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Related Experiment Video

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Causal effect analysis of serving performance using double machine learning.

Jiacai Ma1, Fuzhu Zou2

  • 1Department of Sports Science Convergence, Graduate School, Dankook University, Yongin-Si, Gyeonggi-do, 16890, Republic of Korea.

BMC Sports Science, Medicine & Rehabilitation
|November 29, 2025
PubMed
Summary

Ace rate positively impacts tennis match outcomes, while other serve metrics have minor effects. Adapting serve strategies is key for winning probability in professional men's tennis.

Keywords:
Causal inferenceDouble machine learningMatch win probabilityServing performanceTennis analytics

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Area of Science:

  • Sports Science
  • Tennis Analytics
  • Performance Analysis

Background:

  • Serving performance is crucial for match outcomes in professional tennis.
  • Quantifying the causal impact of specific serve metrics on winning probability is essential for strategic development.

Purpose of the Study:

  • To estimate the causal effects of ace rate, first serve win rate, first serve in rate, and double fault rate on winning probability in ATP men's singles matches.
  • To provide data-driven insights for optimizing serve strategies in professional tennis.

Main Methods:

  • Analysis of ATP men's singles match data from 2013-2024.
  • Estimation of causal effects using statistical modeling across various serve indicators.

Main Results:

  • Ace rate demonstrates a modest positive causal association with winning probability.
  • First serve win rate and first serve in rate show context-dependent, statistically small impacts.
  • Double fault rate effects on winning probability are limited and statistically insignificant.

Conclusions:

  • Serve strategies, particularly ace rate, significantly influence match outcomes, though impacts vary by context.
  • Findings underscore the need for adaptive serving strategies across different surfaces, player rankings, and tournament levels.
  • Future research should extend analysis to WTA and mixed competitions for broader generalizability.