1. bookVolume 19 (2020): Issue 1 (July 2020)
Journal Details
License
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English
access type Open Access

Feature Selection to Win the Point of ATP Tennis Players Using Rally Information

Published Online: 29 Jun 2020
Volume & Issue: Volume 19 (2020) - Issue 1 (July 2020)
Page range: 37 - 50
Journal Details
License
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English
Abstract

In tennis, the accumulation of data has progressed and research on tactical analysis has been conducted. Estimating strategically important factors would have the benefit of providing players with useful advice and helping audience members understand what tennis players are good at. Previous research has been conducted into ways of predicting Association of Tennis Professionals (ATP) tennis match outcomes as well as estimating factors that are important for victories using machine learning models. The challenge of previous research is that the victory factor lacks concreteness. Since we thought the root of the abovementioned problem was that previous researchers used game summary as a feature and did not consider the process of rallies between points, this research focused on calculating the frequency of single shots, two-shot patterns, and specific effective shot patterns from each point rally of ATP singles matches. We then used those data to predict point winners and useful features using L1-regularized logistic regression. The highest accuracy obtained was 66.5%, and the area under the curve (AUC) was 0.689. The most prominent feature we found was the ratio of specific shots by specific players. From these results, our method could reveal more concretely tactical factors than previous studies.

Keywords

Clarke, S. R., & Dyte, D. (2000). Using official ratings to simulate major tennis tournaments. International transactions in operational research, 7(6), 585-594.10.1111/j.1475-3995.2000.tb00218.xSearch in Google Scholar

Clowes, S., Cohen, G. L., & Tomljanovic, L. (2002). Dynamic evaluation of conditional probabilities of winning a tennis match. In Sixth Australian Conference on Mathematics and Computers in Sport. UTS.Search in Google Scholar

de Araujo Fernandes, M. (2017). Using Soft Computing Techniques for Prediction of Winners in Tennis Matches. Machine Learning Research, 2(3), 86-98.Search in Google Scholar

Klaassen, F. J., & Magnus, J. R. (2003). Forecasting the winner of a tennis match. European Journal of Operational Research, 148(2), 257-267.10.1016/S0377-2217(02)00682-3Search in Google Scholar

Knottenbelt, W. J., Spanias, D., & Madurska, A. M. (2012). A common-opponent stochastic model for predicting the outcome of professional tennis matches. Computers & Mathematics with Applications, 64(12), 3820-3827.10.1016/j.camwa.2012.03.005Search in Google Scholar

Ma, S. M., Liu, C. C., Tan, Y., & Ma, S. C. (2013). Winning matches in Grand Slam men’s singles: An analysis of player performance-related variables from 1991 to 2008. Journal of sports sciences, 31(11), 1147-1155.10.1080/02640414.2013.77547223458278Search in Google Scholar

McHale, I., & Morton, A. (2011). A Bradley-Terry type model for forecasting tennis match results. International Journal of Forecasting, 27(2), 619-630.10.1016/j.ijforecast.2010.04.004Search in Google Scholar

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.Search in Google Scholar

Sipko, M., & Knottenbelt, W. (2015). Machine learning for the prediction of professional tennis matches. MEng computing-final year project, Imperial College London.Search in Google Scholar

Wei, X., Lucey, P., Morgan, S., Reid, M., & Sridharan, S. (2016). The thin edge of the wedge:Accurately predicting shot outcomes in tennis using style and context priors. In Proceedings of the 10th Annu MIT Sloan Sport Anal Conf, Boston, MA, USA (pp.1-11).Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo