Three southern states really hate the name Leo

If you’re into baby name trends, I built a daily quiz based on 150 years of US naming data. You can play it here.

With 150 years of US naming data at my fingertips, I needed to figure out where to start digging. Building Namegrid meant working with national name data, but I suspected I was missing some state-level weirdness that was worth exploring. I limited my search to 2024 naming data from the 50 US states and DC, skipping the less complete data for for territories like Puerto Rico and Guam.

I started by looking at the most popular names for each state in 2024, but the results were pretty boring. Almost every state’s most popular name was in the national top 10 with one notable exception: Montana. Their top girl’s name, Lainey, only reached a national ranking of 38th.

Measuring State Preferences

I needed a more interesting way to find state-level outliers. The challenge is that states vary wildly in population. Wyoming had only 1,995 recorded births in 2024, while California and Texas both topped 340,000.

Note: Names with under 5 occurrences in a given location are stripped out by the Social Security Administration to protect privacy, so actual births will be higher than what’s listed here.

First, I calculated what percentage of 2024 births each name represented nationally. For example, Liam accounted for about 1.51% of all boys born in 2024. Then I calculated what percentage of each state’s births were Liams. Florida came in higher at 1.91%, while Mississippi was lower at 0.93%. This gave me a score for how much higher or lower the state used the name compared to the national average.

BUT…this approach isn’t perfect and can skew towards smaller states. If 30 babies in Wyoming are given a name, that’s about 1.5%. Just one more baby bumps it to 1.55%. In California, 3,000 babies with a single name come out to just 0.87%, and one more doesn’t even register until you get four digits deep!

Put another way, one extra birth in Wyoming has 170x more weight than a birth in California or Texas!

Enter the Surprise Quotient

To get around this, I layered in Z-score, also called the standard score. I prefer to skip the stuffy statistical explanation and call it the Surprise Quotient instead. It measures how many standard deviations a data point is from the average. In this case, I took each name’s average popularity across all states and measured how much it varied. Importantly, each name gets its own distribution: Olivia’s state-by-state variation isn’t compared to Emma’s.

Looking at the names with the largest Surprise Quotients, a score of +3 or -3 means a state uses the name 3 standard deviations more / less than the average. From there, I calculated the wildest Z-scores for each state.

This produced some very uncommon names, so I needed to add guardrails. I only looked at names with 20+ occurrences in the state and disqualified any names without 100+ occurrences nationally.

Before applying these filters, I noticed something interesting: some names appeared in only one state.

Then I got sidetracked and went hunting for the total number of single-state names, which ended up being more lopsided than I expected.

It makes sense that more populous states have more unique names, but I was surprised to find that New York, with ~20M residents, has so many more unique names than even bigger states like California (~40M), Texas (~31M), and Florida (~23M). Anyway, back to the main analysis…

After applying my guardrails, this approach identified less common names with strong regional associations. For example, if 150 babies nationwide were given a name but 35 of them were in Minnesota, that’s a huge outlier. Especially considering Minnesota is home to just 1.7% of the US population!

Most Overused and Most Avoided

The map of overused girls’ names reveals some massive outliers. Wyoming was the only state without a standout girls’ name (no name exceeded a Z-score of 1). Hawaii’s Kaia led with a Z-score over 6, which makes sense given the state’s unique cultural heritage.

The boys’ data tells a different regional story.

Only Hawaii, New Mexico, West Virginia, and Mississippi have very high Z-scores (4+) for both boys’ and girls’ names. Wyoming makes up for having no standout girls’ name by being an outlier when it comes to Hudson. At the other end of the spectrum, Kansas, Missouri, and Ohio are boring and stick close to the national average for both genders.

Next, I examined the most avoided names, meaning these names have negative Z-scores. Since avoidance patterns were less extreme and many states stayed near national averages, I adjusted the thresholds (and switched to a tile map) to highlight the few states with significant avoidance.

More states showed avoidance patterns for boys’ names than girls’ names. I can’t really account for why, though boys’ names had fewer overall variations (~14.2k unique names vs. ~17.6k for girls), which might make regional differences more pronounced.

The Leo pattern caught my attention: three nearby southern states (Mississippi, Alabama, and South Carolina) all showed strong avoidance, so I started exploring different ways to group states and uncover other patterns.

Beyond State Borders

To see how patterns change across broader areas, I grouped states into nine US Census divisions. At this scale, avoidance patterns became less distinct, so I only focused on overused names.

Half of the names simply reflect their largest state’s preference. For example, the state-level preferences in Texas dominate the entire region, while other regions, like New England, overuse names that don’t match any single state.

I experimented with grouping states into even broader regional categories (Northeast, South, Midwest, West), but the results barely changed. The one exception: Estelle emerged as the Midwest’s most overused girls’ name despite not topping any individual state. That said, several Midwestern states overuse girls’ names starting with E: Evelyn (ND), Emersyn (SD), Elaine (IN), and Elliana (OH).

Next, I wanted to see if political leanings affected naming patterns. Using voting data from the 2016, 2020, and 2024 presidential elections, I calculated a partisan score for each state (% GOP vote share minus % Democratic vote share, averaged across all three elections). Positive scores = red states, negative scores = blue states.

Grouping states by political affiliation smooths out some of the extreme Z-scores we saw at the individual state level, but there were a handful of cases where the overuse or avoidance was quite extreme.

The Question of Donald

All this political data led me to one final question: Since 2015, has the name Donald become more popular in red states than blue states?

I checked the national trends and found that the name has declined dramatically over the past 60 years.

To test my hypothesis, I calculated what percentage of boys in each state were named Donald each year. Then I divided the data into two periods: the pre-Trump era (2000–2014) and post-Trump era (2015–2024). Using the same partisan scores from earlier, I calculated the change in Donald’s usage for each state in the two periods.

Surprisingly, the name Donald continued to decline in every single state, even the most conservative ones.

While DC (with the most liberal partisan score) saw a steep decline, several conservative states declined even more sharply. When grouped by ideology, the pattern is even clearer: Donald declined in popularity more in conservative states than liberal ones.

To quantify this relationship, I calculated the correlation between partisan score and change in usage: -.13. That’s basically no relationship at all and shows that state-level politics didn’t actually contribute to the name’s continued decline.

Even though I disproved my hypothesis, I have two ideas about what might explain this:

1. Percentage-based measures are more volatile in smaller states. The biggest drops occurred in small, conservative states like West Virginia, Louisiana, and Montana, where one fewer Donald carries more weight than larger states.
2. This analysis uses state-level data because that’s what’s available, but county-level patterns would be much more interesting. California alone has ~6M registered Republicans whose naming choices are averaged into the state’s overall liberal pattern.

So current politics didn’t explain Donald’s continued decline. But other political figures — both historical and more recent — have dramatically influenced US naming trends. Next time, I’ll dig into the most unexpected name spikes in history.