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Looking back at my year in running data
Hi, I’m Alex, a designer at Datawrapper focused on interface and experience design. When not iterating on Datawrapper’s interactions, you can find me taking care of my small garden, playing volleyball, or jogging around Berlin. That last one is what I’m writing about today…
For many years I’ve made running a part of my regular training routine. Not only is it healthy to do cardio, I also enjoy how a run gives me time for listening to music, podcasts, and just my own thoughts. Even with something as simple as running straight ahead, I find it helpful to have a goal to aim for — so for the last two years that goal has been to run 1000 kilometers over an entire year.
I use an Apple Watch to track my running pace and progress. I’m pretty fascinated by the amount of data those trackers can collect over time, but in the end Apple’s own visualizations in their Health app are pretty basic. I was always interested in looking more deeply into my routines. Of course there are other apps out there, like Tempo or Peak, but for this week I wanted to get my own hands dirty and see if I could find something interesting.
Last year I missed my goal by 150 km. That was quite a lot and I couldn’t just leave it at that, so I decided to tackle the same goal again in 2023. As you can see, I’m still not quite there yet. I have about 49 km to go — about 4–6 running sessions for me, which I’m confident I can do.
In the search for interesting patterns, I wanted to see when in the day I normally go running and whether that affects my performance. It seems I’m a pretty consistent runner: I usually like to run in the mid afternoon or late evening. Interestingly, I tend to run a little faster at night. Streets are getting empty in Berlin, during the warmer seasons the temperature cools down and it’s a great power-finish for the day.
Last but not least, let’s take a look at my running habits over the whole year. To reach my 1000 km goal, I would have to log about 84 km per month, which I managed to do most of the time. I took quite a big break in the beginning of July, when I was traveling and attending festivals. The other longer breaks are mostly sick time.
Want to compare your running data?
All of the raw data was collected automatically by the built-in Health app on my iPhone and Apple Watch. The Health app supports data export out of the box — tap the profile image in the top right and then choose “Export All Health Data.” Depending how long a period you’re looking at, that can be quite a lot of data and export might take a while. In my case it was 4.7 GB of raw XML, CSV, and GPX files.
To speed things up, I decided to use an app called Simple Health Export CSV. The free app helps you to export only data from a certain time range or for certain activity types. This gave me an easier-to-handle CSV file, which I then parsed and prepared in Google Sheets.
What I learned
It was interesting to see the difference in my average pace depending on the time of day. That validates the feeling I have while running: My body is just more ready to work out at late hours. Other than that there were no obvious patterns I could spot from this year — to be honest, I was expecting to see more variation in the data.
As for my yearly goal, I think I can still hit it the next weeks! It will be tight with Christmas around the corner, but after eating all the candy and gingerbread cookies it’s refreshing to go for a run.
That’s it for my first Weekly Chart. I hope I can inspire you to also set goals and take a look at your journey, whether it’s related to sports or anything else. What kind of personal data do you like to look at? Are there any areas of your live you’d wish for more insights on?
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