Research on the Impact of Momentum in Tennis Players' Matches Based on LGBM and ExtraTrees Models

Tennis is a sport where the concept of momentum plays a decisive role. The ability of players to stage comebacks or seize strong starts can significantly influence the dynamics of a match. This study aims to use advanced machine learning models to analyze the impact of momentum on the performance of tennis players. Our main objective is to construct and evaluate the performance of Light Gradient Boosting Machine (LGBM) and ExtraTrees models, to quantify and predict shifts in momentum during tennis matches. We have collected a comprehensive dataset of tennis matches and identified key performance indicators (KPIs) that could potentially affect a player's momentum. These KPIs include player fatigue, technical proficiency, and psychological state. We preprocessed the data to handle missing values and outliers, followed by feature standardization. Subsequently, we employed LGBM and ExtraTrees models to analyze the data. To ensure robustness and generalizability, we trained the models using a 50-fold cross-validation method. The LGBM model demonstrated high accuracy in capturing real-time momentum, focusing on predicting the outcomes of individual game points. The ExtraTrees model was used to simulate match dynamics and provided insights into the overall game flow. Both models revealed a significant correlation between predicted momentum and actual match results. This study successfully implemented LGBM and ExtraTrees models to analyze the impact of momentum in tennis matches. The findings suggest that these models can be effectively used to predict match outcomes based on player momentum. The research also highlights areas for future work, such as incorporating player-specific metrics and expanding the model to other sports.