A Comprehensive Study on the Role of Momentum in the Sports Games

Yaqi Fan; Shilin Fan; Kaiyu Zhang

doi:10.54097/3w1t1y21

Authors

Yaqi Fan
Shilin Fan
Kaiyu Zhang

DOI:

https://doi.org/10.54097/3w1t1y21

Keywords:

Random Forests; Momentum Forecast; Logistic Regression Pearson; Match Swings; Correlation Coefficient.

Abstract

This study aims to explore the effects of momentum and swing on tennis match results. The paper believes that momentum may be a key factor affecting player performance and match results. The research methods include data preprocessing, PCA dimensionality reduction, Pearson correlation coefficient analysis, logistic regression model and random forest model. Data preprocessing includes outlier treatment, one-hot encoding and normalization. PCA is used to reduce the dimension of match indicators and extract the main information. Pearson correlation coefficient analysis is used to test the correlation between match results and momentum. Logistic regression model is used to predict the probability of a player winning a point, and random forest model is used to predict match fluctuations. The study uses data from the 2023 US Open to validate the model. Sensitivity analysis is performed by changing model parameters to ensure model accuracy. The conclusion shows that momentum does affect match results, and our model can predict match performance and fluctuations with high accuracy. This is of great significance for coaches and athletes to formulate strategies and improve match performance. This study is of great value in understanding the momentum effect in tennis and how to use this knowledge to improve athlete performance.

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References

[1] Anbarci N, Lee J, Ulker A. Win at all costs or lose gracefully in high-stakes competition? Gender differences in professional tennis[J]. Journal of Sports Economics, 2016, 17(4): 323-353.

[2] Qiu H, Liu C, Zhang X. [Retracted] Intelligent Design of Tennis Player Training Schedule Based on Big Data of Complexity[J]. Complexity, 2021, 2021(1): 4759395.

[3] Wang Z H, Pan R C, Huang M R, et al. Effects of integrative neuromuscular training combined with regular tennis training program on sprint and change of direction of children[J]. Frontiers in physiology, 2022, 13: 831248.

[4] Coulon T, Barki H, Paré G. Conceptualizing project team momentum: a review of the sports literature[J]. International Journal of Managing Projects in Business, 2021, 14(2): 270-299.

[5] Russomanno T G, Lam H, Knopp M, et al. Within-Match Performance Dynamics–Momentary Strength in Handball[J]. Journal of Human Kinetics, 2021, 79(1): 211-219.

[6] Liu Y, Mu Y, Chen K, et al. Daily activity feature selection in smart homes based on pearson correlation coefficient[J]. Neural Processing Letters, 2020, 51: 1771-1787.

[7] GUO Liang, GUO Zixue, JIA Hongtao, et al. Residents electric larceny detection based on pearson correlation coefficient and SVM[J]; Journal of Hebei University (Natural Science Edition), 2023, 43(04):357-363.

[8] Ali Abd Al-Hameed K. Spearman's correlation coefficient in statistical analysis[J]. International Journal of Nonlinear Analysis and Applications, 2022, 13(1): 3249-3255.

[9] ZHANG Weifeng. Statistical Analysis of Spearman's footrule and Gini’s gamma[D]; Guangdong University of Technology, 2020.

[10] YAO Judeng, YANG Jing, ZHAN Xiaojuan. Feature selection algorithm based on random forest[J]; Journal of Jilin University (Engineering and Technology Edition), 2014, 44 (01):137-141.

[11] WANG Jiaqi, ZHU Junguo, YU Zhengtao. Low-Resource Machine Translation Based on Training Strategy with Changing Gradient Weigh[J/OL]. Journal of Frontiers of Computer Science and Technology:1-10.

[12] LoMonte F D, Suszan B, Dames P. Open and Shut? The Promise-And Problems-Of Government Open Data Portals in Meeting Community Information Needs[J]. UCLA JL & Tech., 2023, 28: 93.