Crowd Flow monitors the number of fans in their seats throughout the game.

It’s binary data (someone is either in a seat or not), but when looking at the whole venue it’s incredibly insightful.

In the real-world example above it’s clear that day and night crowds have different movement patterns. You’ll see significantly more fans leave their seats during halftime for evening games. It would be encouraging for the team to note that more than 95% of the fans are in their seats at the start of games – a good indication that their new touchless turnstiles are working as expected.

Another real-world example:

  • One MLB partner utilized Crowd Flow to compare what percentage of their fans remained in the stadium at the end of their Thursday, Friday, and Saturday night games to see if their Friday Night Fireworks Promotion, a multi-million dollar investment, was keeping people in the ballpark longer. At 2 hours and 40 minutes into the game, on average 52% of the crowd remained in their seats on Thursday nights, 51% on Friday nights, and 64% on Saturday nights. The data proved that the Friday Night Fireworks Promotion was not an incentive for fans to stay longer and that the money could be spent on something else.

This same data is also used to:

  • Find optimal start times
  • Predict when fans start leaving
  • Analyze specific sections
  • Identify bottlenecks in the flow of fans

When combined with Demographics this data allows teams to determine to what extent different promotions or changes in programming work.