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 note describes machine learning techniques for building success probability models from sports data. The most advanced model incorporates playing style representations for 1K+ NHL players. The resulting action values, player rankings, and player rankings are illustrated with data from the National Hockey League.