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

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

Guiliang Liu, Oliver Schulte

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3442-3448. https://doi.org/10.24963/ijcai.2018/478

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.
Keywords:
Machine Learning: Data Mining
Machine Learning Applications: Applications of Reinforcement Learning