Momentum Dynamics in Competitive Sports: A Multi-Model Analysis Using TOPSIS and Logistic Regression

TL;DR

This study analyzes sports momentum using TOPSIS and logistic regression models, revealing how performance fluctuations influence match outcomes and providing a framework applicable across various sports.

Contribution

Introduces a multi-model approach combining TOPSIS and logistic regression to quantify and verify the impact of momentum in sports competitions.

Findings

Models accurately explain match dynamics

Momentum fluctuations significantly affect outcomes

Framework applicable to multiple sports

Abstract

This paper explores the concept of "momentum" in sports competitions through the use of the TOPSIS model and 0-1 logistic regression model. First, the TOPSIS model is employed to evaluate the performance of two tennis players, with visualizations used to analyze the situation's evolution at every moment in the match, explaining how "momentum" manifests in sports. Then, the 0-1 logistic regression model is utilized to verify the impact of "momentum" on match outcomes, demonstrating that fluctuations in player performance and the successive occurrence of successes are not random. Additionally, this paper examines the indicators that influence the reversal of game situations by analyzing key match data and testing the accuracy of the models with match data. The findings show that the model accurately explains the conditions during matches and can be generalized to other sports competitions. Finally, the strengths, weaknesses, and potential future improvements of the model are discussed.