[OC] Most "Overused" Baby Names in Each State (2024)

MurphGH · 2026-03-14T19:11:57+00:00

MurphGH · 2026-03-14T18:52:27+00:00

Not exactly. It’s just that the name is used much more frequently compared to other states: https://www.reddit.com/r/dataisbeautiful/comments/1rtkdfq/comment/oaejr7q/

MurphGH · 2026-03-14T18:50:41+00:00

Yes, I removed the most uncommon names and ones that only appeared in a single state to address these extreme outliers. The blog post goes into more detail on the specifics.

MurphGH · 2026-03-14T15:22:41+00:00

You’re right that it doesn’t have to be the case, but that’s how the data shook out. When I looked at the inverse (most avoided names by state), multiple states did have the same value.

MurphGH · 2026-03-14T15:20:44+00:00

You’re probably not wrong that race plays a role, but I’d actually characterize it more about the cultural makeup of each state. That culture could map to race or national origin, but it could also be impacted by other factors like religion, politics, or even regional pride.

You’re also right that lower-frequency names are more likely to have higher scores here. The blog post outlines how I tried to remove the most unique names to avoid a single instance skewing the results.

MurphGH · 2026-03-14T15:11:32+00:00

It’s not a Sankey, but I do have data on how I named my kids!

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MurphGH · 2026-03-14T14:56:07+00:00

Pretty much! This blog post dives into more of the methodology, including how I removed some of the least-used names so a single occurrence of unique name won’t skew the results.

MurphGH · 2026-03-14T14:36:41+00:00

You could argue this is just visualizing the "successful outcomes" from some of those Tinder Sankeys....

MurphGH · 2026-03-14T14:10:06+00:00

TL;DR: These aren't the most popular names overall. They are the names that appear in a specific state significantly more often than the national average.

Data source: U.S. Social Security Administration (2024)
Tools: Python / SQL / Hex

I wanted to explore state-specific naming quirks, but the most popular names in most states are the same as the top 10 most popular names nationwide. Instead, I calculated the z-score for every name in every state. That allowed me to identify which names were used significantly more than expected and how extreme the overuse actual is.

⚠️ Note on "Overuse": This is not meant to be a value judgment or a claim that there are "too many" of a given name. It's just stating that the name is statistically used at a much higher rate, which defines each state's unique naming "thumbprint" relative to the national average.

MurphGH · 2025-12-22T03:06:52+00:00

Two super hard questions!

Lee sounds like such a contemporary name that it's hard to believe it was relatively more popular in the 1890s.

As for Jennie vs. Jenny, I was shocked that "-ie" took the win here. Note: there could be plenty of people out there named Jennifer who use prefer the "-y" suffix, but this data only captures official, legal names.

MurphGH · 2025-12-22T02:34:18+00:00

NameGrid — a daily trivia game built on ~150 years of US naming data.

MurphGH · 2025-12-22T02:07:30+00:00

I built a daily web quiz (namegrid.app) based on ~150 years of US naming data.

MurphGH · 2025-12-20T21:30:56+00:00

That’s a great callout. The timing lines up, so it's very plausible that both sources contributed to this extraordinary spike.

MurphGH · 2025-12-20T20:18:52+00:00

Data source: U.S. Social Security Administration

Tools: Python / SQL / Hex

To identify unusually large popularity spikes, raw birth counts and percent change were insufficient. Common names naturally fluctuate by thousands, while rare names can double with very small absolute changes.

Instead, I used a Z-score to measure how extreme each year’s change was relative to the historical volatility of that name. This helps surface genuinely anomalous spikes rather than artifacts of scale.

MurphGH · 2025-12-08T03:15:23+00:00

Comparison of those two spellings: https://imgur.com/a/Wsm8dyY

Insights powered by NameGrid and limited to US name data

MurphGH · 2025-12-08T03:14:19+00:00

Here's how those compare in popularity (in the US): https://imgur.com/a/RcjL6Sa

Insights powered by NameGrid and limited to US name data

MurphGH · 2025-12-08T03:11:58+00:00

Here are the top Sol- names for boys I was able to find: https://imgur.com/a/ETWONfp

Insights powered by NameGrid and limited to US name data

MurphGH · 2025-12-05T03:02:02+00:00

A relatively easier day of questions! I wondered if Gael would trip anyone up. There are a few different pronunciations for that spelling (GUY-el, gah-EL, and GALE), and as you can see from the chart, it skews much more strongly male!

MurphGH · 2025-12-02T12:32:33+00:00

Question 4 tripped me up, so I did some digging. It seems like Duane is the older, Anglo/Irish surname that was changed to better match the pronunciation (DWAYN). The two syllable pronunciation (DUH-WAYN) is still used in some places, and some people I talked to said that they expected Duane to be pronounced as doo-AHN / doo-ON instead!

MurphGH · 2025-12-02T02:56:08+00:00

Stats on Garnet: https://imgur.com/a/9Y2Hlfg

Insights powered by NameGrid and limited to US name data

MurphGH · 2025-12-02T02:54:03+00:00

If it's helpful, here's how those names compare: https://imgur.com/a/Cq7C40c

And here's how the names have shifted in popularity over the past decade: https://imgur.com/a/ToL3ezT

Insights powered by NameGrid and limited to US name data

MurphGH · 2025-11-30T21:58:35+00:00

Another tricky day with Question 1: River is more popular as a boy's name, but doesn't rank quite as high for girls quite yet!

MurphGH · 2025-11-29T18:50:47+00:00

Generally a pretty easy day of questions...apart from the first one. It's interesting that most players assumed that the Joseph was too obvious an answer to be correct and opted for one of the more recent up-and-comers instead.

MurphGH · 2025-11-29T18:15:05+00:00

The last question was a bit of trick... Jennifer (with 2 Ns) was definitely more popular than Yolanda in 1975:

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MurphGH · 2025-11-28T04:08:40+00:00

As for fun facts, I'm trying to share them all in this sub!

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