Research on the Prediction of Tennis Match Momentum Based on BP Neural Network and Linear Regression Analysis

Abstract

This study conducted an in-depth analysis of the momentum shift phenomenon during the men's singles matches at the 2023 Wimbledon Open, with the aim of quantifying momentum changes and their impact on match outcomes through a comprehensive data analysis framework that incorporates multivariate statistical methods such as BP neural networks, principal component analysis (PCA), entropy weighting analysis, and multivariate linear regression. The model, after sensitivity analysis and quantification through player rankings, is capable of predicting momentum shifts during a match and providing athletes with strategic adjustments. Additionally, the model's generalization ability was validated, demonstrating its potential applicability across various competitions, courts, and sports. The research outcomes not only offer coaches and athletes strategies for momentum shifts but also highlight the model's broad potential for application in sports match analysis.