Quantifying Tennis Player Performance: A Linear Regression Approach

Yuxi Zeng; Siwei Zhong

doi:10.62051/txzhx330

Authors

Yuxi Zeng
Siwei Zhong

DOI:

https://doi.org/10.62051/txzhx330

Keywords:

Quantifying Tennis Player Performance; Linear Regression; Random Forest Feature Analysis; Least Squares Method; Athlete Performance Evaluation.

Abstract

This paper uses linear regression to quantitatively analyse the performance of players in the men's singles competition at Wimbledon 2023. Firstly, the data is processed by observationally analysing the match data to ensure compliance with the tournament standards and regulations. Next, key metrics were extracted, including short-term and long-term metrics, as well as the introduction of Serve Indicator to consider the impact of serve advantage on player performance. Then, the most important independent variables were identified through Random Forest feature analysis and parameters were calculated using least squares to construct performance indicators for use in linear regression. Finally, through data visualisation and analysis, it was found that player 1 usually performs better at critical moments, showing greater stability and consistency, while player 2 shows greater variability and unpredictability. Overall, the linear regression method in this paper is valuable and practical for quantifying tennis players' performance, and can provide a reference for players and coaches to help them better analyse and improve their performance.

Downloads

Download data is not yet available.

References

Kramer, T., Huijgen, B. C., Elferink-Gemser, M. T., & Visscher, C. (2017). Prediction of tennis performance in junior elite tennis players. Journal of sports science & medicine, 16(1), 14.

Kovalchik, S. A. (2016). Searching for the GOAT of tennis win prediction. Journal of Quantitative Analysis in Sports, 12(3), 127-138.

Whiteside, D., Cant, O., Connolly, M., & Reid, M. (2017). Monitoring hitting load in tennis using inertial sensors and machine learning. International journal of sports physiology and performance, 12(9), 1212-1217.

Cui, Y., Gómez, M. Á., Gonçalves, B., & Sampaio, J. (2018). Performance profiles of professional female tennis players in grand slams. PloS one, 13(7), e0200591.

Signorile, J. F., Sandler, D. J., Smith, W. N., Stoutenberg, M., & Perry, A. C. (2005). Correlation analyses and regression modeling between isokinetic testing and on-court performance in competitive adolescent tennis players. The Journal of Strength & Conditioning Research, 19(3), 519-526.

Buszard, T., Reid, M., Krause, L., Kovalchik, S., & Farrow, D. (2017). Quantifying contextual interference and its effect on skill transfer in skilled youth tennis players. Frontiers in psychology, 8, 1931.

Triolet, C., Benguigui, N., Le Runigo, C., & Williams, A. M. (2013). Quantifying the nature of anticipation in professional tennis. Journal of Sports Sciences, 31(8), 820-830.

Cui, Y., Gómez, M. Á., Gonçalves, B., Liu, H., & Sampaio, J. (2017). Effects of experience and relative quality in tennis match performance during four Grand Slams. International Journal of Performance Analysis in Sport, 17(5), 783-801.

Hayes, M. J., Spits, D. R., Watts, D. G., & Kelly, V. G. (2021). Relationship between tennis serve velocity and select performance measures. The Journal of Strength & Conditioning Research, 35(1), 190-197.

Keaney, E. M., & Reid, M. (2018). Quantifying hitting activity in tennis with racket sensors: new dawn or false dawn?. Sports Biomechanics.

Cui, Y., Gómez, M. Á., Gonçalves, B., & Sampaio, J. (2019). Clustering tennis players’ anthropometric and individual features helps to reveal performance fingerprints. European journal of sport science, 19(8), 1032-1044.

Fett, J., Ulbricht, A., & Ferrauti, A. (2020). Impact of physical performance and anthropometric characteristics on serve velocity in elite junior tennis players. The Journal of Strength & Conditioning Research, 34(1), 192-202.