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

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.