football analytics

Unsupervised methods for identifying pass coverage among defensive backs with NFL player tracking data

Analysis of player tracking data for American football is in its infancy, since the National Football League (NFL) released its Next Gen Stats tracking data publicly for the first time in December 2018. While tracking datasets in other sports often …

A naive bayes approach for NFL passing evaluation using tracking data extracted from images

The NFL collects detailed tracking data capturing the location of all players and the ball during each play. Although the raw form of this data is not publicly available, the NFL releases a set of aggregated statistics via their Next Gen Stats (NGS) …

Going Deep: models for continuous-time within-play valuation of game outcomes in american football with tracking data

Continuous-time assessments of game outcomes in sports have become increasingly common in the last decade. In American football, only discrete-time estimates of play value were possible, since the most advanced public football datasets were recorded …

nflWAR: a reproducible method for offensive player evaluation in football

Unlike other major professional sports, American football lacks comprehensive statistical ratings for player evaluation that are both reproducible and easily interpretable in terms of game outcomes. Existing methods for player evaluation in football …

nflscrapR

Along with Maksim Horowitz and Sam Ventura, I have developed the nflscrapR package in R which allows for easy access of publicly available NFL play-by-play data. We provide estimates for the expected points and win probability for every play based on our fully reproducible methodology explained in our paper available for free on ArXiv. The nflscrapR package is frequently used by the football analytics community appearing in articles on The Athletic and FiveThirtyEight, as well as shared by the NFL Director of Data and Analytics.