Soccer analysis tools emphasize metrics such as expected goals, leading to an overrepresentation of attacking players' contributions and overlooking players who facilitate ball control and link attacks. Examples include Rodri from Manchester City and Palhinha who just transferred to Bayern Munich. To address this bias, we aim to identify players with pivotal roles in a soccer team, incorporating both spatial and temporal features. In this work, we introduce a GNN-based framework that assigns individual credit for changes in expected threat (xT), thus capturing overlooked yet vital contributions in soccer. Our pipeline encodes both spatial and temporal features in event-centric graphs, enabling fair attribution of non-scoring actions such as defensive or transitional plays. We incorporate centrality measures into the learned player embeddings, ensuring that ball-retaining defenders and defensive midfielders receive due recognition for their overall impact. Furthermore, we explore diverse GNN variants-including Graph Attention Networks and Transformer-based models-to handle long-range dependencies and evolving match contexts, discussing their relative performance and computational complexity. Experiments on real match data confirm the robustness of our approach in highlighting pivotal roles that traditional attacking metrics typically miss, underscoring the model's utility for more comprehensive soccer analytics.

本研究针对现有足球分析工具对进攻球员贡献的偏重，提出了一个基于图神经网络（GNN）的框架，以识别并有效评估在球队中扮演关键角色的球员。通过结合空间和时间特征，该研究展示了如何公平地归因给防守或过渡行动中的非得分行为，实验结果表明，该方法能够更全面地识别传统指标所忽视的关键球员，从而推动足球分析的深入发展。

通过GoalNet揭示隐藏的关键球员：基于GNN的足球球员评估系统