Trio of University of Toronto students win NFL's annual analytics competition

A trio of University of Toronto students has won what you could call the Super Bowl of analytics competitions.

The group beat out 300 other teams from around the world and walked away with $20,000 US at the NFL's annual Big Data Bowl, held in conjunction with the league's scouting combine, set in Indianapolis.

Hassan Inayatali, a third-year student in the engineering science department, has never played a down of contact football in his life or experienced the crush of a defender's helmet.

But along with fellow students Aaron White and Daniel Hocevar, he was able to develop a new way to measure one of the most important things in football: getting pressure on an opposing team's quarterback.

Currently in football, pressure on a quarterback is measured by stats like sacks, hits or hurries.

"Those outcomes are measured at the end of the play and they don't really speak to how, for example, pressure evolves over the duration of the play," Inayatali explains. "So what we wanted to do was essentially develop a metric that provides continuity in the sense that you can tell what the pressure is on a quarterback at every frame of the play.

"There's still pressure when a defender is four yards away. So what we wanted to do is essentially quantify that so that teams could have a better sense of what's going on over the duration of the play rather than just the results of the play itself."

The NFL provided the teams with detailed player tracking data from games played during the first eight weeks of the 2021 season. Inatayatali's group used 4,911 plays as the basis for their analysis.

Hocevar says the project's colourful graphics that clearly demonstrated how quarterback pressure evolves over the course of a play was attractive to