By Chris Hayhurst, Contributor
To the diehard fan at the start of the 2019 National Football League season, the Philadelphia Eagles looked good on paper. The team’s offensive line had been described as “elite” with its roughshod roster of All-Pro and Pro Bowl players, and its franchise quarterback, Carson Wentz, appeared to be ready to crush the competition. But almost as important, and mostly behind the scenes, the Eagles had been preparing with an unconventional weapon. Just as they’d done for the last several years—a stretch that included victory in the 2017 Super Bowl—the team was investing heavily in data analytics, the technological equivalent of an extra player on the field.
Almost anyone who follows sports is already familiar with the data-driven culture of Major League Baseball. (The best-selling book Moneyball, published in 2003, details how a team with a relatively small budget found a way to win through statistical analysis.) But teams in the NFL are also crunching data; they just happen to be coming to the game a bit later, with early adopters like the Eagles leading the way.
Sandy Weil, a sports technology consultant, says the most common use for data analytics currently is for evaluating prospects in the NFL draft and making decisions about player acquisition from other clubs.
“Most NFL teams have some analytics capabilities in their organizations,” says Sandy Weil, a sports technology consultant who previously spent three seasons as director of football analytics with the Baltimore Ravens. “But the extent to which they’re actually using those tools—taking the data they have and deriving value out of it—that really depends on the club.”
The most common use for data analytics currently is for evaluating prospects in the NFL draft and making decisions about player acquisition from other clubs, Weil says. He’s worked for the Los Angeles Rams, for example, where he and his colleagues ran analytics on a player who’d been released by the Cincinnati Bengals. “The coaching staff had already identified him as a candidate, but they were worried because he was older, like in his mid-30s.” When Weil’s team did their analysis, however, they confirmed that the player was well worth pursuing. “Our model showed that based on his pass blocking, this guy was still elite, a top-five tackle in the league,” says Weil.
Mining Data for a Competitive Edge
The technology at the heart of data analytics in the NFL can be found in a program called Next Gen Stats, which is sponsored by the league through a partnership with Zebra Technologies. Since 2014, when the initiative launched, players have worn shoulder pads embedded with chips that can be tracked via radio frequency identification (RFID). All NFL stadiums are equipped with RFID readers that capture tag movement on the field, and the league makes that data available online to anyone who wants to see it—players, coaches, and fans alike.
“Everyone in the NFL is fast and strong. But if you’re a tenth of a second faster than the next guy, you’re the one the coach is going to want to rush on third down.”
—David Anderson, chief digital officer, Gains Group
The challenge for individual teams, says David Anderson, chief digital officer with the sports and technology agency Gains Group, involves mining the data they now have at their fingertips for insights they can use to win games. “Everyone in the NFL is fast and strong,” notes Anderson, who played in the league as a wide receiver with three teams. “But if you’re a tenth of a second faster than the next guy, you’re the one the coach is going to want to rush on third down.” The analytics departments now housed in many teams’ offices were created to not only make sense of tracking data, but also to decipher data sets from other sources, like strength-and-conditioning coaches and athletic trainers, he says. “There is so much data out there now; their job is to take everything and put it into context.”
With the Zebra data, for example, an analyst can look at an individual receiver’s initial acceleration after the ball is snapped, his top speed as he runs downfield, and the gap he puts on his closest defenders just before he makes (or misses) a catch. Balls are equipped with the tracking technology, as well, so the analyst can also consider its path once it’s thrown, its speed in the air, and even the spin as it hits the receiver’s hands. “If Odell Beckham Jr. scored this many touchdowns with the Giants, but he only managed this many with the Browns, obviously there’s an issue there, so let’s look into that,” Anderson says. “Was it pouring rain in certain games? How well did the quarterback play? What did the opposing defense look like?”
Big Data, Big Potential
The Eagles, for their part, seem to believe that analytics can drive results. By the halfway point of the current season, the team posted at least two online ads seeking a quantitative analyst and a director of analytics (job description for the latter position: “use data to address key issues in the modern NFL front office including player evaluation, game preparation, resource allocation, sports science, and player development”). And the hiring builds on what they already have, including more than half a dozen people in their front office with titles like “vice president of football technology” and “football operations analyst.”
Nine weeks into their 2019 schedule, they had a winning record and were second in the NFC East. The Eagles ended the season with a 9-7 record, falling to the Seattle Seahawks Sunday 17-9 in an exciting Wild Card Round match-up.
When he considers the potential for data analytics to impact how professional football is played, Sandy Weil thinks there’s room for the niche to expand. Some, for example, point to the fact that teams will eventually develop machine learning and predictive models to glean even more actionable information from player- and ball-tracking data. And as teams hire more analysts and attract the brightest technological minds, they may find new applications for the data parsing that could revolutionize the sport itself. Experts have said that it’s just a matter of time before a team wins the Super Bowl because of their work with data, Weil notes. “I don’t think it’s happened yet, but it will once somebody gets their arms wrapped around it.”
Chris Hayhurst is a freelance writer based in Massachusetts.