UW team develops AI systems to improve hockey analytics
Posted Nov 10, 2025 06:44:44 AM.
Last Updated Nov 10, 2025 10:44:33 AM.
The future of team analytics in hockey, and sports in general, could now include the use of artificial intelligence.
A new study out of the University of Waterloo has designed a pair of systems using AI and game footage for analysis. The first system, called Puck Localization Using Contextual Cues (PLUCC), uses a player’s ability to watch the puck during a game and helps guess its location based on the player’s eye gaze and body position.
Initial tests during the study found that the accuracy of puck location was boosted by 12 per cent and reduced localization error by 25 per cent, compared to other tracking systems.
Dr. David Clausi, a professor of systems design engineering at the University of Waterloo, said the system can be used to assess individual roles by tracking all the players instead of just one.
“So not only are you scouting a team or predicting the team outcome, you can also look at individual players,” said Clausi. “This is crucial for NHL teams looking to trade and find those key players for the third and fourth lines; they’re the game changers when it comes to the playoffs.”
Another system is called SportsMamba, which uses an AI framework to track and predict player movements during a game. The system was tested with footage from hockey, soccer and basketball, showing an 18 per cent increase in efficiency compared to other tracking methods. The study notes this could help “teams and broadcasters to conduct real-time, data-driven performance analysis without the need for costly sensor systems or fixed-camera setups.”
“Tracking a hockey player on a breakaway is relatively easy,” said Dr. John Zelek, a systems design engineering professor and a director with Clausi of the Vision and Image Processing (VIP) Lab at Waterloo, in a press release.
“It’s much more difficult to track and differentiate players in a scrum along the boards or in front of the net. SportMamba can tackle these difficult situations and tell us, for example, who deflected the puck and scored.”
One of the big beneficiaries of this technology is smaller teams and markets, being seen as a low-cost option compared to expensive tracking systems.
“Our goal was to make puck tracking something that doesn’t require a million-dollar setup,” said Liam Salass, a graduate student who was lead author of the study in a press release. “If a coach can analyze a game using only video, that’s a big win for accessibility in sports analytics.”
