A shot taxonomy in the era of tracking data in professional tennis

Shots are an essential part of the language of tennis yet little is known about the distinct types of shots in the professional game. In this study, we build a taxonomy of shots for elite tennis players using tracking data from multiple years of men's and women's matches at the Australian Open. Our taxonomy is constructed using model-based multi-stage functional data clustering, an unsupervised machine learning approach. Among 270,023 men's and 178,136 women's shots, we found 13 distinct types of serves to both the Ad and Deuce court for male players and 17 and 15 types to the Ad and Deuce for female players. More variety was found among serve returns and rally shots compared to the serve; with less variety on the backhand than forehand. There was also more overlap in the physical characteristics of groundstroke shots between male and female players than on serve. Shot type was strongly associated with winning points and shots in the highest speed and lowest net clearance categories tended to be the most effective. This data-driven dictionary of shots provides a framework for analysis of elite player performance, characterizing playing style, and designing more representative practice.