Unveiling Hidden Pivotal Players with GoalNet: A GNN-Based Soccer Player Evaluation System
This work addresses the problem of biased player evaluation in soccer analytics for teams and analysts, offering a more comprehensive tool, though it is incremental as it builds on existing GNN methods.
The paper tackled the bias in soccer analysis tools that overrepresent attacking players by introducing a GNN-based framework to identify pivotal players using spatial and temporal features, assigning credit for changes in expected threat to highlight overlooked contributions like defensive plays.
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.