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

Understand

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.