Clustering tennis players' anthropometric and individual features helps to reveal performance fingerprints.

The study was aimed to explore distinct players' groups according to their anthropometric and individual features, and to identify the key performance indicators that discriminate player groups. Match statistics, anthropometric and personal features of 1188 male players competing during 2015-2017 main draw Grand Slam singles events were collected. Height, weight, experience, handedness and backhand style were used to automatically classify players into different clusters through unsupervised learning model. Afterwards, 29 match variables were analysed through MANOVA and discriminant analysis in order to evaluate the different match performance among player groups and to identify the key performance indicators that best differentiate player clusters in each Grand Slam. The analysis revealed the existence of four clusters, they were classified as Big-sized Right Two-handed Players ( = 387), Medium-sized Right One-handed Players ( = 265), Small-sized Right Two-handed Players ( = 414), and Left Two-handed Players ( = 122). Serve, winner, net and physical performance-related indicators (Structure Coefficient ≥ |0.30|) were showed to be the maximum contributors to the group separation. Left-handed players were the most homogenous group in performance. Taller players outperformed their peers in all Slams except for Roland Garros, where left-handed players demonstrated certain advantage playing on slow-pace surface. In Wimbledon and US Open, Medium-sized Right One-handed Players showed better net and physical performance. The advantage of left-handed player is over-represented at elite level. Current findings promote a better understanding of match-play from distinct player groups and offer information on evaluating contextual variability for achieving better performances.