2023 has been a great year in running for me. Previous running round-ups are here (2022, 2021). My two main goals for 2023 were to run 3000 km and also to run 50 HM-or-more distance runs. I managed both with a couple of weeks left. I also bagged new PBs for 5K, 10K and half marathon as well as a handful of segments on Strava. I won no races but I did win two little running competitions at work.

2022 was my best year for running to date. In 2021, my goal was to run 2021 km. For 2022, I wanted to see if I could run 2500 km and also to run 50 HM-or-more distance runs. I managed both and ended the year on a total of 2734 km. I also bagged two PBs for half marathon. Of course, if you subscribe to Strava or VeloViewer or whatever, you can get a nice data visualisation of your year in running.

There are lots of ways for runners and cyclists to analyse training data. A key question most fitness enthusiasts want to know is “how am I doing?”. “How you are doing” is referred to as form . Unsurprisingly, form can be estimated in many ways. One method is using training stress scores (acute training load and chronic training load) to assess form as training stress balance.

By 30th September 2022, I had clocked up a total of over 2000 km of running in 2022. This milestone was a good opportunity to look at how I got to this point. The code is shown below. First, we can make a histogram to look at the distance of runs. From this type of plot it’s clear that my runs this year consist of a lot of 4-5 km runs and then a chunk of 21 km plus.

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.

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.

One of several features that Strava put behind a paywall was the ability to compare performance on similar courses. I miss this comparison tool and wondered how hard it would be to code my own. This post is a walkthrough of how I approached the problem. The code is available here. It uses the trackeR library in R to convert the GPX tracks to a huge dataframe. This is then processed by IgorPro.

Joe Friel reposted an article earlier this year on Efficiency Factor in running. Efficiency Factor (EF) can be viewed in Training Peaks software and he describes how it is calculated. This post describes how I went about calculating EF in R using a single gpx file. What is Efficiency Factor (EF)? Essentially, EF is the average distance that you are propelled forward per heart beat. The higher the number, the more efficient you are at running.

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?

I’d seen the small multiple artwork of running and cycling routes from Marcus Volz’s R package Strava all over the web. Ads for “posters of your GPS tracks” pop up on Reddit and I’d notice a few #Rstats people put up their posters on Twitter. I’ve had the package bookmarked for a while and this week I finally got round to generating a small multiple poster of some of my cycling routes.