Player categorization based on playing style is a highly important task in professional ice hockey, aiding scouting, player development, and strategic decision-making. Traditional methods often rely on simple metrics like goals or assists, which fail to capture the full complexity of a player’s style and contributions. Motivated by the increasing availability of detailed event data and advances in machine learning based modeling techniques, this paper explores a richer, data-driven approach to player categorization. We build on recent work in player vector representations and apply Gaussian Mixture Models (GMMs) to cluster forwards and defenders based on event data from five seasons of the Swedish Hockey League (SHL). Our contributions are threefold: (1) we construct detailed player vectors that summarize a wide range of offensive and defensive skills, (2) we apply GMMs to identify soft clusters of players, allowing for nuanced overlapping playing styles, and (3) we analyze the resulting clusters to interpret distinct player profiles and provide concrete examples. Our results offer a more flexible and realistic view of player roles, reflecting the continuous and multi-dimensional nature of playing styles. The approach helps enhance talent evaluation and roster building, and offers an efficient framework for future analyses across leagues and seasons.