![]() ![]() In just under five years time since the publishing of that article, it has become hard to ignore the analytic movement within the NFL. ![]() Pederson believed in and trusted the team’s approach to analytics so much that a direct line of communication was created between the two during games, with the analytics department providing the head coach with math-based recommendations for any scenario Pederson requested ( Awbrey, 2020). Leading this early adoption of analytics, and reporting directly to team Vice President Howie Roseman, were Joe Douglas and Alec Halaby, “a 31-year-old Harvard grad with a job description” that had an emphasis on “integrating traditional and analytical methods in football decision-making.” The result? A “blending of old-school scouting and newer approaches” that were often only seen in other leagues, such as the NBA and MLB ( Rosenthal, 2018). In 2014, Eagles head coach Doug Pederson explained that all decisions made by the organization - from game planning to draft strategy - are informed by hard data and analytics. Kevin Clark, in a 2018 article for The Ringer, explained that despite not being as obvious as the sabermetrics movement in baseball, the analytics movement in the NFL is “happening in front of you all the time.” The use of analytics in the NFL did, however, predate Clark’s article. Most recently, the Chargers hired 2020 participate Alex Stern and the Chiefs hired Marc Richards, a member of the winning 2021 team, as a Football Research Analyst. ![]() As of the 2022 version of the Big Data Bowl, over “30 participants have been hired to work in data and analytics roles in sports, including 22 that were hired in football” ( Big Data Bowl, n.d.). In fact, it is now a regular occurrence for somebody to advance their R programming ability because of the nflverse and then go on to win the Big Data Bowl. The creation of the nflverse allowed for anybody interested in NFL analytics to easily access data, manipulate it to their liking, and release their visualizations and/or metrics to the wider public. The original nflfastR package is now part of the larger nflverse of packages that drive the NFL analytics community on Twitter and beyond. Because of the “open source” nature of R and R packages, a laundry list of companion packages quickly developed alongside nflfastR. Thankfully, he and Carl did not keep the creation to themselves and released nflfastR to the public. Yurko officially marked the end of the nflscrapR era and the beginning of the nflfastR era with a tweet on September 14, 2020: 1Īs a reply to his first tweet about the nflfastR project, Baldwin explained that he created the original function to scrape NFL data for the creation of his NFL analytics website. At this point, Ben Baldwin and Sebastian Carl had already built upon the nflscrapR project’s foundations to create nflfastR. json feed used to gather NFL data changed. The trio’s work on nflscrapR led to a peer-reviewed paper titled “ nflWAR: A Reproducible Method for Offensive Player Evaluation in Football.” Ultimately, the nflscrapR project came to an end when the specific. After Horowitz graduated - and got hired by the Atlanta Hawks - the nflscrapR package was taken over by fellow CMU student Ron Yurko (who would go on to receive his Ph.D. from the Statistics and Data Science program and, at the time of this book’s writing, is an Assistant Teaching Professor in the Department of Statistics and Data Science at CMU). The nflverse as we know it today was initially birthed from the nflscrapR project, which was started by the Carnegie Mellon University student and professor duo of Maksim Horowitz and Sam Ventura. That said, and without getting too far into the weeds of the history, the above-mentioned people are responsible in some shape or form for the current status of the nflverse, which is a superb collection of data and R-based packages that allows anybody the ability to access deeply robust NFL data as far back as the 1999 season. Thanks to the work of multiple people ( and … to name just a few), the process of getting started with analytics using NFL data is now easier than ever. ![]()
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