ΑΙhub.org
 

Going top shelf with AI to better track hockey data


by
05 April 2024



share this:

Researchers from the University of Waterloo got a valuable assist from artificial intelligence (AI) tools to help capture and analyze data from professional hockey games more quickly and more accurately, something which could have implications for the business of sports.

The growing field of hockey analytics currently relies on the manual analysis of video footage from games. Professional hockey teams across the sport, notably in the National Hockey League (NHL), make important decisions regarding players’ careers based on that information.

“The goal of our research is to interpret a hockey game through video more effectively and efficiently than a human,” said Dr David Clausi, a professor in Waterloo’s Department of Systems Design Engineering. “One person cannot possibly document everything happening in a game.”

Bounding boxes are used to identify players as they move on the ice in broadcast game video. Jersey colours allow identification of home and away players.

Hockey players move fast in a non-linear fashion, dynamically skating across the ice in short shifts. Apart from numbers and last names on jerseys that are not always visible to the camera, uniforms aren’t a robust tool to identify players — particularly at the fast-paced speed hockey is known for. This makes manually tracking and analyzing each player during a game very difficult and prone to human error.

The AI tool developed by Clausi, Dr John Zelek, a professor in Waterloo’s Department of Systems Design Engineering, research assistant professor Yuhao Chen, and a team of graduate students, uses deep learning techniques to automate and improve player tracking analysis.

The research was undertaken in partnership with Stathletes, an Ontario-based professional hockey performance data and analytics company. Working through NHL broadcast video clips frame-by-frame, the research team manually annotated the teams, the players and the players’ movements across the ice. They ran this data through a deep learning neural network to teach the system how to watch a game, compile information and produce accurate analyses and predictions.

When tested, the system’s algorithms delivered high rates of accuracy. It scored 94.5 per cent for tracking players correctly, 97 per cent for identifying teams and 83 per cent for identifying individual players.

The research team is working to refine their prototype, but Stathletes is already using the system to annotate video footage of hockey games. The potential for commercialization goes beyond hockey. By retraining the system’s components, it can be applied to other team sports such as soccer or field hockey.

“Our system can generate data for multiple purposes,” Zelek said. “Coaches can use it to craft winning game strategies, team scouts can hunt for players, and statisticians can identify ways to give teams an extra edge on the rink or field. It really has the potential to transform the business of sport.”

More information about this work can be found in the research paper, Player tracking and identification in ice hockey, published recently in the journal Expert Systems With Applications.



tags:


University of Waterloo




            AIhub is supported by:



Related posts :



Machine learning for atomic-scale simulations: balancing speed and physical laws

How much underlying physics can we safely “shortcut” without breaking a simulation?

Policy design for two-sided platforms with participation dynamics: Interview with Haruka Kiyohara

  09 Oct 2025
Studying the long-term impacts of decision-making algorithms on two-sided platforms such as e-commerce or music streaming apps.

The Machine Ethics podcast: What excites you about AI? Vol.2

This is a bonus episode looking back over answers to our question: What excites you about AI?

Interview with Janice Anta Zebaze: using AI to address energy supply challenges

  07 Oct 2025
Find out more about research combining renewable energy systems, tribology, and artificial intelligence.

How does AI affect how we learn? A cognitive psychologist explains why you learn when the work is hard

  06 Oct 2025
Early research is only beginning to scratch the surface of how AI technology will truly affect learning and cognition in the long run.

Interview with Zahra Ghorrati: developing frameworks for human activity recognition using wearable sensors

  03 Oct 2025
Find out more about research developing scalable and adaptive deep learning frameworks.

Diffusion beats autoregressive in data-constrained settings

  03 Oct 2025
How can we trade off more compute for less data?

Forthcoming machine learning and AI seminars: October 2025 edition

  02 Oct 2025
A list of free-to-attend AI-related seminars that are scheduled to take place between 3 October and 30 November 2025.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence