Predicting Tennis Serve directions with Machine Learning

Ying Zhu; Ruthuparna Naikar

cs.LGFeb 26, 2026

Predicting Tennis Serve directions with Machine Learning

Authors: Ying Zhu, Ruthuparna Naikar

Organizations: Georgia State University, Atlanta, USA

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.

Figures & tables

First name	Last name	LR	RF	DT	SVM	NN	MEAN
Novak	Djokovic	0.47	0.50	0.45	0.46	0.47	0.47
Roger	Federer	0.46	0.49	0.44	0.47	0.47	0.47
Nick	Kyrgios	0.55	0.52	0.50	0.55	0.54	0.53
Daniil	Medvedev	0.45	0.49	0.48	0.47	0.46	0.47
Andy	Murray	0.56	0.53	0.58	0.52	0.54	0.55
Rafael	Nadal	0.52	0.50	0.43	0.52	0.52	0.50

Table 1: First Serve Direction Prediction Accuracy for the Deuce Side Serves (men)

First name	Last name	LR	RF	DT	SVM	NN	MEAN
Novak	Djokovic	0.49	0.50	0.44	0.49	0.48	0.48
Roger	Federer	0.56	0.52	0.53	0.59	0.56	0.55
Nick	Kyrgios	0.50	0.49	0.45	0.48	0.48	0.48
Daniil	Medvedev	0.53	0.51	0.53	0.49	0.60	0.53
Andy	Murray	0.48	0.45	0.44	0.48	0.47	0.46
Rafael	Nadal	0.54	0.51	0.45	0.54	0.54	0.52

Table 2: First Serve Direction Prediction Accuracy for the Ad Side Serves (men)

First name	Last name	LR	RF	DT	SVM	NN	MEAN
Victoria	Azarenka	0.47	0.47	0.40	0.49	0.48	0.46
Ashleigh	Barty	0.51	0.45	0.45	0.51	0.48	0.48
Angelique	Kerber	0.37	0.37	0.33	0.40	0.36	0.36
Anett	Kontaveit	0.41	0.43	0.39	0.39	0.39	0.40
Garbine	Muguruza	0.47	0.45	0.40	0.47	0.45	0.45
Naomi	Osaka	0.49	0.41	0.41	0.48	0.45	0.45

Table 3: First Serve Direction Prediction Accuracy for the Deuce Side Serves (women)

First name	Last name	LR	RF	DT	SVM	NN	MEAN
Victoria	Azarenka	0.46	0.41	0.34	0.47	0.43	0.42
Ashleigh	Barty	0.51	0.46	0.45	0.46	0.46	0.47
Angelique	Kerber	0.61	0.58	0.48	0.61	0.60	0.58
Anett	Kontaveit	0.43	0.42	0.42	0.43	0.42	0.42
Garbine	Muguruza	0.39	0.40	0.38	0.37	0.40	0.39
Naomi	Osaka	0.44	0.38	0.36	0.45	0.47	0.42

Table 4: First Serve Direction Prediction Accuracy for the Ad Side Serves (women)

Next-Action Prediction

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Open paper AskPDF arXiv

Table tennis, a dynamic, compact, and popular sport, has received significant attention as a robotics benchmark over the last decades. Most of the research has focused on the rally aspect - returning an incoming ball - requiring high-speed vision, agile motion planning, and tight closed-loop control. However, the other component of table tennis gameplay - the serve - is comparatively a quite unexplored research problem, that in fact requires pushing physics modeling and control to the extremes. Achieving competitive serves with a robot presents domain-specific challenges, such as high-spin generation from a spinless ball, precise aiming, or multi-objective optimization. In this work, we present a novel approach for generating official rule-compliant serves by combining motion primitives, Model Predictive Control, and Bayesian Optimization. Serves generated in this way offer a wide and controllable variation of spins of up to 550 rad/s, and speeds of up to 6.7 m/s, matching and even surpassing those of elite table tennis players.

Abstract

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