Lateral End-Range Movement Profile and Shot Effectiveness During Grand Slam Tennis Match-Play

Eur J Sport Sci. 2025 Feb;25(2):e12250. doi: 10.1002/ejsc.12250.

Abstract

End-range movements are among the most demanding but least understood in the sport of tennis. Using male Hawk-Eye data from match-play during the 2021-2023 Australian Open tournaments, we evaluated the speed, deceleration, acceleration, and shot quality characteristics of these types of movement in men's Grand Slam tennis. Lateral end-range movements that incorporated a change of direction (CoD) were identified for analysis using k-means (end-range) and random forest (CoD) machine learning models. Peak speed, average deceleration into the CoD, average reacceleration out of the CoD, and the quality of the shot played were computed. Players were grouped based on their ATP rankings (top 10, top 50, and outside top 50) to examine the influence of ranking on movement profiles and shot effectiveness. Our data showed that end-range movements profiles of top 10 and top 50 players were characterized by higher peak speed (d = 0.3-0.88), deceleration intensity (d = 0.25-0.63), and acceleration intensity (d = 0.06-0.51) when compared to players outside the top 50 (p < 0.05). Top 10 players also demonstrated greater peak speeds (d = 0.59) and acceleration intensities (d = 0.45) compared to top 50 players (p < 0.05). There was a nonlinear inverse relationship between peak speed and shot quality, such that, as peak speed increased, shot quality decreased-notwithstanding that top 10 players were more likely to hit high-quality shots at higher peak speeds. These results quantify the discrete kinematic characteristics of the sport's most challenging movement sequence and reveal, for the first time, that higher ranked players may possess superior movement potential on court.

Keywords: change of direction; movement analysis; shot quality; tennis performance; tennis ranking.

MeSH terms

  • Acceleration*
  • Adult
  • Athletic Performance* / physiology
  • Australia
  • Biomechanical Phenomena
  • Deceleration
  • Humans
  • Machine Learning
  • Male
  • Movement* / physiology
  • Tennis* / physiology
  • Young Adult