This project uses unsupervised learning to group ATP players from the 2023 season based on season-level performance data. By analyzing win rates, surface performance, match volume, and opponent ranking, players are segmented using clustering methods. The results show that tennis performance involves multiple dimensions and cannot be explained by ranking alone.