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[2602.22527] Predicting Tennis Serve directions with Machine Learning
Summary
The paper explores a machine learning approach to predict tennis serve directions, achieving around 49% accuracy for male players and 44% for female players, highlighting the strategic decision-making in professional tennis.
Why It Matters
Understanding serve prediction in tennis can enhance training methods and strategies for players and coaches. The findings suggest that serve direction choices are influenced by player fatigue and contextual factors, which could inform game tactics and player performance analysis.
Key Takeaways
- Machine learning can predict tennis serve directions with moderate accuracy.
- Top players may employ mixed-strategy models in their serving decisions.
- Fatigue and contextual information significantly impact serve direction choices.
Computer Science > Machine Learning arXiv:2602.22527 (cs) [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 (experimental) 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]...
