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March Mapness: Analyzing Mobility Patterns of NCAA Basketball Fans

April 5, 2021
by
Briana Brown

There’s no denying that fans often feel strongly about their team - and the outcomes of the NCAA tournament. But do differing opinions on college sports indicate larger behavioral divides? We decided to look at geosocial data on sports team affiliations and mobility to various points of interest to see if different fanbases frequent different brands or types of businesses.

Using Spatial.ai’s geosocial segments, we identified census block groups in the US in the 50th percentile for #marchmadness activity on social media and in the top 10th percentile for activity related to the following 9 teams:

  • Connecticut Huskies
  • Florida Gators
  • Gonzaga Bulldogs
  • Michigan Wolverines
  • North Carolina Tar Heels
  • Ohio State Buckeyes
  • Oklahoma State Cowboys
  • Oregon Ducks
  • Syracuse Orange
Using geosocial data to Identify census block groups with a strong Gonzaga fanbase.
Identifying census block groups with a strong Gonzaga fanbase.

Once we identified these target census block groups with large fanbases for the 9 teams, we used SafeGraph Patterns data to identify which points of interest people from those census block groups tend to frequent. 

Movement from census block groups associated to NCAA basketball teams and the top ten POIs they visit.
Movement from census block groups associated to NCAA basketball teams and the top ten POIs they visit.
Movement between census block groups for specific NCAA basketball teams and the top ten POIs they visit in the LA area.
Movement between census block groups for specific NCAA basketball teams and the top ten POIs they visit in the LA area.

We then standardized the data to identify brands that are more popular for these particular CBGs than they are nationally. This helps us avoid seeing the same few large national chains repeated across each fanbase. The resulting data helped us identify the top 10 brands and top 10 NAICS codes visited by people from census block groups from each team’s fanbase. The “Standard Difference” measure represents the standardized difference of popularity of the brand. For example, a measure of “1” says that the average CBG for that team visits that brand one standard deviation (within that brand) more than the average CBG overall.

The below dashboard shows the census block groups with affiliations for each team, as well as the top NAICS codes visited by each team. 

This dashboard shows the top ten brands visited by people from census block groups associated to each team.

Aside from being incredibly interesting to look at, these fanbase maps and charts can tell us a lot about subsets of the population, and inform marketing, logistics, and site selection strategies. For instance, a supplier of sports memorabilia might decide to sell their goods at stores within a certain radius of a major city based on where fans in that area tend to travel, or an ad campaign targeted at a specific fanbase could analyze consumer behaviors for that group.

Geosocial profiles and mobility data may not be able to predict which team will win a game, but they can help brands better understand consumers to offer more compelling products, services, and ads.

To learn more about ways to combine Spatial.ai's geosocial segmentation data and SafeGraph's POI and mobility data, check out our recent webinar, "Stronger Market Intelligence with Mobility Patterns and Geosocial Profiles."

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