The NFL has announced the two grand prize winners of the inaugural Big Data Bowl: Matthew Reyers, Dani Chu, Lucas Wu and James Thomson from Simon Fraser University (college entry), and Nathan Sterken (open entry).
The Big Data Bowl marked the first time that Next Gen Stats NFL player-tracking data from entire games has been accessible to non-league personnel. Eight finalists presented their findings to league and team personnel at an event held in Indianapolis before the 2019 NFL Scouting Combine.
Grand prize winners received $1,000 to NFLShop.com and four tickets to any 2019 NFL regular season game.
Below are summaries of the winning projects:
Matthew Reyers, Dani Chu, Lucas Wu and James Thomson from Simon Fraser University won the grand prize for their presentation “Routes to Success.”
The group modeled play success rate and expected points under various passing route combinations. Using a technique called model-based clustering, the group found several complementary pass route patterns that could consistently yield positive outcomes, even when accounting for defensive formation and behavior.
Key Stat: Through effective pass route combinations, an offense could control roughly 70% of the field.
Nathan Sterken received the grand prize for his presentation “RouteNet: a convolutional neural network for classifying routes.”
Sterken treated receiver routes as an image recognition problem, using a neural network to categorize each route. Once grouped, these patterns were compared to win probability added (the change in the offensive team’s chance of winning the game before and after the play).
Key Stat: The flat-in-post route, a staple of the Steve Spurrier days at the University of Florida, was the best three-receiver route combination.