I hope this provides bit of levity as everyone is focused on the election. If you have not yet voted & are undecided or planning to vote for Trump, I’d love if you read this post, especially if you’re a wealthy tech person.
A few of my friends in college ran Cross Country and taught me the phenomenon of “speed goggles,” where runners find other runners more attractive if they’re fast. This seems particularly applicable now that it’s well-documented that running clubs are the new dating apps.
As such, it makes sense that participation in the New York City Marathon grew 8% year-over-year, with more than 55 thousand runners completing it on Sunday.
Marathon Sunday is very fun and wholesome; it feels like everyone in the city is watching, and everyone is bumping into their coworkers on the street and cheering for their friends’ siblings. I had fun taking pictures in Brooklyn:
And I have been having fun playing with the results of the marathon, which includes data on participants’ age and gender and city of origin.
Because I like looking at names (though none of them will top the dog named EBITDOG), I wanted to see the fastest name in New York. Of course, the overall winners were Abdi Nageeye (who averaged 4:53 miles 😮), Sheila Kepkirui, and Sofia Camacho Ferral, but those are exceptional indivdual performances. I wanted to see how fast the more common names were on average.
The Fastest Men’s Names in the NYC Marathon
If we look at the number of people with a given first name and that name’s average mile pace, we can start to understand the names that were both most common and fastest overall:
For the men above, we can see:
Abdi was indeed fastest, but there is only one of him.
As the number of runners with a given name grows, the average pace tends to come towards the overall average — the law of large numbers in action.
“Michael” is the most common first name overall, with almost 600 Michaels finishing the race on Sunday. This makes sense: imagine saying “yeah, my coworker Michael is running it. He’s actually thinking about becoming a Product Manager at Strava.” It rolls off the tongue.
There are first names with a substantial number of runners that have average pace well below the overall average. “Jack” is particularly impressive given how many of them there were, and makes sense to me — Jack is a fast name.
I filtered these to only include names with at least 10 runners, to preserve some anonymity and have averages that were somewhat meaningful. These are the fastest men’s names! If any of these guys are your friends, give them my most sincere congratulations:
The Fastest Women’s Names in the NYC Marathon
We can do the same for the women (there is also a non-binary category which I’m not going to plot because there aren’t enough names to maintain some anonymity):
Sarah and Emily are neck-and-neck for the most common women’s names, which makes sense: both are extremely marathon-y names. Emily is a little faster, though!
Fabienne Schlumpf has a wikipedia page so I’m comfortable congratulating her directly on being extremely fast
The fastest women’s names with at least 10 runners are below. Karolina definitely feels like a fast name to me. So does Elisa. Is Avery fast? I guess Avery is fast.
To all of you Marcelos, Sylvies, Calebs, Jacks, and Beccas: I hope you bump into each other at running club next week and congratulate each other. And at the bar afterward, take a big swig of your Athletic N/A Hazy IPA, straighten your little Ciele hat, and ask each other on a date to the dog park, or something.
Congrats to everyone who ran! It’s a feat.
Speed by age, Murakami, & the fastest cities
Another cool thing about distance running that does not fit into my little run club narrative is that people can maintain competitive times well into middle age; you can see that the distribution of mile times for 40-50 year olds looks pretty similar to that of 20-30 year olds:
I was really hoping to see Haruki Murakami, who is 75 and an avid marathon runner and whose lovely book What I Talk About When I Talk About Running I forgot to put in my list of the very best nonfiction books. But no such luck.
It’s also funny to see how different cities and neighborhoods stack up. I don’t think I would want to live in a place like Boulder or Vancouver. I want to feel like I could outrun most of my neighbors without trying very hard.
Let me know if you’re interested in the data, it’s fun to play around with.
Thanks for reading, take care. And again, If you have not yet voted & are undecided or if you’re planning to vote for Trump, I’d love if you read this post, especially if you’re a wealthy tech person.
came here from "Data is plural" newsletter, this looks so interesting! i want to play around with the data too, the possibilities are endless!
Thanks for this! I might put together a little graphic showing the breakdown of countries represented in the marathon. Will give you credit for the data if/when I do.