Understanding the role of a particular player, or set of players, in a team is an important tool for players, scouts, and managers, as it can improve training, game adjustments and team construction. In this paper, we propose a probabilistic method for quantifying player roles in ice hockey that allows for a player to belong to different roles with some probability. Using data from the 2021-2022 NHL season, we analyze and group players into clusters. We show the use of the clusters by an examination of the relationship between player role and contract, as well as between role distribution in a team and team success in terms of reaching the playoffs.
© Copyright 2024 Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science. Veröffentlicht von Springer. Alle Rechte vorbehalten.