Based on the LGBM model, a dynamic analysis was conducted to study the momentum of athletes in tennis matches

Haoming Tian; Ye Zhang; Runduo Ding

doi:10.62051/f4c1pq70

Authors

Haoming Tian
Ye Zhang
Runduo Ding

DOI:

https://doi.org/10.62051/f4c1pq70

Keywords:

Light Gradient Boosting Machine, Support Vector Regression, Momentum, Multiple logistic regression, Tennis tournament.

Abstract

The changes in momentum in a tennis tournament have a significant impact on players' performance and the outcome of the match. The application of big data and machine learning techniques can effectively enhance the accuracy of predicting these dynamic changes. This article aims to delve into the impact of momentum on matches and how it can be leveraged to help players achieve success. Firstly, this paper determines the confidence level of each indicator through multiple logistic regression and uses the LGBM algorithm to train the data to predict the real-time momentum of an athlete, confirming that momentum is real and will have a great impact on the competition result. The paper trains the obtained data using the SVR model. By comprehensively applying the information gain method, this paper derive importance scores for each indicator. This measure was found to have the biggest impact on momentum in the progress in scoring.

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