2018-19 Big Data Bowl

Recap of the NFL's inaugural Big Data Bowl.


Congratulations to the winners of the NFL’s inaugural Big Data Bowl. Finalists presented their findings to members of the NFL league office, team executives, industry-leading representatives and league sponsors at the NFL Combine. The two grand prize winners received four tickets to a 2019 regular season NFL game and a $1,000 NFLShop.com gift card.



Matthew Reyers, Dani Chu, Lucas Wu, James Thomson, Simon Fraser University  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  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.

Recap of the first-ever Big Data Bowl.



Kyle Burris, Duke University  A trajectory planning algorithm for quantifying space ownership in professional football

  • Burris used metrics like player speed, direction and acceleration to chart the space occupied by the 22 players on the field. One example highlighted a 64-yard touchdown pass from Derek Carr to Johnny Holton to show how Holton’s speed and direction indicated that he was moving towards an open space well before he looked open on the field.
  • Key Stat: In the play above, Carr released the pass only 0.3 seconds after Holton beat his defender, suggesting the quarterback knew he was going to Holton before the receiver broke free.

Peter Wu, Brendon Gu, Carnegie Mellon University  DIRECT: A two-level system for defensive interference rooted in repeatability, enforceability, clarity, and transparency

  • Wu and Gu merged statistical modeling techniques with potential changes in penalty calls and receiver catch probability to consider new standards for defensive pass interference and defensive holding.
  • Key Stat: There appear to be two peaks in catch probabilities on pass interference calls, about 55% and 75%, which suggests the feasibility of a two-level foul system.

Jack Soslow, Jake Flancer, Eric Dong, Andrew Castle, University of Pennsylvania  Using autoencoded receiver routes to optimize yardage

  • The group presented three unique ways to represent pass route data, including time series and shape-based clustering. Merging in-play and game-specific traits, the group suggested that hitch routes are generally an underused strategy for increasing efficiency.
  • Key Stat: Roughly one in three Odell Beckham pass routes was classified as a 10-yard curl pattern.


Sameer Deshpande, Katherine Evans  Expected hypothetical completion probability

  • Deshpande and Evans tracked receiver catch probability across entire pass routes. Their approach allows for an estimation of the receiver’s performance regardless of when and where the pass was thrown.
  • Key Stat: On an 18-yard touchdown pass to Cooper Kupp during Week 1 of the 2017 season, Rams quarterback Jared Goff had another receiver that was more open than Kupp. Roughly 1.5 seconds into his drop-back, had Goff thrown to Robert Woods, the pass would have had a 92% catch probability.

Cathy Ha, Lucas Calestini  Efficient speed usage and the impact of fatigue in speed performance: an exploratory study

  • Ha and Calestini looked at the link between play specific factors such as play type, game factors (home vs. away, game surface, weather), and player fatigue (rest since last play, intensity of last play) and their impact on player speed efficiency.
  • Key Stat: Alvin Kamara had the highest speed efficiency of any ball carrier during the first 6 weeks of the 2017 season.

Adam Vonder Haar  Exploratory data analysis of passing plays using NFL tracking data

  • Vonder Haar classified routes and defensive space allocation to identify which route combinations yielded the most open receivers. His approach using convex hulls to characterize defender spacing was particularly novel.
  • Key Stat: The receiver generating the highest maximum separation was only targeted on about one in five plays in the first six weeks of the 2017 season.