Causal Effect Analysis of Serving Performance Using Double Machine Learning

Causal Effect Analysis of Serving Performance Using Double Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal Effect Analysis of Serving Performance Using Double Machine Learning JIACAI MA, FUZHU ZOU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7324500/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 9 You are reading this latest preprint version Abstract Serving performance is widely recognized as a critical factor influencing match outcomes in professional tennis. To assess its true contribution to winning probability, this study applies Double Machine Learning (DML) to 2013–2024 ATP men’s singles match data, estimating the causal effects of four key serve-related indicators: ace rate, first serve win rate, first serve in rate, and double fault rate. The analysis identifies both Average Treatment Effects (ATE) and Conditional Average Treatment Effects (CATE). Results show that ace rate exhibits a consistent negative causal effect, suggesting potential drawbacks of ace-based strategies. First serve win rate displays strong positive effects on grass courts and among lower-ranked players, while first serve in rate has a stable positive impact, especially on clay surfaces and in mid-tier tournaments. Double fault rate effects are generally insignificant. The robustness of the estimates is confirmed through placebo tests and subsample validations. These findings provide data-driven insights for optimizing serve strategies in professional tennis under varied competitive conditions. Serving performance Match win probability Causal inference Double Machine Learning Tennis analytics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 23 Sep, 2025 Reviews received at journal 19 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 13 Aug, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 08 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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