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 :



Dataset reveals how Reddit communities are adapting to AI

  25 Apr 2025
Researchers at Cornell Tech have released a dataset extracted from more than 300,000 public Reddit communities.

Interview with Eden Hartman: Investigating social choice problems

  24 Apr 2025
Find out more about research presented at AAAI 2025.

The Machine Ethics podcast: Co-design with Pinar Guvenc

This episode, Ben chats to Pinar Guvenc about co-design, whether AI ready for society and society is ready for AI, what design is, co-creation with AI as a stakeholder, bias in design, small language models, and more.

Why AI can’t take over creative writing

  22 Apr 2025
A large language model tries to generate what a random person who had produced the previous text would produce.

Interview with Amina Mević: Machine learning applied to semiconductor manufacturing

  17 Apr 2025
Find out how Amina is using machine learning to develop an explainable multi-output virtual metrology system.

Images of AI – between fiction and function

“The currently pervasive images of AI make us look somewhere, at the cost of somewhere else.”

Grace Wahba awarded the 2025 International Prize in Statistics

  16 Apr 2025
Her contributions laid the foundation for modern statistical techniques that power machine learning algorithms such as gradient boosting and neural networks.




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association