title: Spatial characteristics of professional tennis serves with implications for serving aces: A machine learning approach.
abstract: This study sought to determine the features of an ideal serve in men's professional tennis. A total of 25,680 first serves executed by 151 male tennis players during Australian Open competition were classified as either aces or returned into play. Spatiotemporal (impact location, speed, projection angles, landing location and relative player locations) and contextual (score) features of each serve were extracted from Hawk-Eye data and used to construct a classification tree model (with decision rules) that predicted serve outcome. k-means clustering was applied to the landing locations to quantify optimal landing locations for aces. The classification tree revealed that (1) serve directionality, relative to the returner; (2) the ball's landing proximity to the nearest service box line and (3) serve speed classified aces with an accuracy of 87.02%. Hitting aces appeared more contingent on accuracy than speed, with serves directed >5.88° from the returner and landing <15.27 cm from a service box line most indicative of an ace. k-means clustering revealed four distinct locations (≈0.73 m wide × 2.35 m deep) in the corners of the service box that corresponded to aces. These landing locations provide empirically derived target locations for players to adhere to during practice and competition.
authors.affiliation: a Game Insight Group , Tennis Australia , Melbourne , Australia.|b Institute of Sport, Exercise and Active Living , Victoria University , Melbourne , Australia.
authors.forename: David
authors.initials: D
authors.lastname: Whiteside
authors.affiliation: a Game Insight Group , Tennis Australia , Melbourne , Australia.|c School of Sport Science, Exercise and Health , University of Western Australia , Crawley , Australia.
authors.forename: Machar
authors.initials: M
authors.lastname: Reid
mesh_terms: D054874:Athletic Performance; D001315:Australia; D001696:Biomechanical Phenomena; D016000:Cluster Analysis; D003163:Competitive Behavior; D006801:Humans; D000069550:Machine Learning; D008297:Male; D009048:Motor Skills; D009068:Movement; D010806:Physical Education and Training; D062206:Spatial Analysis; D013037:Spatial Behavior; D013647:Task Performance and Analysis; D013715:Tennis
keywords: Coaching; ace; biomechanics; decision tree; k-means; rule induction