Valuing Actions and Ranking Hockey Players With Machine Learning

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 their aggregate action value. A recent AI-based approach is to measure the value of a player’s action by how much it increases their team’s chance of success, that is, their team’s chance of scoring the next goal. This requires a model that outputs a success probability estimate, given a match context and an action. This talk describes machine learning techniques for building success probability models from data. The techniques range from easy-to-implement probabilistic classifiers to advanced reinforcement learning methods. The results of success probability models are illustrated with action values and player rankings for the National Hockey League.