Pass coverage model wins NFL Big Data Bowl and $10,000


NEW YORK — A model classifying zone versus man-to-man coverage schemes to measure the before and after pass ability of each defender has won the NFL’s Big Data Bowl.

The winning group of Wei Peng, Marc Richards, Sam Walczak, Jack Werner took home an additional $10,000 prize to bringing their competition total to $25,000.

“The Big Data Bowl has changed how NFL clubs and their fans ingest and understand the game,” said Mike Lopez, NFL director of football data and analytics. “This year’s event covered new ground in football analytics: defending the pass play. More than 250 participants submitted a unique approach, and the eight finalist teams represent the best of the best public football analytics work done to date.”

Baltimore Ravens coach John Harbaugh joined the program, noting why his team values data and analytics, offering advice for aspiring football data analysts and his thoughts on the annual competition.

“This is a great way to broaden the perspective of the sport and to get more people involved in it in a fun way,” Harbaugh said. “There are so many perspectives and ways to look at anything, especially football because it’s a complex, crazy game.”

Last year’s winning algorithm, which provided predictions for rushing play outcomes, was adopted by the NFL’s analytics team as one of this season’s new Next Gen Stats. It was used and shared with NFL clubs and media during the 2020 season.

The competition also helps the league identify and develop future industry leaders. Since the first Big Data Bowl during the 2018 season, 15 participants secured jobs with either NFL clubs or affiliate vendors.

More AP NFL: and

No posts to display