How does the NFL use Big Data to drive engagement

CamVsPayton

Image courtesy of NFL/SAP

Football is an incredibly data centric sport, and many fans eat that data for lunch (or tailgating). They use the data to answer questions on who will win, who will perform better, and which quarterback has the best chance of a better game. As we approach the Super Bowl, I’m curious about who the better quarterback is: Cam Newton or Peyton Manning? I don’t know my football stats that well, but I do have a secret weapon: a big data analysis tool accessible on the cloud containing data on every play made in the NFL. If we look over the past 50 seasons, that means over 37,000,000 data records¹. Even a subset of that data (the current players in the NFL) amounts to a lot of data, and it takes less than 1 second to bring back the results. The tool is called the NFL Fantasy Player Comparison Tool (by SAP).

The process is easy: you pick two players you want to match up–in my case, Cam Newton and Peyton Manning–and then click analyze. The tool measures each player on performance, matchup, consistency, upsides, and intangibles. You can adjust the weight of analysis placed on each of these areas (see below). Once you adjust these categories, the tool performs the analytics on the data and presents you with the analysis.

weightings

Image courtesy NFL/SAP

The NFL understands that its fans love data and that using big data and fantasy football are ways to drive attachment to the sport and keep fans engaged. The results of implementing the tool, according to SAP, include a 45% growth in the NFL’s fantasy football platform and a 7x increase user consumption of NFL.com content. We can also assume that it had an impact on the number of ad clicks on their websites and the exposure of fans to other sources of ads.

When I played fantasy football this year, I engage more because I have a stake in the game. I want to win! Since I don’t have a lot of knowledge in stats, I relied on online recommendations based on data in order to make my fantasy decisions. The interest in that will lead me to watch the game this weekend, where I otherwise might not have. So this is an interesting outcome: Big Data combined with gamification of the stats (ex. fantasy football) can lead to better engagement. What other big data can we gamify?

¹ Where did I get the 37m records from? 256 games per season multiplied by around 134 plays per game multiplied by 50 seasons and 22 players on the field per play.
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3 Responses to How does the NFL use Big Data to drive engagement

  1. sydhavely says:

    Quite clearly, Geoff, you have focused on the highly successful NFL marketing of Big Data and gamification utilizing its fan base with these newer forms of technology. Did you say who you thought was the better QB? Without looking at the “analyze” feature, my gut tells me that Manning’s best years are behind him and the Broncos are facing a team and a QB at the top of their and his game. Excellent post.

    • Geoff Irwin says:

      Believe it or not, all except for one of the analysis options picked Manning as the favorite based on the stats.

      • Geoff Irwin says:

        I should mention that it picked him as having a greater number of fantasy points, and Imnot sure if it recognizes that you are asking it to bet on the super bowl. It is a general comparison.

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