A Markov Game model for valuing actions, locations, and team performance in ice hockey

Abstract

We apply the Markov Game formalism to develop a context-aware approach to valuing player actions, locations, and team performance in ice hockey. The Markov Game formalism uses machine learning and AI techniques to incorporate context and look-ahead. Dynamic programming is applied to learn value functions that quantify the impact of actions on goal scoring. Learning is based on a massive new dataset, from SportLogiq, that contains over 1.3M events in the National Hockey League. The SportLogiq data include the location of an action, which has previously been unavailable in hockey analytics. We give examples showing how the model assigns context and location aware values to a large set of 13 action types. Team performance can be assessed as the aggregate value of actions performed by the team’s players, or the aggregate value of states reached by the team. Model validation shows that the total team action and state value both provide a strong indicator predictor of team success, as measured by the team’s average goal ratio.

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Notes

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    Pulling the goalie can also result in a skater manpower advantage.

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Acknowledgements

This work was supported by an Engage Grant from the National Sciences and Engineering Council of Canada. We are grateful for constructive discussions in SFU’s Sports Analytics Research Group.

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Correspondence to Oliver Schulte.

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Schulte, O., Khademi, M., Gholami, S. et al. A Markov Game model for valuing actions, locations, and team performance in ice hockey. Data Min Knowl Disc 31, 1735–1757 (2017). https://doi.org/10.1007/s10618-017-0496-z

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Keywords

  • Markov Game
  • Sports analytics
  • National Hockey League
  • Q-learning