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

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

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Feature Selection to Win the Point of ATP Tennis Players Using Rally Information"}

In tennis, the accumulation of data has progressed and research on tactical analysis has been conducted. Estimating strategically important factors would have...

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

Pubblicato online: 29 giu 2020

Pagine: 37 - 50

DOI: https://doi.org/10.2478/ijcss-2020-0003

Parole chiave
TENNIS, MACHINE LEARNING, LOGISTIC REGRESSION, REGULARIZATION, FEATURE SELECTION

© 2020 Makino M. et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Pubblicato online: 29 giu 2020

Pagine: 37 - 50

DOI: https://doi.org/10.2478/ijcss-2020-0003

Parole chiaveTENNIS, MACHINE LEARNING, LOGISTIC REGRESSION, REGULARIZATION, FEATURE SELECTION

© 2020 Makino M. et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
TENNIS, MACHINE LEARNING, LOGISTIC REGRESSION, REGULARIZATION, FEATURE SELECTION