Exercise Training for Competitive Tennis

Quantifying Change of Direction Movement Demands in Professional Tennis Matchplay: An Analysis From the Australian Open Grand Slam

ABSTRACT

Giles, B, Peeling, P, and Reid, M. Quantifying change of direction movement demands in professional tennis matchplay: An analysis from the Australian Open Grand Slam. J Strength Cond Res 38(3): 517-525, 2024-Change of direction (COD) contributes significantly to the movement repertoire of professional tennis players, yet the time-motion and degree demands of these changes are poorly understood. This study examines the velocity, acceleration, and angular displacement profiles of COD movements in modern professional tennis. One hundred eighty-two singles matches of Hawk-Eye player tracking data collected from the Australian Open Grand Slam were used for analysis. A novel COD classification algorithm was used to identify >120,000 medium and high-intensity CODs for analysis. Descriptive characteristics of the COD performance were calculated using player coordinate and time variables. Sex comparisons were analyzed using 2 mixed-effects models assessed for differences via likelihood ratios. Players performed 1.6 CODs per point. Both sexes executed, on average, 1.3-1.4 shots and covered 4.8 m per COD, with men performing changes every 2.7 seconds and women every 3.1 seconds. Medium-intensity COD comprised 88-94% of all identified changes. Approximately 2 in 3 CODs involved a degree of change >105°, whereas cutting maneuvers (<45°) were most commonly high-intensity COD. This study is the first to quantify the COD characteristics of professional tennis matchplay. Both sexes performed the same average number of CODs per point, however, men executed high-intensity changes twice as frequently as women, at an average of 1 every 5 points. These novel findings will help to improve the specificity of training interventions in elite tennis conditioning.

Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach

PURPOSE

Recent explorations of tennis-specific movements have developed contemporary methods for identifying and classifying changes of direction (COD) during match-play. The aim of this research was to employ these new analysis techniques to objectively explore individual nuance and style factors in the execution of COD movements in professional tennis.

METHODS

Player tracking data from 62 male and 77 female players at the Australian Open Grand Slam were analysed for COD movements using a model algorithm, with a sample of 150,000 direction changes identified. Hierarchical clustering methods were employed on the time-motion and degree characteristics of these direction changes to identify groups of different COD performers.

RESULTS

Five unique clusters, labelled "Cutters", "Gear Changers", "Lateral Changers", "Balanced Changers" and "Passive Changers" were identified in accordance with their varying speed, acceleration, degree and directionality of change features.

CONCLUSIONS

Player COD clustering challenge previously held assumptions regarding on-court movement style, highlighting the complexity and variation in the sport's locomotion demands. In practice, the speed, acceleration, directionality and degree of change characteristics of each COD style can facilitate athlete profiling and the specificity of training interventions.HighlightsWe used machine learning techniques and cluster analysis methodology to explore the time motion characteristics of direction change skill in professional tennis.We present five unique types of change of direction style in professional tennis players. These include "Cutters", "Gear Changers", "Lateral Changers", "Balanced Changers" & "Passive Changers". These style classifications were established in accordance with their varying speed, acceleration, degree and directionality of change features.We show that the application of machine learning techniques to player tracking data can facilitate a more intricate understanding the sport's physical demands, which can be used to inform training programme design.

A machine learning approach for automatic detection and classification of changes of direction from player tracking data in professional tennis

The purpose of this study was to develop an automated method for identifying and classifying change of direction (COD) movements in professional tennis using tracking data. Three sport science and strength and conditioning experts coded match-play footage of nineteen professional tennis players (9 male and 10 female) from the Australian Open Grand Slam for COD of medium and high intensity. A total of 1,494 changes were identified and aligned with 2D player position sampled at 25 Hz based on camera tracking data. Several machine learning classifiers were trained and tested on a set of 1,128 time-motion features. A random forest algorithm was found to have the best out-of-sample performance, classifying medium and high intensity changes with an F1-score of 0.729. This research offers a novel and applicable way for utilising player tracking data and machine learning techniques to automatically identify and classify COD movements in professional tennis.

Iliopsoas and gluteal muscles are asymmetric in tennis players but not in soccer players

PURPOSE

To determine the volume and degree of asymmetry of iliopsoas (IL) and gluteal muscles (GL) in tennis and soccer players.

METHODS

IL and GL volumes were determined using magnetic resonance imaging (MRI) in male professional tennis (TP) and soccer players (SP), and in non-active control subjects (CG) (n = 8, 15 and 6, respectively).

RESULTS

The dominant and non-dominant IL were hypertrophied in TP (24 and 36%, respectively, P<0.05) and SP (32 and 35%, respectively, P<0.05). In TP the asymmetric hypertrophy of IL (13% greater volume in the non-dominant than in the dominant IL, P<0.01) reversed the side-to-side relationship observed in CG (4% greater volume in the dominant than in the contralateral IL, P<0.01), whilst soccer players had similar volumes in both sides (P = 0.87). The degree of side-to-side asymmetry decreased linearly from the first lumbar disc to the pubic symphysis in TP (r = -0.97, P<0.001), SP (r = -0.85, P<0.01) and CG (r = -0.76, P<0.05). The slope of the relationship was lower in SP due to a greater hypertrophy of the proximal segments of the dominant IL. Soccer and CG had similar GL volumes in both sides (P = 0.11 and P = 0.19, for the dominant and contralateral GL, respectively). GL was asymmetrically hypertrophied in TP. The non-dominant GL volume was 20% greater in TP than in CG (P<0.05), whilst TP and CG had similar dominant GL volumes (P = 0.14).

CONCLUSIONS

Tennis elicits an asymmetric hypertrophy of IL and reverses the normal dominant-to-non-dominant balance observed in non-active controls, while soccer is associated to a symmetric hypertrophy of IL. Gluteal muscles are asymmetrically hypertrophied in TP, while SP display a similar size to that observed in controls. It remains to be determined whether the different patterns of IL and GL hypertrophy may influence the risk of injury.