Using Reinforcement Learning to Identify The Key Factors for Players to Win Games

With the gradual development of sports data analysis, data-driven game prediction has gradually become an important tool for improving the decision-making efficiency of teams and coaches. Basketball, as a team sport, is influenced by multiple factors, and traditional statistical analysis methods make it difficult to intuitively identify the factors that have the greatest impact on the game. This study proposes a reinforcement learning model based on the Proximal Policy Optimization (PPO) algorithm for predicting the winning rate of NBA games. By collecting career statistics of NBA players and combining them with the team's current season winning rate, a feature vector containing individual player characteristics and team winning rate is constructed. The model is trained using the PPO algorithm to identify the most critical features that affect the winning rate of games. This study not only provides new ideas for sports event prediction, but also provides solutions for future decision-making and dataization in big data environments.