Purpose The purpose of this paper is to propose a hierarchical deep reinforcement learning (H-DRL) framework for real-time tactical decision-making in team sports. The framework addresses challenges such as continuous action spaces, partial observability and adversarial environments by leveraging multi-agent collaboration, adaptive strategy optimization and explainable artificial intelligence (AI). It aims to enhance tactical accuracy, decision speed and resource efficiency while uncovering novel tactical patterns that human experts may overlook. Design/methodology/approach The study combines graph neural networks (GNNs) for spatial-temporal player interactions and transformer-based attention for strategic pattern recognition. It integrates opponent modeling via inverse reinforcement learning (IRL) and self-play. The hierarchical architecture decomposes decisions into strategic, tactical and technical levels. Experiments were conducted on professional basketball, soccer and rugby datasets, with validation by expert coaches. The framework was tested in real-world deployments, including youth academies and professional teams, to evaluate performance and tactical innovations. Findings The H-DRL framework achieved a 34.7% higher tactical accuracy, 28.3% faster decision-making and 41.2% lower resource usage compared to state-of-the-art methods. It identified 17 new tactical patterns, such as dynamic role-switching, which improved scoring efficiency by 23.6%. Real-world deployments demonstrated significant performance gains, including a 42.3% improvement in tactical decision-making for youth teams. The system's explainable AI module bridged algorithmic insights with coach expertise, fostering trust and adoption. Research limitations/implications The study is limited by its reliance on proprietary tracking data and the computational demands of real-time deployment. Future research could explore cross-sport generalization and integration of physiological/psychological factors. The framework's scalability to larger team sizes and more complex environments remains a challenge. These limitations highlight opportunities for advancements in model compression and hardware optimization. Practical implications The framework provides actionable insights for coaches and players, enhancing in-game decision-making and training efficiency. It enables teams to adopt data-driven tactics, such as elastic pressing in soccer or optimized phase play in rugby. The system's real-time capabilities (30.9 ms latency) make it suitable for live match analysis. Professional teams reported improved tactical understanding (91.7% of coaches) and scoring efficiency (23.6% increase). The technology is applicable beyond sports, including autonomous systems and emergency response. Social implications The study promotes the ethical use of AI in sports, emphasizing augmentation over replacement of human expertise. It fosters collaboration between coaches and AI, enhancing tactical literacy and innovation. The framework's transparency builds trust, addressing concerns about black-box AI. By uncovering counterintuitive strategies, it challenges traditional coaching paradigms and encourages continuous learning. The technology's broader applications (e.g. military, robotics) underscore its societal impact. Originality/value This paper presents a hierarchical DRL framework for real-time tactical decision-making in team sports, integrating GNNs, transformers and IRL. Its dual-stream architecture and adaptive computation are novel contributions. The system's ability to discover and explain tactical innovations (e.g. dynamic role-switching) sets it apart from prior work. The rigorous validation across multiple sports and real-world deployments demonstrates its practical value. The study advances multi-agent AI, offering scalable solutions for complex, dynamic environments.
Kong et al. (Fri,) studied this question.