University of Edmonton uses AI to predict Olympic men’s hockey team winning gold medal

Machine Learning


In men’s hockey, Team Canada had big wins against the Czech Republic on Thursday and Switzerland on Friday. Machine learning students at Edmonton’s Norquest University are hoping artificial intelligence will help Team Canada achieve more wins in its bid for an Olympic medal.

Using computers, artificial intelligence and a century’s worth of data, students in the Machine Learning Analyst Diploma Program predicted that the Canadian men’s hockey team had a 97 per cent chance of winning a medal and a 75 per cent chance of winning the gold medal.

“We’re predicting that we’ll win 17 gold medals at the Olympics, the Winter Olympics. That’s our prediction. We’re looking at all the historical data to come up with that number,” said David Barahona, a sophomore at Norquest University.

student at Norquest University in Edmonton. (Leo Cruzatte, City News)

To achieve this result, students will input publicly available historical data into the software, such as medals won by Canada from 1924 to the 2022 Beijing Olympics.

“If you think about Netflix, for example, it can predict what you’re going to watch next. Netflix looks at all the data on what you’ve watched before, and it can start making predictions about what people just like you have watched before and what their trends are,” said Stephanie Husby, machine learning analyst program chair at Norquest University.

This AI prediction project is intended to increase students’ knowledge in machine learning and is not intended for financial gain.

Canada’s Nathan MacKinnon celebrates after scoring his fifth goal with Canada’s Connor McDavid (left) and Canada’s Macklin Chebrini during the 2026 Winter Olympics men’s ice hockey qualifying match between Canada and Switzerland on Friday, February 13, 2026 in Milan, Italy. (AP Photo/Hassan Ammar)

“This is a project that the students worked on with very narrow data, and it’s for fun and for us to see how it turns out. It’s not worth risking the house or anything like that at all,” Husby said.

Accuracy may still vary as certain factors need to be considered.

“You can add information about the athletes, the condition of each athlete, whether they’re injured, etc., and you can also change the model based on that additional information,” said Nasimeh Asgharian, a machine learning instructor at Norquest University.

Data examined by students at Edmonton’s Norquest University. (Leo Cruzatte, City News)

This number is not yet final, and we will need to identify any changes to the AI ​​model and determine whether the numbers are correct by the final day of the Winter Olympics.

“But you can blame the players first and then blame me,” Norquest sophomore David Barahona said.



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