Research on Tennis Match Outcome Prediction Based on Multi-Algorithm Integration and Bayesian Analysis

Kangqi Yu; Jingjing Liu; Xiaole Meng

doi:10.54097/08qdyh39

Authors

Kangqi Yu
Jingjing Liu
Xiaole Meng

DOI:

https://doi.org/10.54097/08qdyh39

Keywords:

GA-XGBoost, LightGBM, Bayesian Change Point Analysis, Machine Learning, Tennis Momentum Prediction.

Abstract

The intense competition in the men's singles final of the 2023 Wimbledon Championships highlighted the dynamic and unpredictable nature of tennis matches. Inspired by this observation, this study aims to quantify and analyze momentum shifts in tennis matches and explore their impact on match outcomes. We compare multiple machines learning algorithms, including MLP, Logistic Regression, XGBoost, and Naive Bayes, ultimately selecting the XGBoost model for its superior ability to handle nonlinear relationships and high-dimensional data. Furthermore, we optimize the XGBoost model with a Genetic Algorithm (GA) to better capture match dynamics and employ LightGBM to predict fluctuations during matches. Additionally, Bayesian Change Point Analysis is used to detect key fluctuation points. Through data preprocessing, feature extraction (covering technical, tactical, psychological, and physical factors), and model training and validation, our findings reveal that momentum plays a significant role in determining match outcomes. This study presents a novel analytical framework, offering new perspectives and methods for understanding and predicting tennis match results.

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