Development of a Reinforcement Learning-Based Decision Support System for Optimizing Tactical Strategies in Professional Team Sports

Abstract

Introduction: Tactical decision-making in professional team sports represents one of the most complex challenges for coaches and analysts, influenced by numerous dynamic factors. Traditional tactical analysis methods rely primarily on human observation and descriptive statistics, lacking sufficient capability for real-time prediction and optimization. This study aimed to develop a reinforcement learning-based decision support system for optimizing tactical strategies in professional team sports. Methodology: Data from 500 professional matches across football, basketball, and volleyball leagues were utilized. Player positioning data were collected using optical tracking systems and match statistics. A reinforcement learning simulation environment was designed using Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to train an agent to make optimal tactical decisions. System performance was evaluated using win rate, points scored, and decision efficiency, compared with decisions made by professional coaches. Findings: The reinforcement learning-based decision support system identified optimal decisions in 73.8% of cases compared to actual coaches' decisions. In match simulations, teams using system recommendations achieved 12.8% higher average points and 6.5% higher win rates than control teams. The PPO algorithm demonstrated superior performance with 86.4% accuracy and higher convergence speed compared to DQN (82.1% accuracy). The most influential variables in tactical decisions included ball position, player distance from ball, remaining time, and score differential. Conclusion: The reinforcement learning-based decision support system can serve as an assistive tool for tactical analysis and decision-making in professional team sports, improving team performance through optimal real-time recommendations.