AI & Fundamentals
What is the value of an action in ice hockey? Deep Reinforcement Learning for Context-Aware Player Evaluation - Oliver Schulte and Guiliang Liu, Simon Fraser University

DATE: Mon, March 25, 2019 - 3:00 pm

LOCATION: Computer Science Building - X836, 2366 Main Mall, V6T 1Z4


Speakers: Oliver Schulte and Guiliang Liu, Simon Fraser University

Abstract: A fundamental goal of sports analytics is to rank player performance.  A common approach is to assign a value to each player action and rank a player by his or her aggregate action value. For measuring the value of an action, a recent AI-based approach, successful in a variety of team sports, estimates its expected impact on team success (e.g., the team’s chance of scoring the next goal). We introduce a high-resolution neural network representation of the expected action value, which integrates both continuous context signals and the recent match history.  Deep Reinforcement Learning is used to learn an action-value Q function from 3M play-by-play events in the National Hockey League (NHL). Empirical evaluation shows that the resulting player ranking is consistent throughout a play season, and correlates highly with standard success measures and future salary. A full version of the paper is available at


Bio: Oliver Schulte is a Professor in the School of Computing Science at Simon Fraser University, Vancouver, Canada. He received his Ph.D. from Carnegie Mellon University in 1997. Current research focuses on machine learning for structured data, such as events, networks, and relational databases. He has published papers in leading AI and machine learning venues on a variety of topics, including sports analytics, learning Bayesian networks, learning theory, game theory, and scientific discovery. While he has won some nice awards, his biggest claim to fame may be a draw against chess world champion Gary Kasparov.

Guiliang Liu is a Ph.D. candidate at Simon Fraser University working in reinforcement learning.

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