Predicting Tennis Serve directions with Machine Learning

TL;DR

This paper introduces a machine learning approach to predict professional tennis players' first serve directions, revealing insights into strategic decision-making and the influence of fatigue and context on serve choices.

Contribution

The study develops a novel machine learning method for predicting serve directions and provides evidence of strategic mixed strategies and fatigue effects in professional tennis.

Findings

Prediction accuracy of 49% for men and 44% for women.

Top players may use mixed-strategy serving approaches.

Fatigue influences serve direction choices.

Abstract

Serves, especially first serves, are very important in professional tennis. Servers choose their serve directions strategically to maximize their winning chances while trying to be unpredictable. On the other hand, returners try to predict serve directions to make good returns. The mind game between servers and returners is an important part of decision-making in professional tennis matches. To help understand the players' serve decisions, we have developed a machine learning method for predicting professional tennis players' first serve directions. Through feature engineering, our method achieves an average prediction accuracy of around 49\% for male players and 44\% for female players. Our analysis provides some evidence that top professional players use a mixed-strategy model in serving decisions and that fatigue might be a factor in choosing serve directions. Our analysis also suggests that contextual information is perhaps more important for returners' anticipatory reactions than previously thought.