Title: What is the value of an action in ice hockey? Deep Reinf orcement Learning for Context-Aware Player Evaluation Abstract: A fundamental goal of sports analytics is to rank player performance. A c ommon approach is to assign a value to each player action and rank a playe r by his or her aggregate action value. For measuring the value of an acti on, 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 o f scoring the next goal). We introduce a high-resolution neural network re presentation of the expected action value, which integrates both continuo us context signals and the recent match history. Deep Reinforcement Learn ing is used to learn an action-value Q function from 3M play-by-play event s in the National Hockey League (NHL). Empirical evaluation shows that theresulting player ranking is consistent throughout a play season, and cor relates highly with standard success measures and future salary. A full ve rsion of the paper is available at https://www.ijcai.org/proceedings/2018/ 478. Bio: Oliver Schulte is a Professor in the School of Computin g Science at Simon Fraser University, Vancouver, Canada. He received hisPh.D. from Carnegie Mellon University in 1997. Current research focuses o n machine learning for structured data, such as events, networks, and r elational databases. He has published papers in leading AI and machine lea rning venues on a variety of topics, including sports analytics, learnin g Bayesian networks, learning theory, game theory, and scientific disco very. While he has won some nice awards, his biggest claim to fame may bea draw against chess world champion Gary Kasparov. Guiliang Liu is aPh.D. candidate at Simon Fraser University working in reinforcement learn ing.
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