Data-driven analysis of point-by-point performance for male tennis player in Grand Slams


  • Yixiong Cui Beijing Sport University
  • Haoyang Liu AI Sports Engineering Lab, Beijing Sport University
  • Hongyou Liu School of Physical Education and Sports Science, South China Normal University
  • Miguel-Ángel Gómez Facultad de la Actividad Física y del Deporte (INEF), Universidad Politécnica de Madrid



Tennis is an individual sport that requires a specialized training and match preparation for every player. Former studies in tennis have tried many approaches to analyze player’s performance using descriptive statistics (such as: match time, rally duration, or game number) and match-related statistics (such as: first and second serve percentage, aces, double faults, or net points won). Although helpful in providing general information of match characteristics and evaluating player performance, there is scarce consideration over how elite male players behave on point-by-point basis according to different contextual variables. This study aimed to assess predictors of point outcome (win/lose) related to year, tournament types, round, set, quality of opposition, game status, serve and rally by using match data of 2011-2016 four Grand Slam. A total of 29675 points were recorded and analyzed through classification tree analysis (exhaustive CHAID). The results showed that the performance of tennis player was conditioned by the familiarity with court surfaces as well as other contextual variables, such as game type, quality of opposition, match status, serve and return and rally length (p< 0.05). These results provide insight for coaches and players when planning the game strategy, allowing more appropriate tactics under different game status.






Original Article