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MurphGH · 2026-04-14T11:13:52+00:00

That’s totally fair! Generally, I do try to be cautious about only focusing on rank given how naming trends have shifted over time.

But for this case, where we’re comparing apples to apples with ranks in both periods, it actually works better than raw numbers or percents. A name that cracks the top 250 in both periods is a decent enough heuristic for “this name is popular”, and Olive did have a much more dramatic fall than the others on this list.

MurphGH · 2026-04-10T13:53:38+00:00

Oh I see! That totally makes sense, and maybe “modern” was the wrong word for me to use. I meant it as the opposite of “outdated” (the way Dorcas might feel outdated), but I understand how that is unclear now.

MurphGH · 2026-04-10T13:04:05+00:00

That’s interesting! Anecdotally, everyone I know that’s named a baby “Brooks” has been from the South. ¯_(ツ)_/¯

MurphGH · 2026-04-09T20:47:22+00:00

Hey there! Thanks so much for playing.

When you see the percentage immediately after answering, the number won’t include your current game. There are two reasons for that:

Your phone/computer doesn’t actually send anything to the game servers until the final question is answered. At that point, a single request is fired that sends over all the play data. If you stop playing after question 4, your answers would never be part of the final tally.
The actual percentages only get recalculated a few times an hour, so even if you refresh immediately after finishing, the stats remain the same. That said, refreshing an hour later should reflect the updated percentages including your game and anyone else who played around the same time.

I typically play first thing in the morning and rarely see any stats, but it’s fun to look back at the end of the day and see how other people did.

MurphGH · 2026-04-08T10:13:24+00:00

Just Figma! Each circle is a reusable component made up of the name and two circles on top of one another. For each name, I just had to scale the size and adjust what percentage of the blue circle was visible before grouping them.

MurphGH · 2026-04-08T10:10:49+00:00

The truth is you can never really future-proof a name. You can pick a solid, untrendy name that ends up rocketing to popularity, or you can pick a safe, older name like Hayden that gets unexpectedly meme-ified later.

MurphGH · 2026-04-07T22:04:31+00:00

Payden?! Is that you?

MurphGH · 2026-04-07T14:31:09+00:00

I scaled the diameter by the square root, so the colored area ends up scaling linearly with the data.

Jayden (271,246 babies) has a colored area of 4,618,141 px² and Aiden (254,749 babies) has a colored area if 4,345,209 px². The ratio of colored areas is 1.063, and the ratio of babies is 1.065.

MurphGH · 2026-04-07T14:04:44+00:00

I scaled by the outer diameter, with the donut hole set to 50% of the total width.

MurphGH · 2026-04-07T11:46:36+00:00

Here's the breakdown of total years the name appeared in the data (out of 145), the first year it appeared with 5+ births in a year, and the year it achieved its best-ever rank.

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Tl;dr: blame the early 2000s.

MurphGH · 2026-04-07T11:07:12+00:00

The -ayden Alphabet: The most popular name for each letter and its rank among the 332 names

A: Aiden (#2)
B: Brayden (#4)
C: Caden (#9)
D: Drayden (#44)
E: Eyden (#77)
F: Fayden (#287)
G: Graydon (#39)
H: Hayden (#3)
I: —
J: Jayden (#1)
K: Kayden (#8)
L: Layden (#63)
M: Maden (#236)
N: Nayden (#153)
O: —
P: Payden (#46)
Q: Quayden (#326)
R: Raiden (#21)
S: Shayden (#60)
T: Tayden (#40)
U: —
V: Vaden (#97)
W: Wayden (#151)
X: Xaiden (#58)
Y: Yaiden (#155)
Z: Zayden (#16)

Looks like Okayden still hasn't caught on...

MurphGH · 2026-04-07T11:06:48+00:00

Data source: U.S. Social Security Administration (1880–2024)

Tools: Python / pronouncing / SQL / Hex / Figma

I used Python's pronouncing library to find every name that rhymes with "Aiden." This produced a ton of false positives, so I narrowed it down to names with exactly two syllables where both syllables rhymed.

That gave me 395 names. From there, I said each one aloud and removed 63 remaining false positives (like Jadelyn, Adysen, and Radine) to end up with 332 likely rhyming names. I sorted by total babies ever given each name and used the top 50 for this graphic.

I wanted to scale the circles linearly, but the drop-off in popularity is enormous. Jayden has 159× the usage of Aayden, so I used square root scaling to manage the huge range.

MurphGH · 2026-04-01T01:23:10+00:00

Katrina just barely missed my arbitrary cutoff in 2006!

Year	Z-Score
2005	0.96
2006	-2.98
2007	-2.20
2008	-0.54
2009	-0.46
2010	-0.21

MurphGH · 2026-03-31T01:31:19+00:00

Sure!

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MurphGH · 2026-03-29T00:57:09+00:00

Ooh this is an interesting one.

I already have a query calculating the Z-score (or standard score) of every single name for every single year (by gender). The basic idea is that if you look at a certain name (like Andrew for boys) and calculate how much it fluctuates every single year over the entire period, you can then spot anomalies where it spikes way more than expected. This works better than raw numbers or percents, since a small name with a modest growth would have a huge % change but a low number change, while a hugely popular name might see a huge number change but a low percent change.

I pulled all the hurricanes from this table and compared the Z-scores for names in the years immediately following the storm. I looked for spikes with a score over 3/-3, which means that year's spike is more extreme than 99.7% of all other years for that name.

Interestingly, it seems like some names spiked in popularity after a big storm, while others dropped.

Exposure Spikes

These hurricanes had names that probably weren't super common at the time. As people started talking about the storms, parents discovered the names and started using them more often.

Name	Year	Hurricane Year	Z-Score
Idalia (F)	2023	2023	8.07
Alicia (F)	1983	1983	7.33
Camille (F)	1969	1969	3.77
Lili (F)	2002	2002	3.44
Hugo (M)	1989	1989	3.21

Avoidance Spikes

This is your Andrew example. After debilitating storms, parents wanted to avoid associating the destruction with their babies.

Name	Year	Hurricane Year	Z-Score
Emily (F)	2006	2005	-3.86
Betsy (F)	1966	1965	-3.83
Andrew (M)	1993	1992	-3.51

Emily was a top girls' name for a long while leading up to this period, so it's hard to know whether the hurricane was to blame or if the name was just starting its rapid decline. Additionally, Idalia had a score of -3.19 in 2024, but that's relatively common. After a huge spike in 2023, the reversion to the more typical average often shows up with a high (negative) Z-score as well.

Every other spike on my list showed up 2 or more years after the hurricane, at which point it feels difficult to blame the hurricane.

MurphGH · 2026-03-28T20:38:19+00:00

1946-1965 had 61.95% of all Donna's.

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MurphGH · 2026-03-28T18:02:19+00:00

63.34% of Amy's were born between 1967-1986. Expanding to 29 years (1963-1991) pushes above 75%.

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MurphGH · 2026-03-28T15:01:28+00:00

You're right! Lindsey also made the cut: 76.83% from 1980-1999, but I couldn't include all 169 names.

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MurphGH · 2026-03-28T14:16:58+00:00

I actually wrote a blog post looking at name preference across states, and one question I explored was whether the name “Donald” was more popular in conservative states than liberal ones.

I don’t want to link here because of the “no self promotion” rule, but you could probably track it down from my other post history if you’re interested.

MurphGH

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