One of the best features of Strava is the battle to be King (or Queen) of the Mountain. Originally, in cycling, segments were typically climbs or difficult sections of road, and the simple idea, is who can complete the segment in the quickest time. Hence they would be KOM/QOM, King or Queen of the Mountain. Segments quickly expanded to pretty much any section of a course and to include running segments, to separate them from cycling.

It’s been a while since I posted a breakdown of half marathon times. The last time seems to have been 2018. I decided to give my old code a clean-up and quickly crunched the numbers from the 2022 Kenilworth Half Marathon. First, the results: Briefly, the code below reads in a csv file of race results downloaded from the provider.

As 2021 draws to a close, it’s time to check back in on a previous post. As a recap, Garmin Connect offered four challenges to run 3 x 505 km and 1 x 505 km, one in each of the four quarters of 2021. Completing all four would mean running 2021 km in 2021 . I used an R script to check my progress throughout the year (available here). Each quarter gave a set of results, the final set for the whole of 2021 is shown here.

Reading about someone else’s recovery-after-injury story can be a bit dull. At least that was my conclusion after pressing delete on my story a moment ago. Having spared you the details, the summary is: I got injured. It hurt. It took me a year to recover because I didn’t tackle the recovery properly.

Many running races and events have been cancelled or disrupted due to the pandemic. To fill the void, I’ve been taking on Garmin’s “Challenges”. In Garmin Connect, you can accept a challenge set by Garmin (I think users can challenge each other too). Completion of these challenges gives the user points, which I’ve become somewhat obsessed with.

Garmin Connect has a number of plots built in, but to take a deeper dive into all your fitness data, you need to export a CSV and fire up R. This post is a quick guide to some possibilities for running data.  There’s a few things that I wanted to look at. For example, how does my speed change through the year? How does that compare to previous years? If I see some trends, is that the same for short runs and long runs?

On a whim a posted a plot on Twitter. It shows a marathon training schedule. This post explains the story behind the graph. I downloaded a few different 17-week marathon training schedules. Most were in imperial measurement and/or were written for time at a certain pace, e.g. 30 min Easy Run etc.

The Green Leek 10.5 km run is a mixed terrain race now in its third year. Today’s was a wet and muddy edition. The chip times were posted this afternoon and using my previous code, I took a look at the results. I was a bit disappointed with my time, which was about 24 s slower than last year. Considering that I’m running faster this year than last, I wondered whether the conditions affected my time.

I’ve previously crunched times for local Half and Full Marathons here on quantixed . Last weekend was the Kenilworth Half Marathon (2018) over a new course. I thought I’d have a look at the distributions of times and paces of the runners. The times are available here. If the Time and Category for finishers are saved as a csv, the script below works to generate the following plots.

I recently got a new GPS running watch, a Garmin Fēnix 5. As well as tracking runs, cycling and swimming, it does “activity tracking” – number of steps taken in a day, sleep, and so on. The step goals are set to move automatically and I wondered how it worked. With a quick number crunch, the algorithm revealed itself. Read on if you are interested how it works.