Dataset for: Match analysis and probability of winning a point in elite men’s singles tennis

This dataset supports the study “Match analysis and probability of winning a point in elite men’s singles tennis” published in PLOS ONE (Prieto-Lage et al., 2023; https://doi.org/10.1371/journal.pone.0286076). The research analyses the probability of winning a point in elite men’s professional tennis based on key performance variables, using singles matches from the final rounds (from the quarter-finals onwards) of three Grand Slam tournaments in the 2021 season (Roland Garros, Wimbledon, and US Open), covering three court surfaces (clay, grass, and hard court).Using a structured observational methodology and the OBSTENNIS-S21 instrument, a total of 4,669 points were systematically coded. Data were recorded using LINCE PLUS software, and statistical analyses were conducted in IBM SPSS Statistics (v25.0). Variables include court surface, type of serve (first/second, including aces), rally length (short/medium/long), bounce zone, finish zone, point winner (server/returner), point ending (winner/forced error/unforced error), and final stroke characteristics, enabling replication of the descriptive and chi-square analyses and the probability estimates reported in the article.The repository contains a single file in IBM SPSS Statistics format (.sav) titled “Season 2021 - MALE”, which enables replication of the statistical analyses and facilitates further research on serve effectiveness, rally structure, surface-specific patterns, and combined play patterns associated with point outcomes in elite men’s singles tennis.