Is the Groundhog onto Something?

It’s February 2026, and the groundhog, unfortunately, predicted a longer winter. But does that really matter? Who told this groundhog he was going to predict weather? Did he grow up, as a pup, dreaming of being a weatherman? Does he know something we don’t know?

I want to know if he’s onto something. I want to know how good he is at actually predicting a longer winter or early spring.

Methodology

This is actually harder than you would think – ha! Mostly because, it’s not actually a single groundhog, but 80 total groundhogs, all making their own predictions (all with their own biases). And the consensus varies. Overall, my goal was to average it out, and compare the groundhogs prediction to the weather observed in the following eight weeks.

1. Pull in Groundhog Data. I did have to clean up some of the records, but the data source is here.
2. Pull in the Weather Data. I selected Dayton Ohio, because that’s currently where I live, and it has good coverage for sample data. I used the climate data online search, limited down to a single station.
3. Add in a longer-winter or longer-spring category. This is a major (and ultimately incorrect) assumption. I classify anything with above average weekly temperature highs as an early spring, and less than average weekly temperature highs as a longer winter.
4. Merge the dataset along the Year, and compare the groundhogs prediction, against the longer-winter/early spring, category.
5. Create a confusion matrix, for false positives/negatives, etc.

Find the repository here!

Results

Apparently, the groundhog shouldn’t quit his day job… of being a groundhog. The groundhogs as a collective, are right, roughly 53% of the time. But when they are wrong, are they typically wrong about early-spring, or longer-winter?

A confusion matrix, which compares different false positives, and false negatives can help here. A false positive, is when you get a positive result, that is actually a negative. A false negative, is the inverse: where you get a negative results (or no result) when there actually should have been a positive. Not all errors are the same, and there are many situations where one false result is preferred over another.

For example:

Let’s talk tornado sirens. If the tornado siren goes off, and everyone goes to the basement, and there is no tornado (false positive), the effect is everyone hangs out in their basement for a bit, watches a few episodes of Scrubs, and goes back to bed.

If the tornado siren doesn’t go off, and the tornado still comes (false negative), the effect is a mass casualty event.

So in the case of the climate agnostic groundhog, what does the confusion matrix look like?

Long story short, if the ground hog says it’s going to be an early spring, it’s a 50/50 shot. But, if the groundhog say’s early winter, he’s right 63% of the time; which could be bad news for 2026.

Stay Curious!

Sources

All predictions by year — GROUNDHOG-DAY.com. n.d. Retrieved February 3, 2026. https://groundhog-day.com/predictions.

Search | Climate Data Online (CDO) | National Climatic Data Center (NCDC). n.d. Retrieved February 3, 2026. https://www.ncei.noaa.gov/cdo-web/search;jsessionid=1206B4963C812652EE6AF13573CB4798.