Research on Tennis Match Strategies Based on Machine Learning and Markov Chain Modeling

Abstract

This paper mainly studies the influence of athletes' performance on the results of sports competitions, and uses advanced data analysis and machine learning technology to predict the players' scoring patterns in tennis matches. Firstly, the Markov chain model is used to capture match points and game flow. In order to quantify player performance, the concept of momentum is introduced. The random forest model based on factor analysis optimization studies the factors affecting player performance. Secondly, the random forest model optimized by firefly algorithm is used to select the factors that may affect the competition, identify the important factors and data that affect the fluctuation of the competition, and realize the prediction. This study provides an advanced perspective on sports analytics, providing actionable insights for athletes and coaches to refine their strategies and gain a competitive edge, while also providing lessons for the development of sports analytics and data-driven decision making.