Apples-to-Apples: Clustering and Ranking NHL Players Using Location Information and Scoring Impact

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Using new game events and location data, we introduce a player performance assessment system that supports drafting, trading, and coaching decisions in the NHL. Players who tend to play in similar locations are clustered together using machine learning techniques, which capture similarity in styles and roles. Clustering players avoids apples-to-oranges comparisons, like comparing offensive and defensive players. Within each cluster, players are ranked according to how much their actions impact their team’s chance of scoring the next goal.  Our player ranking is based on assigning location-dependent values to actions. A high-resolution Markov model also pinpoints the game situations and rink locations in which players tend to do actions with exceptionally high/low values.

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