Tuesday, December 9, 2014
Monday, February 17, 2014
Where Nashville Runs
Over on Flowing Data, Nathan Yau mapped out Where people run in major cities across the US. I thought I'd do the same with the four major cities in Tennessee. I used RunKeeper's route search tool to find routes in Nashville, Chattanooga, Knoxville, and Memphis and then graphed them altogether on tile maps from CloudMade. See anything interesting.
As Yau pointed out in his post, most people like to run near water or in state parks. I think this is true here in Tennessee as well, but also note that many run where they work or live or go to school.
(Click on the images below to see them full-screen)
Nashville (larger map)
Chattanooga
Knoxville
Memphis
Friday, December 13, 2013
Lookout Mountain 50 Miler and 10k Runners: Where Are They From?
Runners are travelling from all across the US to the Chattanooga Lookout Mountain Trail Races. With a low temp tonight of 37°F and 100% chance of rain tomorrow, those from California, Texas, and Florida should be very afraid.
Cheers!
Lookout Mountain 50 Miler Finishing Times: Summary and Predictions
I've been delayed in publishing this report because I really wanted to show you some elevation profiles of the race, but with the course change and a longer distance than I'm used to drawing (I'm a 50km map drawing specialist ;) it became clear that I wouldn't finish.
So I present to you first a prediction of how you should fare in the Lookout Mountain 50 Miler presuming you've run the StumpJump 50k before. Just like the UpChuck50k report, I've found runners who have run both the LM50 and the StumpJump, averaged their times and used them to build a linear prediction model. I didn't find any recommended times from race management, so the red line has been omitted. Of course there's an obvious flaw in the model given the difference in race length when comparing the two, and another flaw would be if a runner who has not run a 50 miler before but has run the StumpJump may find their predicted time a little ambitious.
| StumpJumpTime | ModelRecommendation |
|---|---|
| 04:00:00 | 07:16:04 |
| 04:30:00 | 07:56:36 |
| 05:00:00 | 08:37:08 |
| 05:30:00 | 09:17:40 |
| 06:00:00 | 09:58:12 |
| 06:30:00 | 10:38:44 |
| 07:00:00 | 11:19:16 |
| 07:30:00 | 11:59:48 |
| 08:00:00 | 12:40:20 |
| 08:30:00 | 13:20:52 |
| 09:00:00 | 14:01:24 |
And just like the StumpJump50k report, here are a few interesting graphs which you might want to peruse before the race..
Monday, November 25, 2013
Predicting Your Duncan Ridge Trail 50km Time
Congratulations to all the DRT finishers this past weekend! I meant to get this post out before the race to provide you with something to talk about with your friends and family on race day, maybe even encourage you to shoot for a faster finishing time. Yet time got away from me.
What follows is an analysis of finishing times for runners who have finished both the StumpJump and the DRT 50km races.
Given how fast you ran the StumpJump, how well should you expect to do in the DRT? Someone recommended adding two hours to your StumpJump time, but is that true? Let's look at the data. Taking all the finishing times for those runners who have finished both races, averaging their results if they've run more than once, we plot DRT time versus StumpJump time. Stumpjump times are on the X axis (independent variable), DRT on the Y axis (dependent variable). Although the DRT 2013 results are in, let's look at how good a prediction adding two hours to your StumpJump time stacks up against a linear model prior to 2013. What's a linear model? Simple, try and draw a straight line on a plot that goes through each of your data points. That's a linear model ;) Obviously this doesn't work if your data are not already in a straight line, but try and draw a line through the oval shape that closely resembles your data. That's a linear model, too :) Unfortunately as you'll see below, sometimes the shape of your data is weird. That's when your linear model doesn't really characterize your data, and using a linear model just becomes useless.
The two hour recommendation is the red line, while the linear model recommendation is the blue line. All those dots represent runners who have run both races, their average StumpJump on the X axis, average DRT time on the Y axis. If you want to predict your own DRT finishing time, start by finding your StumpJump time on the X axis with your computer mouse, scroll up to either one of the lines, then scroll to the left to get your predicted DRT Time.
So given StumpJump times for all years including 2013, and given DRT times for all years except 2013, we see that the linear model predicts DRT times less than two hours plus StumpJump time. But our data look funny, and the linear model is probably not be a good predictor.
Now let's look at the DRT 2013 finishers who've also run the StumpJump.
At least for 2013, the two hour recommendation looks like it characterizes the data pretty well, although you could probably get away with recommending adding only 1.5 hours instead of 2.
And finally let's add the DRT 2013 finishers to our original data and see what our linear model does.
It seems to characterize the data a little better, but the data shape still looks funny. Maybe next time I'll investigate a different model. Regardless, here's a prediction table to use while preparing for DRT 2014!
What follows is an analysis of finishing times for runners who have finished both the StumpJump and the DRT 50km races.
