Predicting Tennis Serve directions with Machine Learning
#Tennis Serve Prediction#Machine Learning#Sports Analytics#Mixed-Strategy Model#Professional Tennis#Fatigue Analysis#Contextual Information
📌 Key Takeaways
Researchers developed a machine learning method to predict tennis serve directions with 49% accuracy for male players and 44% for female players
Top professional players appear to use mixed-strategy models for their serve decisions
Fatigue may influence serve direction choices in professional tennis matches
Contextual information plays a more significant role in returners' reactions than previously thought
📖 Full Retelling
Researchers Ying Zhu and Ruthuparna Naikar published a machine learning study on February 26, 2026, that demonstrates how to predict professional tennis players' first serve directions, aiming to better understand the strategic decision-making process in professional tennis matches. The study focuses on the crucial importance of serves in tennis, particularly first serves, where servers strategically choose directions to maximize winning chances while attempting to remain unpredictable. Meanwhile, returners try to predict these directions to make effective returns, creating a mental game that significantly impacts match outcomes. The researchers developed a sophisticated machine learning method that, through advanced feature engineering, achieved an average prediction accuracy of approximately 49% for male players and 44% for female players in professional tennis matches. Their analysis provides evidence that top professional players utilize a mixed-strategy model for their serve decisions and suggests that physical fatigue may play a role in choosing serve directions during matches. Additionally, the study indicates that contextual information might be more influential in returners' anticipatory reactions than previously understood in sports science.
🏷️ Themes
Machine Learning, Sports Analytics, Strategic Decision Making
Sports analytics are collections of relevant historical statistics that can provide a competitive advantage to a team or individual by helping to inform players, coaches and other staff and help facilitate decision-making both during and prior to sporting events. The term "sports analytics" was popu...
Study of algorithms that improve automatically through experience
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances i...
No entity connections available yet for this article.
Original Source
--> Computer Science > Machine Learning arXiv:2602.22527 [Submitted on 26 Feb 2026] Title: Predicting Tennis Serve directions with Machine Learning Authors: Ying Zhu , Ruthuparna Naikar View a PDF of the paper titled Predicting Tennis Serve directions with Machine Learning, by Ying Zhu and 1 other authors View PDF HTML 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. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22527 [cs.LG] (or arXiv:2602.22527v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.22527 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: MLSA 2022, Communications in Computer and Information Science , Springer, 2023, pp. 89-100 Related DOI : https://doi.org/10.1007/978-3-031-27527-2_7 Focus to learn more DOI linking to related resources Submission history From: Ruthuparna Naikar [ view email ] [v1] Thu, 26 Feb 2026 01:56:40 UTC (13 KB) Full-text links: Access Paper: View a PDF of the paper title...
