The growing field of hockey analytics currently relies on the manual analysis of video footage from games. But the speed of hockey makes manually tracking and analyzing each player during a game very difficult and prone to human error.
Dr. David Clausi and Dr. John Zelek, both professors in the Department of Systems Design Engineering, with research assistant professor Yuhao Chen and a team of graduate students, have developed an AI tool that uses deep learning techniques to automate and improve player tracking analysis.
“The goal of our research is to interpret a hockey game through video more effectively and efficiently than a human,” said Clausi. “One person cannot possibly document everything happening in a game.”
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.
“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.”
Go to Going top shelf with AI to better track hockey data for the full story.