A study of sports scores based on correlation analysis and logistic regression

Shaohang Chen; Jiacheng Chen; Ruizhe Jiang; Yian Xuan; Siming Liang

doi:10.62051/85rm8739

Authors

Shaohang Chen
Jiacheng Chen
Ruizhe Jiang
Yian Xuan
Siming Liang

DOI:

https://doi.org/10.62051/85rm8739

Keywords:

Correlation Analysis; Logistic Regression; Momentum.

Abstract

This paper examines the relationship between "Momentum" and victory in the imbledon 2023 men's tournament, particularly between the semi-finals and the final. The definition and ormula of the Pearson's correlation coefficient are presented, and the Pearson's correlation oefficient is analysed based on the match data between Carlos Alcaraz and Novak Djokovic. The results show that there is a positive correlation between "Momentum" and winning. Then the logistic regression model was used to analyse the factors affecting the variation of "kinetic energy", such as onsecutive points, decisive points, key points, fatigue level and double faults. By training and testing the data set, a logistic regression model was developed for predicting the changes in "kinetic energy", and some suggestions based on the differences in "Momentum" were made to help players and coaches better cope with the changes in "Momentum" in the game. This paper also proposes some suggestions based on "Momentum" differences to help players and coaches better cope with "Momentum" changes in matches.

Downloads

Download data is not yet available.

References

[1] WANG Wei, CHEN Xiaowei, KANG Shaojie, et al. Research on the performance prediction method of female throwers--taking the shot-put event as an example[C]// Chinese Society of Sports Science. Abstracts of the Thirteenth National Conference on Sports Science--Special Report (Sports Statistics Division). Chengdu Institute of Physical Education; Aba Normal College; 2023: 3.

[2] Lin Xianjian. Prediction of Physical Education Secondary School Examination Scores Based on BP Neural Networks[C]// Chinese Society of Sports Science. Abstracts Collection of the Thirteenth National Sports Science Conference - Written Communication (Sports Statistics Division). Yili Normal University; 2023: 2.

[3] J.Y. Long,Z.H. Ui. Research on the prediction of Olympic men's 100m gold medal performance based on GM (1,1) grey model [J]. Journal of Southwest Normal University (Natural Science Edition), 2023, 48 (07): 123 - 128.

[4] Li Ran. A study on the competitive performance of women's throwing events in the 24th-32nd Olympic Games and the prediction of the results of the Paris Olympics [D]. Qufu Normal University, 2023.

[5] Pei Zheng, Leng Qijian. Research on the development trend of women's event performance and grey GM (1,1) model prediction at the World Athletics Championships [J]. Sports Science and Technology Literature Bulletin, 2023, 31 (05): 37 - 39+64.

[6] Li Jiaqi. A comparative study of different prediction methods on the trend of sprint performance in China [D]. Tianjin Sports Institute, 2023.

[7] FAN Yingli, ZHENG Zhiqiang, ZHENG Wei. Prediction of college students' sports performance based on improved SSA-LSSVM model [J]. Computer Simulation, 2023, 40 (01): 283 - 289+483.

[8] Feng QM. Sports performance prediction based on time series analysis method [J]. Microcomputer Applications, 2022, 38 (12): 35 - 37+48.

[9] Huang ZY. Research on tennis performance prediction model based on BP neural network [D]. Fujian Normal University, 2022.

[10] Zhang M. A study on the prediction of competition time of China's outstanding speed skating 1500m athletes by 30sWingate test [D]. Jilin Institute of Physical Education and Sports, 2022.