We demonstrate that higher variability in snap timing is beneficial for the passing game, as it relates to facing less havoc created by the opposing defence. We also obtain a quarterback leaderboard based on our snap timing variability measure, and Patrick Mahomes stands out 🐐

Using a Bayesian multilevel model with heterogeneous variances, we provide an assessment of National Football League (NFL) quarterbacks and their ability to synchronize the timing of the ball snap with pre-snap movement from their teammates.

you should fix the axes so that the units are the same (i.e., make 0.1 along the x-axis the same actual length as 0.1 along the y-axis)

You can find a link to my slides and all of the other great work, including research by
PhD students Quang Nguyen and Michele Sezgin here: nessis.org/nessis25.html

Thanks to NextGenStats and @awscloud.bsky.social for the partnership, @kaggle.com for hosting, and to all of our loyal participants for ensuring that our competition is changing the game

#BigDataBowl

The analytics competition will allow folks to creatively identify characteristics or visualizations of player movement, either on the offensive or defensive sides of the ball

www.kaggle.com/c/nfl-big-da...

The leaderboard competition will use the last 5 weeks of 2025 to identify who builds the best model/algorithm

www.kaggle.com/c/nfl-big-da...

This is the most beautiful time of year here Jonathan...