What is the Value of an Action in Ice Hockey? Q-Learning for the NHL

نویسندگان

  • Oliver Schulte
  • Zeyu Zhao
  • Kurt Routley
چکیده

Abstract. Recent work has applied the Markov Game formalism from AI to model game dynamics for ice hockey, using a large state space. Dynamic programming is used to learn action-value functions that quantify the impact of actions on goal scoring. Learning is based on a massive dataset that contains over 2.8M events in the National Hockey League. As an application of the Markov model, we use the learned action values to measure the impact of player actions on goal scoring. Players are ranked according to the aggregate goal impact of their actions. We show that this ranking is consistent across across seasons, and compare it with previous player metrics, such as plus-minus and total points.

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تاریخ انتشار 2015