions and tackles, but through preventing dangerous opportunities before they arise. Existing approaches have largely focused on valuing on-ball actions, leaving much of defenders' true impact unmeasured. To address this gap, we propose DEFCON (DEFensive CONtribution evaluator), a comprehensive framework that quantifies player-level defensive contributions for every attacking situation in soccer. Leveraging Graph Attention Networks, DEFCON estimates the success probability and expected value of each attacking option, along with each defender's responsibility for stopping it. These components yield an Expected Possession Value (EPV) for the attacking team before and after each action, and DEFCON assigns positive or negative credits to defenders according to whether they reduced or increased the opponent's EPV. Trained on 2023-24 and evaluated on 2024-25 Eredivisie event and tracking data, DEFCON's aggregated player credits exhibit strong positive correlations with market valuations. Finally, we showcase several practical applications, including in-game timelines of defensive contributions, spatial analyses across pitch zones, and pairwise summaries of attacker-defender interactions.
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