This report details frameworks optimizing performance in sports analytics, indicating benefits in tactical decision-making.
The report details comprehensive decision support frameworks for real-time sports analytics, including a six-stage closed-loop system for performance optimization that covers objective definition, data acquisition, integration, analytics, decision support, and implementation. It describes AI-powered tactical optimization systems like Hierarchical Deep Reinforcement Learning (H-DRL) for real-time adjustments and Markov Decision Process-based engines for substitution optimization. The infrastructure enabling this relies on edge computing and streaming frameworks to achieve sub-100ms decision cycles. The frameworks emphasize human-AI collaboration through interpretable AI (e.g., SHAP values) and are applied in sport-specific cases like basketball fatigue monitoring and soccer tactical dashboards.
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Subramani et al. (2025) studied this question.
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