Serve Statistics and Match Outcomes in Tennis: Separating Descriptive Correlates from Genuine Predictors Using Simulation

Samyak Shah

Serve Statistics and Match Outcomes in Tennis: Separating Descriptive Correlates from Genuine Predictors Using Simulation

Author(s)	Samyak Shah
Country	India
Abstract	Statistics including first-serve percentage and aces and service points are frequently given as drivers of tennis performance, and machine-learning research frequently classifies match winners based on them. Since these statistics are cumulative up to the time of the match they are supposed to account, they can reflect the outcome but not forecast the outcome. The paper measures such difference through a point-by-point Monte Carlo simulation of 200 seasons (64 players, 50 rounds, best-of-three matches), where the attributes of serve, return and second-serves of each player are the drivers of every point. Five sets of KPI were assessed in logistic regression, linear discriminant analysis, decision trees and support machine with 5-fold cross-validation at n = 90, 180, 360 and 1,000 matches. At n = 180, logistic regression was 90.6% accurate with all post-match KPIs and 86.8% with post-match serve statistics alone, but only 59.2% with pre-match rolling serve statistics (majority-class baseline 53.0%). A pre-match win-rate test score was 66.9 and its inclusion in a test score did not assist (-1.5 points). KPI set accounted 91.4% of accuracy variance; algorithm accounted 1.1%. The choice of features prior to cross-validation resulted in 60.8 percent accuracy using pure noise. Serve statistics are good descriptive correlates of winning but poor prospective predictors; we give a reporting checklist and open code.
Keywords	tennis analytics; serve statistics; data leakage; match outcome prediction; cross-validation; Monte Carlo simulation
Published In	Volume 8, Issue 6, November-December 2026
Published On	2026-11-07

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Serve Statistics and Match Outcomes in Tennis: Separating Descriptive Correlates from Genuine Predictors Using Simulation

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