By Isabella Ibrahim

When the Toronto Maple Leafs partnered with analytics firm SAS in August 2014, the idea didn’t take long to trickle down to the Ontario University Athletics (OUA) division. 

In 2018, the Toronto Metropolitan University (TMU) Bold men’s hockey program added its first analytics coordinator, Larkin Lee, to the roster. 

Eight years later, they’ve doubled down by hiring two additional analytics assistants, while the women’s team will introduce analytics staff to the lineup this season for the first time in program history.

For Jack Bayley, a second-year media production student and analytics assistant for the men’s hockey team, much of his preparation and day-to-day work begins before puck drop, starting with scouting TMU’s upcoming opponents.

“I will watch Brock’s last game, and I’ll go through the whole game. I’ll track the time on ice from the teams, kind of where, who they’re rolling out for lines,” Bayley said. 

By preparing video footage that analyzes their competition’s playing styles and tendencies, these insights help the team evaluate what TMU faces before planning their own tactics ahead of games.

During games, Faizah Alam, fellow analytics assistant and fifth-year business technology management student, divides the workload with Bayley. 

The pair then collect a laundry list of statistics, including but not limited to face-off wins, critical shot attempts, odd-man rushes, and rebounds. 

Bayley said precise information is crucial for making TMU’s data models useful in games. “It’s important to be getting the right stuff down,” he said. 

“Accurate data can help improve our models and make it so that our scouting reports and the information we have are the most accurate and the most up-to-date.” 

According to Alam, a data model synthesizes an abundance of collected information. This includes scoring opportunities and shot quality to identify patterns, evaluate performance and generate insights, which can be used to recommend improvements and support tactical decision-making during games.

“[Data models] are especially valuable because they provide a more objective way to evaluate performance beyond what we observe during a game,” said Alam.

Much of Alam and Bayley’s work is done under a time crunch. They are often racing against the clock to compile and integrate their data into TMU’s analytics software, which has been developed in-house over multiple years by Lee. 

During intermissions, Lee creates detailed game reports based on the data tracked during periods and then shares these findings with the other coaches.

“In the 15 minutes that we have, we have to present them with something that’s easy to identify and something that can be used quickly in terms of their decision-making,” said Lee. Presenting the data in a tailored format makes it easily digestible for coaches when they head back to the locker room.

Alam said it’s important not to overwhelm the coaching staff with statistics, but instead, to share the most relevant details in that moment. 

“The goal is to take what’s happening on the ice and turn it into information that can help the coaching staff better understand how the team is performing,” Alam said.

“Analytics give teams another way to evaluate performance, identify patterns and understand where improvements can be made. For a university team like TMU, I think it’s also a great opportunity to bring a more detailed approach to player development and game preparation,” she said.

However, identifying a problem is just the first step. While analytics can display where a team may be struggling, they do not automatically provide the solutions needed to address them. 

“It’s not necessarily about replacing traditional coaching methods, but rather adding another perspective that can help the team continue to improve,” Alam said. 

Coaching staff must understand and integrate the data presented to them, give players feedback and ensure they apply corrections accordingly.

“For us, in terms of our operations, it’s never quite just analytics, and it’s never quite just like what we see on the ice,” Lee said. “We try to take everything as a whole picture and then make decisions based on that.” 

This balance is extremely important at the Ontario University Athletics level, where athletic resources greatly differ from those attainable in professional organizations, such as the Ontario Hockey League and the National Hockey League (NHL). 

Lee said that teams in the NHL have access to detailed tracking technology built into players’ equipment and even game-used pucks. At TMU, this information is all tracked manually by the analytics team. 

Even without these resources, the Bold have expanded their analytics operations to the women’s hockey program this year and aim to employ the same in-house software that Lee has developed with the men’s team.

Alam said tools like these can make all the difference for teams trying to find every bit of performance. 

“I think one of the biggest benefits [of using analytics] is being able to make more informed decisions using data alongside what coaches and players are already seeing on the ice,” she said. 

Alam added how important this is for a game like hockey, in which line changes and strategic adjustments happen in a matter of instants and can easily get lost in the chaotic beauty of the game.

“Hockey is such a fast-paced sport, and there are so many little details that can be easy to overlook when you’re watching a game in real time.”