The NFL’s Big Data Bowl posted videos yesterday of the finalist entry presentations for the 2021 event, which asked “what happens after a quarterback takes a snap and drops back to pass?” Submissions used NFL Next Gen Stats to find data-driven ways to characterize and predict successful pass defense, typically focusing on separation data (distance from targeted receiver to nearest defender) and targeting/completion rates over expectation data (how likely the receiver is going to get thrown at and catch the ball compared to other players in similar situations).
Eight submission videos were posted by the NFL’s official YouTube account:
- Wei Peng, Marc Richards, Sam Walczak, Jack Werner - Evaluating Defensive Player Coverage: A New Framework
- Meyappan Subbaiah, Dani Chu, Matthew Reyers, Lucas Wu - Let’s Eliminate the Defense
- Zach Bradlow, Zach Drapkin, Ryan Gross, & Sarah Hu - Check the TAPE, He’s Wide oPENN (Target-Agnostic Player ELO)
- Asmae Toumi, Marschall Furman, Sydney Robinson, Tony ElHabr - WADE (Weighted Assessment of Defender Effectiveness)
Each of the presentations are quite different and use their own approaches and methods, showcasing how creative analysts can be with raw data.