2016 St Jude Memphis Marathon

Published on Dec. 10, 2016, 10:42 p.m.

It's been exactly one week since I participated in the St. Jude Memphis Marathon, and I wanted to take a moment to say thank you to everyone who helped me get so close to my fundraising goal. Together, we raised $1435 of $1500, and I'm really happy about that (although, you can still give up 'till January!).

As for the race, well... I'm glad I ran it. This year, I've run a 50k, a 50-miler, and this marathon. Unfortunately the marathon was my least favorite of the three. Don't get me wrong: I love this race, and I love what it stands for. It's just the first race that I've had real issues with cramping. Basically, here's how it broke down:

  • Mile 1 - 10: Yeah, I'm feeling great! I'm gonna crush this thing.
  • Mile 11-17: Hrmm, I may have started off too fast. I'm suddenly feeling a little tired.
  • Miles 18-26: i am nothing but cramps, please stop running.

This is first race where I just wanted to stop. I guess this happens to everyone, and it's no fun. I'm not even sure what I did wrong, and I've been pretty consistent with training & nutrition for the past few months. Head over to Garmin Connect and you can see where things fall apart. I did however, set nearly a 15-minute PR with this, by finishing in just over four hours: 4:14. So there's that.

Oh well. C'est la vie.

Perhaps I can beat the 4-hour mark next year.

And now for some geeky stuff

A couple days after the race (once my legs stopped hurting), I went looking for the race results, and I realized they would be pretty easy feed into a python script and generate some interesting aggregate stats. So, this happened:

I posted this to a Facebook group, and my friend Von thought it'd be fun to see this kind of data for the entire history of the race. So, I grabbed as many of the data files as I could and started analyzing them. You can can check out this spreadsheet with some cool data like, average pace, average finish time, and male/female participation numbers. And if you're interested, the code to analyze this stuff is on github.

Here's a a few charts to whet your appetite:

Unfortunately, I couldn't get Google Sheets to recognize things like 9:50 and 4:44:17 as durations nor could I get them to plot those values. So I ended up converting pace and finish times to seconds. Still, it's interesting stuff.

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