Armstrong Cameron, Peeling Peter, Murphy Alistair, Turlach Berwin A, Reid Machar
School of Human Sciences (Exercise and Sport Science), The University of Western Australia, Perth, Australia.
Department of High Performance, Tennis Australia, Melbourne, Australia.
Eur J Sport Sci. 2025 Feb;25(2):e12250. doi: 10.1002/ejsc.12250.
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.
在网球运动中，极限动作是要求最高但却最不为人所理解的动作之一。我们利用2021 - 2023年澳大利亚网球公开赛比赛期间的男性鹰眼数据，评估了男子大满贯网球中这些类型动作的速度、减速、加速和击球质量特征。使用k均值（极限动作）和随机森林（方向变化）机器学习模型识别并分析了包含方向变化（CoD）的横向极限动作。计算了峰值速度、进入方向变化时的平均减速、离开方向变化时的平均再加速以及击球质量。根据球员的ATP排名（前10、前50和前50以外）进行分组，以研究排名对动作特征和击球效果的影响。我们的数据显示，与排名前50以外的球员相比，排名前10和前50的球员的极限动作特征表现为更高的峰值速度（d = 0.3 - 0.88）、减速强度（d = 0.25 - 0.63）和加速强度（d = 0.06 - 0.51）（p < 0.05）。与排名前50的球员相比，排名前10的球员还表现出更高的峰值速度（d = 0.59）和加速强度（d = 0.45）（p < 0.05）。峰值速度与击球质量之间存在非线性反比关系，即随着峰值速度增加，击球质量下降——尽管排名前10的球员在更高的峰值速度下更有可能打出高质量的击球。这些结果量化了这项运动中最具挑战性的动作序列的离散运动学特征，并首次揭示出排名较高的球员在球场上可能拥有更出色的动作潜力。
