A Comprehensive Study on Dynamic Prediction and Momentum of Tennis Matches Based on XGBoost Technology and Factor Analysis Model

In recent years, “momentum” has attracted attention in sports competitions. This study, based on the data from the 2023 Wimbledon Men's Singles Final, quantitatively analyzes the impact of its dynamic changes on the outcome of the match, revealing the key role of momentum in tennis matches. The article first combines a model based on logistic regression and XGBoost technology to quantify the analysis of tennis match scoring moments and match processes. By analyzing the Wimbledon Gentlemen's Final data, it predicts the momentum changes in the match and provides the match process. Then, the article establishes a factor analysis model, using the screen plot to determine the number of factors, and obtains the factor scores through SPSS software, resulting in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F}-\mathbf{X}, F-F n$</tex> comprehensive scoring model. This concludes that “momentum” plays an important role in the match, that is, the player's performance fluctuations and the continuous occurrence of success exhibit a mutually dependent and restrictive relationship. Finally, the article uses the Pearson correlation coefficient as an analytical tool to quantify the correlation between match characteristics and draws a heat scatter plot. The final results demonstrate that the model can effectively capture momentum changes in competitions, providing a valuable reference for athletes and coaches in terms of competition strategies and future research.