<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to make up for the existing deficiencies in the analysis of the factors affecting the athletes’ victory, scientifically and effectively judge the technical indicators that affect the athletes’ victory, the author puts forward the analysis of tennis techniques and tactics based on multiple linear regression model. Based on the Logistic regression model, the author made statistics on the data of 10 events of men's singles in 2021 Open, and conducted a series of tests such as normal distribution test and Kruskal Wallis test on the data, finally, a regression model was obtained, which believed that the scoring rate of the first round, the second round and the second round had the greatest impact on the victory or defeat of men's singles in 2021 open, the final Logistic regression model was used to test the goodness of fit of various data of men's tennis singles in 2021, the segmentation point was selected as 0.5, and the prediction accuracy of the regression model was 82.1%. Therefore, it can be determined that this model has a high accuracy in predicting the victory or defeat of 2021 men's tennis singles matches, and the regression model has a good prediction effect. It is suggested that athletes should pay attention to the integrated ability training of attack and defense.</p>
</abstract>

In order to make up for the existing deficiencies in the analysis of the factors affecting the athletes’ victory, scientifically and effectively judge the technical indicators that affect the athletes’ victory, the author puts forward the analysis of tennis techniques and tactics based on multiple linear regression model. Based on the Logistic regression model, the author made statistics on the data of 10 events of men's singles in 2021 Open, and conducted a series of tests such as normal distribution test and Kruskal Wallis test on the data, finally, a regression model was obtained, which believed that the scoring rate of the first round, the second round and the second round had the greatest impact on the victory or defeat of men's singles in 2021 open, the final Logistic regression model was used to test the goodness of fit of various data of men's tennis singles in 2021, the segmentation point was selected as 0.5, and the prediction accuracy of the regression model was 82.1%. Therefore, it can be determined that this model has a high accuracy in predicting the victory or defeat of 2021 men's tennis singles matches, and the regression model has a good prediction effect. It is suggested that athletes should pay attention to the integrated ability training of attack and defense.

Analysis of tennis techniques and tactics based on multiple linear regression model

Shanghai Lixin University of Accounting and Finance

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to make up for the existing deficiencies in the analysis of the factors affecting the athletes’ victory, scientifically and effectively judge the...

Analysis of tennis techniques and tactics based on multiple linear regression model

Online veröffentlicht: 06. Juni 2023

Seitenbereich: 2061 - 2068

Eingereicht: 05. Juli 2022

Akzeptiert: 29. Nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00310

Schlüsselwörter
Multivariate linearity, Regression model, Tennis, Winning factors, Tactical Analysis

© 2023 Ling Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of tennis techniques and tactics based on multiple linear regression model

Online veröffentlicht: 06. Juni 2023

Seitenbereich: 2061 - 2068

Eingereicht: 05. Juli 2022

Akzeptiert: 29. Nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00310

SchlüsselwörterMultivariate linearity, Regression model, Tennis, Winning factors, Tactical Analysis

© 2023 Ling Yu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Multivariate linearity, Regression model, Tennis, Winning factors, Tactical Analysis