The general aim of the Computational Analysis of Ice Hockey Gameplay project is to develop a system capable of 1) learning the way ice hockey is played and 2) using this knowledge to enhance team performance. More specifically, we are interested in analyzing video from a particular hockey team, automatically detecting play styles, propensities, and habits and then using this information to suggest strategies for improving a team’s performance. Tthis could manifest in a general form (i.e. improving the overall gameplay of team X) or a more specific form (i.e. helping team X exploit weaknesses of team Y so that team X can beat just team Y). Our work draws on ideas from machine learning and machine vision.

David Fleet
Faculty Ryan Lilien
Faculty Vicki Iverson
Graduate Student Matthew Kitching
Graduate Student Derek Kwok
Graduate Student Mansoor Siddiqui
Undergraduate Alumnus More Research Profiles