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

Abstract

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.