Analysis of Momentum in Tennis Matches Based on Ensemble Learning

In tennis, player performance is often attributed to "momentum," pivotal for game control. We analyzed tennis rules, extracting performance indicators such as point differentials, dynamic scoring, and serving advantages. Utilizing a Random Forest model with player performance as the dependent variable, we mapped features to momentum and determined feature importance, achieving an AUC of 0.8796. We then introduced the Comprehensive Ability Volatility Index (CVI) based on factors like serve win rate and breakpoint success rate, identifying match-turning points. To capture these turning points, we integrated momentum, player fitness, and overall strength into an XGBoost model, which yielded an AUC of 0.8716. The model's feature importance output guided concise recommendations for strategic decision-making. Our research offers comprehensive strategies for athletes and coaches to consider momentum, physical fitness, and other match factors, enhancing guidance for daily training and match adjustments.