Running Faster

As a result of my injury this year and subsequent treatment (getting my knee drained & a shot of cortisone), I’ve had to take some time off from running this year.

In fact I didn’t log a single mile for 7 weeks, including the entire month of October.

I’m well below my yearly average over the last 5 years but I’m just happy to be pain-free and back to the grind again.

One of the things I’d like to do better in the coming months & years is to get my speed back up to where it was before my injury and then see how fast I can get. I hate to even speak this out loud, but I’d love to run a sub-4 hour marathon by the time I hit my 45th birthday.

That means I’ve got a little fewer than 4 years to optimize my running to such a point that I’m roughly six minutes faster than my PR.

No pressure.

Now the inimitable Extraface (Dave Coustan) shared a link on his wonderful Slack1 – “Fancy Running Shoes Really Do Seem to Make You Faster“.

I’m no fan of Nike shoes (save for my dad’s original waffle trainers which were cool as hell), but the research seems pretty compelling. Wired even did their own ad hoc skunkworks study at the New York City Marathon which seems equally promising that these shoes (and potential new versions like them) have measurable performance benefits for long-distance runners.

But $250 is a lot of money to spend on shoes, especially when I try to have 2-3 pairs I rotate to keep my feet feeling fine & the treads new. Plus it’s a lot to ask runners to change their kicks, since we’re notoriously finicky and we tend to stick with a model once we find one we love.

The flip side of the coin – at least for me – is tracking and data. I love my running app, iSmoothRun, on both my phone and my Apple Watch but I’m always looking to learn more from my running data.

One way to do that is crunching the numbers more effectively. I’m this close to buying a Pro membership to SmashRun, my current favorite place to see learn about myself through statistical analysis. (I also did a fun mapping project almost exactly a year ago to the day. You should check it. It’s cool.)

The other route I could go is to get a “smart shoe” like the Altra IQ version of my current running shoe, the Torin IQ. That way I’d get to stay in the shoes I know, love & run in while getting more data. Of course I’d have to drop $220 which is almost as much as the “fast” Nike Zoom Vaporfly 4%.

Still another option would be to “roll my own” smart shoes by adding something like the MilestonePod ($30) or the Stryd ($200) to whatever pair of shoes I use. Both seem simple to setup and swap out, it’s simply a matter of how much I’d like to spend and how much more data I’d like to track.

Both units also currently work with my running app, so there’s that too.

In the end what I’ll likely do is just keep running more often. Work my way back up to my pre-injury weekly mileage and see what that gets me.

If and when I feel like I need better gear – either to track my efforts or to make me faster just by virtue of lacing up – I’ll make the change.

Until next time, see you on the road!

  1. You should join his Slack or, at the very least, subscribe to the email list that spawned the community – Three Banana Thursday – for more fantastic stuff like this.

Regular Running

As someone who’d hoped to run 1,000 miles this year (Update/Spoiler Alert: I’m going to fall about 80 miles short), I have a regular running route that I follow.

My most common run is the one I do during the week, during my lunch or some time in the afternoon, around the campus of Georgia Tech. This is a pretty popular urban ‘trail’ known as the Pi Mile and I can extend it from 5k to around 7k by running a bit longer on 10th Street, depending on the amount of time I have on any given day.

After doing some detective work using SmashRun, I determined that I’ve run this route 59 times in 2016!

I did a little bit of file conversion and “wrote” some additional code since my last mapping project, and ended up with some fun visualizations of all that data.

Here, then, are variations of a heat map of all my Pi Mile runs in 2016:

Here’s the actual source code I used myself to create all the maps above:

library(plotKML)
library(ggplot2)
library(ggmap)

# GPX files downloaded from Runkeeper
files < - dir(pattern = "\\.gpx")

# Consolidate routes in one drata frame
index <- c()
latitude <- c()
longitude <- c()
for (i in 1:length(files)) {
    
  route <- readGPX(files[i])
  location <- route$tracks[[1]][[1]]
  
  index <- c(index, rep(i, dim(location)[1]))
  latitude <- c(latitude, location$lat)
  longitude <- c(longitude, location$lon)
}
routes <- data.frame(cbind(index, latitude, longitude))

# Map the routes
ids <- unique(index)
plot(routes$longitude, routes$latitude, type="n", 
axes=FALSE, xlab="", ylab="", main="", asp=1)
for (i in 1:length(ids)) {
  currRoute <- subset(routes, index==ids[i])
  lines(currRoute$longitude, currRoute$latitude, col="#0066FF20")
}

# Plot over map of campus
GnatsMap <- qmap(location = 'Georgia Institue of Technology, Atlanta', 
zoom = 15, maptype = 'satellite', source = 'google')

GnatsMap +
  geom_path(aes(x = longitude, y = latitude, group = factor(index)), 
  colour="#1E2B6A", data = routes, alpha=0.3)

All the GPX files (which you can get from Strava) need to be in one directory when you run the script in R.

To change the color of the routes, modify this hex value:

for (i in 1:length(ids)) {
  currRoute < - subset(routes, index==ids[i])
  lines(currRoute$longitude, currRoute$latitude, col="#0066FF20")
}

To change the underlying map, change this portion:

qmap(location = 'Georgia Institue of Technology, Atlanta', zoom = 15, 
maptype = 'satellite', source = 'google')

Many thanks to the code of Saul Torres-Ortega and Frazier at UCSB.

Refer back to this PDF if you need additional help fussing with the underlying map. If the parsing of the GPX files is the issue, I’d look at the original code I borrowed.

One of the things that jumps out at me, if you look solely at the heat map (without geo data), is that the data is really noisy where/when I start my runs (upper right side). As you can imagine I’m not running across 75/85 in Midtown Atlanta, but that’s what the data shows.

Probably just the nature of tracking GPS with a phone, but the fidelity of the rest of the data seems solid. You can tell at one point when I’m choosing to run on one side of the sidewalk versus the other (lower right side, near Bobby Dodd) and the rare occasions – when I extended a 5k/7k into something more like a 10k – those are the thinner, lighter lines on 10th Street and some of the streets interior to tech’s campus (mostly left side of the map).

If you want to see another cool visualization of the same area of midtown using public running data from Strava from 2015, it’s also pretty cool.

Until next time, Run Happy!