Dual-Encoder Temporal-Contrastive Learning for Coaching Transition Prediction
Keywords:
Coaching-Style Transition, Temporal-Contrastive Learning, Multi-modal Alignment, Graph Neural Networks, Real-Time Sports AnalyticsAbstract
We propose a novel framework for predicting optimal coaching style transitions in dynamic game phases by aligning temporal game-phase dynamics with non-temporal tactical knowledge. The proposed method integrates a Temporal Transformer Encoder to process sequential game data and a Graph Neural Network (GNN) Encoder to embed static coaching strategies, enabling joint modeling of time-sensitive and context-aware features. A contrastive learning objective aligns these representations while preserving temporal dependencies through a dedicated Temporal Feature Alignment (TFA) module, which emphasizes phase-specific patterns without disrupting long-range coherence. The system predicts transitions by fusing the aligned representations and training end-to-end with a combined loss function. Our approach addresses the critical challenge of adapting coaching strategies to rapidly evolving game conditions, where traditional methods often fail to capture the interplay between temporal events and strategic context. Experiments demonstrate significant improvements in transition prediction accuracy compared to baselines, highlighting the framework's ability to generalize across diverse game scenarios. Moreover, the modular design allows seamless integration with existing sports analytics pipelines, offering practical value for real-time decision support. The results suggest that contrastive multi-modal alignment can effectively bridge the gap between data-driven insights and tactical adaptability in competitive sports.
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