Quantifying Tennis Players’ Performance and Analyzing Momentum Fluctuations Based on Weighted Logistic Regression Model

Abstract

This paper proposes a dual-model approach to analyze tennis players’ performance and predict match outcomes, which combines a weighted logistic regression model and a decision tree model. By examining key factors such as scoring rate and serving status, the models calculate the scoring probabilities at different time points, quantifying the performance of players throughout the match. Furthermore, the paper constructs a decision tree model to predict shifts in the match situation based on momentum fluctuations. The research findings underscore the importance of momentum in determining match outcomes and provide valuable insights for training and match strategies. This dual-model approach offers a fresh perspective on the impact of momentum in tennis matches, allowing players to better understand and adapt to the dynamics of momentum fluctuations throughout a match. Additionally, the study suggests targeted training, simulated match conditions, psychological training, real-time data tracking, and physical preparation as strategies to enhance performance and adapt to momentum changes. This comprehensive approach provides players with valuable insights to optimize their performance and strategies in tennis matches.