Understanding Player Movement in the National Football League Using Tracking Data by Tom Bliss Visit https://rstats.ai/nyr/ to learn more. Abstract: Player performance in the National Football League has traditionally been measured using box score stats and game outcomes. With better data - the NFLs Next Gen Stats which contains player tracking data for every player on every play - we can now measure a new element of player performance: player movement. We work to use player movement to better understand aspects of the rules and equity of the game including the NFL schedule and NFL pace of play. Bio: Thompson Bliss is a Manager, Football Operations Data Scientist for the National Football League Office. He started at the NFL as a Data Scientist in February 2020 and was promoted to his current role in December 2021. He completed his master’s degree in Data Science at Columbia University in December 2019. He received a Bachelor of Science in Physics and Astronomy with minors in Computer Science and Mathematics at University of Wisconsin - Madison in 2018. He was born and raised in Oakland, California and attended Oakland Technical High School. Twitter: https://twitter.com/DataWithBliss Presented at the 2022 New York R Conference (June 9, 2022)