Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation

TL;DR

This paper introduces a deep reinforcement learning approach to evaluate hockey players by modeling game context and actions, resulting in a new metric that correlates with success and future salary.

Contribution

It presents a novel DRL-based method for context-aware player evaluation in hockey, integrating game signals and history to produce a meaningful impact metric.

Findings

GIM correlates highly with success measures

GIM is consistent across a season

GIM predicts future salary effectively

Abstract

A variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player performance depends on the game context. This paper proposes a new approach to capturing game context: we apply Deep Reinforcement Learning (DRL) to learn an action-value Q function from 3M play-by-play events in the National Hockey League (NHL). The neural network representation integrates both continuous context signals and game history, using a possession-based LSTM. The learned Q-function is used to value players' actions under different game contexts. To assess a player's overall performance, we introduce a novel Game Impact Metric (GIM) that aggregates the values of the player's actions. Empirical Evaluation shows GIM is consistent throughout a play season, and correlates highly with standard success measures and future salary.