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A group of University of Toronto students will join an estimated 100 million people this weekend to watch Super Bowl LVII – but they’ll be seeing the game differently than most other fans.

Hassaan Inayatali, Aaron White and Daniel Hocevar recently learned that they are finalists in , one of the largest sports analytics competitions in the world.

The three students developed a statistical model that transforms data derived from motion-tracking chips embedded in the players’ uniforms into animated heat maps. The colourful visualizations provide real-time analysis of the all-important pocket of space around the quarterback, including the amount of pressure it is under at any given moment and how long it is likely to last.

“Our tool provides a quantitative way to compare plays and answer the really critical questions that players and coaches have,” says Inayatali, a third-year engineering science student in the Faculty of Applied Science & Engineering.

“For example, which offensive lines are the best at protecting their quarterback? Which defensive lines are best at rushing the other team’s quarterback? And which individual players are having the biggest impact?”

Like his teammates, Inayatali was a big sports fan growing up. Although he preferred baseball and hockey, he says he was more than willing to watch any game he had the chance to see.

Even so, the concept of sports analytics wasn’t really on his radar until he attended the fall clubs fair in his first year as an undergraduate student. That’s where he learned about the .

A statistical model created by three �鶹��Ƶ undergraduate students shows how the pressure on the pocket of space around the quarterback evolves over the course of a given football play (image: Hassaan Inayatali, Aaron White and Daniel Hocevar)

“I was lucky enough to go to their very first meeting, and kind of fell in love with what they were doing,” he says.

“It’s an opportunity to apply all the coding and mathematics I’ve been learning as part of my engineering science degree to sports, which has always been one of my passions.”

Today, Inayatali, White and Hocevar are all on the leadership team for UTSPAN, which is advised by Timothy Chan, a professor in the department of mechanical and industrial engineering and �鶹��Ƶ’s associate vice-president and vice-provost, strategic initiatives. The group holds weekly meetings covering different topics in sports analytics and is open to people of all experience levels.

They also post regularly on , an online hub for the sports analytics community that hosts open-source code, public datasets and notices about big data competitions.

It was there that the team first heard about the Big Data Bowl. The competition, which made its debut in 2019, challenges students and professionals to contribute to the NFL’s continuing evolution of the use of advanced analytics.

The three students entered this year’s competition and were supplied with eight weeks of data from the beginning of the NFL’s 2021 season. The package included play-by-play data, including scores and number of completed passes, as well as scouting data – such as whether a player was hit, sacked or hurried.

But most importantly for the team’s purposes, it also included detailed player tracking data derived from a system of sensors and radio-frequency identification (RFID) tags embedded in the players’ uniforms.

“Basically, what you get is a spreadsheet that shows each player’s X and Y position on the field, down to an accuracy of two decimal places – so one one-hundredth of a yard,” says White, a second-year statistical sciences student, who, like third-year computer science student Hocevar, studies in the Faculty of Arts & Science. “And this position is updated ten times per second.”

The team then combed through the published scientific literature on sports analytics, looking for examples of how to turn this data into useful insights. In the end, their inspiration came not from North American football, but from soccer in the form of .

“That work focused on modelling the spaces between players and determining which ones are most valuable,” says Inayatali.

“So that’s the basis for how we modelled the pocket, but then we layered on two more concepts. The first is pocket pressure, which is an indication for how much control each team has over the space at a given moment. The second is pocket longevity, or how long the pocket is likely to last.

“These concepts previously existed, but nobody had brought them together like this before.”

The team  that outlined their approach via a series of diagrams and sample codes and then submitted it to the NFL in early January. The results were announced on Feb 1.

“I was actually at the dentist at that moment, and I was frantically refreshing the web page on my phone,” says White. “When I found out that we made the finals, I showed it to the dentist. They had no idea what it was all about, but they said congratulations all the same.”

The team is one of only eight selected to participate in the finals – and one of only two composed of undergraduate students – from a field of more than 300 entries. Their finalist entry earned them a $10,000 prize and an all-expenses-paid trip to attend the 2023 NFL Combine in Indianapolis, which is set to kick off at the end of the month.

There, they will have four minutes to present their project to more than 200 football professionals. If successful, they will win a further $20,000 and a chance to be a part of NFL history.

“In the past, winners of the Big Data Bowl often become sports analytics professionals working for major teams,” says White. “The two teams in this year’s Super Bowl – the Kansas City Chiefs and the Philadelphia Eagles – each have one winner of the 2021 Big Data Bowl working for them. That would really be a dream come true.”