NFL Fans on Facebook

Cario Lullo
Read Time: 5 minutes apprx.
big data data science mapping social media data sports visualization


In 2013, Facebook conducted an exploration of the number of Likes received by each NFL team in every U.S. county for each day of the season. The study conducted by Sean Taylor involved approximately 35 million U.S. account holders who Liked a page for one of the 32 teams in the league can be found here. This is one of of the most comprehensive samples of sports fan data ever collected. Taylor posed a number of interesting question such as people fans of a particular team because of their hometown, their friends or because the team is winning. The results were interesting; Taylor found that winning certainly helps but making the playoff (win or lose) may be even more important.



Facebook was also interested in determining if the belief that team support is heavily influenced by family ties and/or where a person grew up. Taylor was curious to see where fans for various teams live now. So, he constructed a map for the top Liked teams throughout the regular season and playoffs. The results tell an “interesting story about how football rivalries and allegiances divide and unite the country and even individual states.”

In some cases entire states or regions of the country uniformly support a single team; e.g. the Vikings and the Patriots. In states such as Florida and Ohio however there are significant fractures in support. Florida has three teams, the Dolphins, Buccaneers and Jaguars and is further segmented by support for Northern team; brought by Snowbirds perhaps. Other teams, notably the Steelers, Packers and Cowboys seem to transcend geography and may truly America’s teams.




As January commences and playoffs begin, fewer teams remain and geographically based divisions become more evident. Even fierce rival teams’ fans begin to switch allegiances; Bears fans cheer for the Packers, Raiders fans transform into 49er’s supporters.




This trend towards geographical division continues throughout subsequent playoff rounds and into the Superbowl, by which time fan support is divided almost entirely by geographical proximity.

This work by Facebook is a practical example of how analytics can be used to derive business value. As the maps above illustrate, not all NFL teams seem to be of the same value; the Broncos, Cowboys and Steelers certainly seem to be worth more than say the Raiders, Rams or Bengals. This work also provides insight into the importance of just making the playoffs. Finally, as evidenced by Florida; it’s very difficult for a new (or transplanted) team to make a foothold in a market.

Professor Russell Walker of Kellogg addressed this topic recently in a LinkedIn post. Walker pointed out the value of this geographic work in identifying who a fan is, who one might be and what the “sphere of influence” a particular NFL team may hold. Walker also discusses the concept of data inversion – the process of manipulating data across dimensions that may be secondary to the original intent of the data collection. According to Walker,

“People vote for their favorite team. Facebook records that information and location and inverts it to provide a view on the sphere of influence of a given team. In some small way, this also says something about the value of the franchise – which leads us to concept of asset surveillance. It is possible because of the Big Data effect. These transformations of the data can bring value from this Big Data. Advertisers, franchise owners, TV networks, and even athletes might look to such data to understand the value of a fan base and their business that that fan base.”

As companies continually seek ways in which to monetize data, this Facebook experiment exemplifies ways in which observations can be transformed interpreted and monetized using modern data collection and visualization techniques.


Analysis and graphics by Sean J. Taylor, an intern on the Facebook Data Science team

Ideas on data inverting, asset surveillance, and the use of Data Science to create value from Big Data are from Russell Walker’s Book, From Big Data to Big Profits: Success with Data and Analytics (Oxford, 2015).