Not All Goals Are Created Equal : Evaluating Hockey Players in the NHL Using Q-Learning with a Contextual Reward Function

• Main Author: Vik, Jon
• Format: Others
• Language: English
• Published: Linköpings universitet, Databas och informationsteknik 2021
• Subjects: Sports Analytics; Markov Game; Machine Learning; Reinforcement Learning; Q-Learning; Data Mining; National Hockey League; Ice Hockey; Reward Function; Player Evaluation; Other Computer and Information Science; Annan data- och informationsvetenskap; Computer and Information Sciences; Data- och informationsvetenskap
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175149

Not all goals in the game of ice hockey are created equal: some goals increase the chances of winning more than others. This thesis investigates the result of constructing and using a reward function that takes this fact into consideration, instead of the common binary reward function. The two reward functions are used in a Markov Game model with value iteration. The data used to evaluate the hockey players is play-by-play data from the 2013-2014 season of the National Hockey League (NHL). Furthermore, overtime events, goalkeepers, and playoff games are excluded from the dataset. This study finds that the constructed reward, in general, is less correlated than the binary reward to the metrics: points, time on ice and, star points. However, an increased correlation was found between the evaluated impact and time on ice for center players. Much of the discussion is devoted to the difficulty of validating the results from a player evaluation due to the lack of ground truth. One conclusion from this discussion is that future efforts must be made to establish consensus regarding how the success of a hockey player should be defined.