Monte Carlo Method and XGBoost Algorithm Based Match Momentum Prediction Study

Focusing on the analysis and prediction of momentum in tennis matches, this study explores the feasibility of using reinforcement learning (RL) and machine learning methods to solve the problem of dynamic changes in tennis matches. First, we constructed a MBPE model based on Monte Carlo method, which was trained by Wimbledon tennis tournament data and successfully revealed the momentum differences between different players in the match. The results show that the accuracy and predictive ability of the model in the final match are effectively verified. Secondly, we verified the existence of momentum in the match using Pearson and Spearman correlation analysis and proved its non-randomness. Finally, we proposed the TBMP model, which combines LSTM, Transformers and XGBoost algorithms, to successfully predict the change of player's momentum during the game. By comprehensively analyzing the game state and players' movements, we identified the main factors affecting the momentum shifts and proposed a quick response to the opponent's momentum changes using meta-learning techniques. This study provides practical data analysis tools and strategies for players and coaches, and is expected to play an active role in improving tennis match decision-making and technical training.