Does unusually heavy traffic at pizzerias near the Pentagon predict global military activity?

While most of my class time is dedicated to the specifics of performing and interpreting inferential tests, basic statistical literacy and thinking are equally important lessons.

Here are some of the big-picture literacy ideas I want my students to think about in my stats classes:

1. How can we use data to understand patterns to make predictions?

2. How can we separate the signal from the noise? 

3. How can data actually inform real life and current events?

4. How can we repurpose existing data in a world where data is everywhere?

Here is an example I JUST found that addresses all of these ideas.

The Pentagon Pizza Report is an X account that monitors Google "Popular times" data in pizzerias near the Pentagon to predict military activity. 

The X account asserts that unusually high, later-than-normal foot traffic at pizzerias near the Pentagon (x) may indicate that Pentagon military staff are working late and need to grab take-out for dinner(y). 

Most recently, the website detected a surge in pizza consumption on June 12, 2025, right before the conflict between Israel and Iran heated up. 

As reported by The Guardian:

https://www.theguardian.com/world/2025/jun/13/pentagon-pizza-delivery-israel-iran-attack

Pentagon Pizza Report uses Google "Popular times" data, which is freely available and used to 1. establish a business's typical popularity over the course of the data and 2. track surges at a given business. Google provides this data for many, many different locations. 

For example, here is the "Popular times" data for a Tim Horton's in Erie, PA, early on a Sunday morning.

Google "Popular Times" data from a Tim Horton's

The Pentagon Pizza Report shared screenshots of the "Popular times" data for several pizzerias near the Pentagon on 6/12. The data shows a surge of activity at 6:59 PM, on 6/12/25, for pizzerias near the Pentagon.

Then it was really quiet at all of these pizza shops:

https://x.com/PenPizzaReport/status/1933301567538536880

Was there a dinner break for everyone working late at the Pentagon, and did that dinner break wind down around 7p? Was there a meeting that started after 7:10 that many people needed to attend? Inferences abound.

So do stats class lessons.

1. You can couch this example with two other creatively used second-hand data examples.

    -The Whataburger App helped Houstonians track power outages after a hurricane.

    -The Waffle Shop Index predicts the severity of damage following extreme weather. 

2. There are also many variables to account for in the multicausal world. In addition to pizza, the account tracks a gay bar near the Pentagon. And the gay bar was busy. This is cited as a conflict because when people at the Pentagon work late, they aren't going to the gay bar. HOWEVER...it is June, so it is Pride, so folks are probably out for that even if there are also folks working hard at the Pentagon. 

https://x.com/PenPizzaReport/status/1933664131066048700

3. Challenge your students to create a research study to test if"Popular times" data actually predicts military activity. What would you need to do? Track all of this data hourly, for a year, waiting for news stories about military activity? Would the predictive ability of this data be hindered by the clandestine nature of some military activities? While pizza is a good, inexpensive way to feed a bunch of people, what could be tracked to look for smaller military operations? Could you identify large, known military movements in the last five year and find archival "Popular times" data? THIS is the sort of thinking our students need to learn to engage in to master their statistical literacy and thinking.