Given how fast you ran the StumpJump, how well should you expect to do in the DRT? Someone recommended adding two hours to your StumpJump time, but is that true? Let's look at the data. Taking all the finishing times for those runners who have finished both races, averaging their results if they've run more than once, we plot DRT time versus StumpJump time. Stumpjump times are on the X axis (independent variable), DRT on the Y axis (dependent variable). Although the DRT 2013 results are in, let's look at how good a prediction adding two hours to your StumpJump time stacks up against a linear model prior to 2013. What's a linear model? Simple, try and draw a straight line on a plot that goes through each of your data points. That's a linear model ;) Obviously this doesn't work if your data are not already in a straight line, but try and draw a line through the oval shape that closely resembles your data. That's a linear model, too :) Unfortunately as you'll see below, sometimes the shape of your data is weird. That's when your linear model doesn't really characterize your data, and using a linear model just becomes useless.
The two hour recommendation is the red line, while the linear model recommendation is the blue line. All those dots represent runners who have run both races, their average StumpJump on the X axis, average DRT time on the Y axis. If you want to predict your own DRT finishing time, start by finding your StumpJump time on the X axis with your computer mouse, scroll up to either one of the lines, then scroll to the left to get your predicted DRT Time.
So given StumpJump times for all years including 2013, and given DRT times for all years except 2013, we see that the linear model predicts DRT times less than two hours plus StumpJump time. But our data look funny, and the linear model is probably not be a good predictor.
| StumpJumpTime | TwoHours | ModelRec | Difference |
|---|---|---|---|
| 04:00:00 | 06:00:00 | 06:09:43 | -09:43 |
| 04:30:00 | 06:30:00 | 06:34:28 | -04:28 |
| 05:00:00 | 07:00:00 | 06:59:14 | 46 |
| 05:30:00 | 07:30:00 | 07:23:59 | 06:01 |
| 06:00:00 | 08:00:00 | 07:48:45 | 11:15 |
| 06:30:00 | 08:30:00 | 08:13:30 | 16:30 |
| 07:00:00 | 09:00:00 | 08:38:16 | 21:44 |
| 07:30:00 | 09:30:00 | 09:03:01 | 26:59 |
| 08:00:00 | 10:00:00 | 09:27:47 | 32:13 |
| 08:30:00 | 10:30:00 | 09:52:32 | 37:28 |
| 09:00:00 | 11:00:00 | 10:17:18 | 42:42 |
At least for 2013, the two hour recommendation looks like it characterizes the data pretty well, although you could probably get away with recommending adding only 1.5 hours instead of 2.
And finally let's add the DRT 2013 finishers to our original data and see what our linear model does.
It seems to characterize the data a little better, but the data shape still looks funny. Maybe next time I'll investigate a different model. Regardless, here's a prediction table to use while preparing for DRT 2014!
| StumpJumpTime | TwoHours | ModelRec | Difference |
|---|---|---|---|
| 04:00:00 | 06:00:00 | 05:50:40 | 09:20 |
| 04:30:00 | 06:30:00 | 06:18:11 | 11:49 |
| 05:00:00 | 07:00:00 | 06:45:42 | 14:18 |
| 05:30:00 | 07:30:00 | 07:13:13 | 16:47 |
| 06:00:00 | 08:00:00 | 07:40:44 | 19:16 |
| 06:30:00 | 08:30:00 | 08:08:15 | 21:45 |
| 07:00:00 | 09:00:00 | 08:35:46 | 24:14 |
| 07:30:00 | 09:30:00 | 09:03:16 | 26:44 |
| 08:00:00 | 10:00:00 | 09:30:47 | 29:13 |
| 08:30:00 | 10:30:00 | 09:58:18 | 31:42 |
| 09:00:00 | 11:00:00 | 10:25:49 | 34:11 |
Tuesday, November 19, 2013
The Harpeth Hills Flying Monkey Marathon: A Soul Crushing Ordeal
Why Would You Even Sign Up?
The Harpeth Hills Flying Monkey Marathon happens again this Sunday, but you don't want to know because it involves winged monkeys trying to eat you off the road while you race up and down the hills in Nashville's Percy Warner Park. Who in their right mind would sign up for this thing?!
Apparently lots of idiots. It sells out in minutes, and there's even a lottery to get in!
Rather than bore you with words, words that don't even want to be used to describe this poor race, I'll show you some truly sad plots that convey how slow these poor idiots run (read my StumpJump report if you need help interpreting the plots).
And if you seriously want to learn more, go here, here, here, or here.
Take all the finishing times, group them by number of times a runner has run, and show the difference each time. The red line is the mean.
What does this tell you? No matter how many times you run Monkey, you'll get slower. On average.
Monkeys Win!
The Harpeth Hills Flying Monkey Marathon: Course Profile
The Harpeth Hills Flying Monkey Marathon happens again this Sunday,
Notable Ascents and Descents (kilometers)
| Start | End | Distance | Average Grade |
|---|---|---|---|
| 2.05 | 2.95 | 0.90 | -0.04 |
| 10.90 | 11.40 | 0.50 | -0.05 |
| 15.85 | 16.55 | 0.70 | 0.07 |
| 17.05 | 17.95 | 0.90 | -0.06 |
| 21.80 | 22.40 | 0.60 | -0.06 |
| 24.30 | 25.15 | 0.85 | -0.06 |
| 28.85 | 29.45 | 0.60 | -0.05 |
| 29.45 | 29.95 | 0.50 | 0.06 |
| 31.85 | 32.45 | 0.60 | -0.02 |
Course Profile Every 5k
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