As the obsessed data nerd I am đ¤, I wanted to be able to reflect on my 2024 running journey so that I can better plan for 2025. This lets me answer questions like:
- How well did I train in 2024?
- Did I leave anything on the table? How do I improve?
- How can I actually improve?
If you use a fitness watch like an Apple Watch, then all of the data is recorded for us to use! Downloading your data from Apple Fitness is as easy as clicking a few buttons (and, ok, maybe an obligatory swear at the XML data format) – go to your Health app, click on your profile in the top right, and finally click on âExport all Health Dataâ in blue on the bottom. This exports a file in a criminal XML format, but from there we can process this data with Python and use Plotly to create all plots and interactive visualizations.
In this series, weâll be demonstrating how to use this data to draw health and fitness insights – I will be using this to plan for a 3 hour Boston Marathon Qualifier race in 2025!
What can we do with this data?
The raw data unlocks a level of customization that Appleâs native tools just donât touch. Think of it as your DIY toolkit for becoming a fitter, faster, and smarter athlete. Some cool experiments Iâve tried include spotting training plateaus with change-point detection, and optimizing routes to get a difficult hill workout in.
This data allows us to investigate loads of other areas as well including:
- đŚžÂ Biomechanics – we can understand our running form using measurements like stride length, ground contact time, and vertical oscillation.
- đŤHeart rate analytics – is your heart thriving or barely surviving after that hill workout? Metrics like cardiac strength and VO2 max are example indicators well prepared your heart is for race day.
- đđźÂ Workout analytics – from splits to weekly mileage, this data will help you optimize your training planâand maybe earn bragging rights on your Strava profile.
- đŚď¸Â Environment impact – how does your performance change with different weather conditions? For me, humidity is such a killer.
- đď¸Â Recovery – we know we need to rest to build muscle, but are we resting sufficiently between workouts?
Who cares?
I assume youâre at least partially a data nerd if you train with a tracker like an Apple Watch – while Appleâs rings are a great nudge to exercise, I think we can go so much deeper to use this data to understand whatâs working and whatâs not. For myself personally, I found my weekly training volume was far too low and slow for the types of races I want. If youâre the kind of person who loves tracking your progressâor just wonders why your training sometimes feels more like survival than progressâyouâre in the right place.
In this series, weâll use the fitness data export as a case study to cover topics like:
- Setting up your data pipeline and data models: How to turn that XML file into something you can actually analyze.
- Creating your first visualizations: Spot your strengths, weaknesses, and everything in between with Python and Plotly.
- Discovering patterns in your training: Learn whatâs holding you backâand whatâs pushing you forward.
- Building a personal dashboard for ongoing tracking: Keep all your key metrics in one place, updated automatically.
Whether you’re a data enthusiast or someone striving to train smarter, this series will combine technical insights with practical strategies to help you achieve your fitness goals. For more projects and insights, connect with me on LinkedIn and YouTube.
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