A spatio-temporal GCN–Transformer framework for team performance analysis in sports psychodynamics with correlation consistency and stability modeling

Sports analytics increasingly demands a comprehensive understanding of both individual athlete behavior and collective team dynamics. However, most existing approaches primarily focus on temporal patterns while overlooking the critical spatial interdependencies among athletes. To address this limitation, this study proposes a hybrid Graph Convolutional Network (GCN)–Transformer framework that jointly models spatial and temporal dynamics in team performance analysis. The proposed model integrates GCNs to capture inter-athlete relationships through dynamic graph construction, while the Transform