What Government Can Learn From NFL's Foray Into Analytics

Cleveland Browns wide receiver Josh Gordon (12) celebrates beside a television cameraman during an NFL football game against the Buffalo Bills.

Cleveland Browns wide receiver Josh Gordon (12) celebrates beside a television cameraman during an NFL football game against the Buffalo Bills. David Richard/AP

The use of big data analytics has helped the league and its teams gain an edge.

The National Football League has never been more competitive than it is today, and its combination of athletic rivalries and weekly drama pushed the league to new revenue heights in 2013, eclipsing the $9 billion mark

The NFL is already the most profitable sports league in the world, yet it manages to retain its fan base and attract new ones each season, largely because of its increased competitiveness. According to NFL Senior Vice President and Chief Information Officer Michelle McKenna-Doyle, the 2012 and 2013 seasons featured more victories by teams with margins of victory fewer than three points than any other two-year stretch in NFL history.

In other words, more close games have translated to more parity among the 32 teams, which ultimately equates to more fans and more revenue. This isn’t dumb luck, according to McKenna-Doyle, who spoke today at the Federal Big Data Summit in Washington, D.C.: The key driver to the NFL’s increased competitive landscape is based in big data analytics.

“The NFL has been into big data before it was cool,” McKenna-Doyle said. “We’ve been collecting statistics for every down and every player who ever played the game. All that is now being converted, and it’s searchable.”

Comparing the NFL and its teams to the federal government and its agencies in terms of technology innovation is an apples-to-oranges comparison. Yet, it’s not as far-fetched a comparison as one might think. As a league, the NFL sets policies and rules that 32 “collaborative, yet competitive teams” must follow, McKenna-Doyle said. “But each team wants to have a competitive advantage,” she added.

McKenna-Doyle’s description of the NFL could easily apply to some of the large federal agencies, with the recognized difference that the government is motivated by mission, not profit.

McKenna-Doyle said agencies and departments should seek out agile methodologies when possible in emerging technology and IT investments. The NFL and its teams test the waters by introducing new innovations such as Wi-Fi-enabled stadiums, and collect data that will ultimately justify whether to move forward. It is a safer way to go about navigating IT investments, and successful baby steps in an initiative can help attain crucial buy-in from senior leaders.

“Taking the time for years and years to try and make something perfect will just leave you behind,” McKenna-Doyle said. “Finding a way to slice off a little piece at a time and begin to use an agile method for developing a data strategy is a way to get buy-in and funding.”

Don’t underestimate the importance of buy-in, either. Innovative agencies including the U.S. Food and Drug Administration and the National Library of Medicine have leveraged senior leadership to pull off transformative big data efforts that meet mission and better the public’s health. Buy-in translates across all sectors.

McKenna-Doyle recalled an instance where she sat down with NFL Commissioner Roger Goodell and explained the importance of transitioning the health records of NFL players from paper-based to digital formats. This was a very intensive IT initiative that required both significant investment and oversight. With Goodell’s backing, the NFL raced to create electronic health records for its players, earning compliance with the Health Insurance Portability and Accountability Act.

As an organization, the NFL pools together all its data and makes it available to all 32 teams, creating an equal playing field for large- or smaller-market teams. The data itself is composed of statistics and meta-tagged video footage from the dozens of cameras that capture each game in every stadium. Teams can use the data for myriad reasons, including examining player injury issues, prospecting player trades or draft picks, decision-making and game-planning.

The NFL sets policies, guidelines and rules that govern the data teams can access and the tools they can analyze it with, McKenna-Doyle said, but teams have leeway through hiring analysts. Each team now has, by official or unofficial capacity, an analytics team to sift through petabytes of tagged video footage and other massive datasets the NFL collects. Unsurprisingly, McKenna-Doyle said there seems to be a correlation between teams heavy into analytics and winning.

“The last few Super Bowl winners are big users of this stuff,” she said.

Plenty of organizations across all team sports have taken to using analytics, but the NFL’s efforts – pushed out from Goodell and its front office – are truly transformative. All 32 teams use analytics, leaving less to chance and variance, and the best teams of late are those making the best use of analytics.

And overall, analytics is helping the league do what it wants to do the most: profit.