I Imported 3 Years of Strava Data Into TNR — Here's What I Learned About My Training
by The Next Race
I'd been on Strava since 2022. Three years of runs, rides, swims. Every session logged, every race uploaded, 847 activities sitting in an account I opened on my phone and mostly used to check who in my training group had gone further than me on Saturday.
When I connected it to The Next Race, I expected to see my data in a slightly different format. What I actually saw was a pattern I hadn't noticed in three years of logging.
What the data showed
My swimming peaked in January and February every year and collapsed by May. Every year. Without fail. I swim well in winter — pool access is easy, motivation is high, I'm not distracted by the bike and run sessions that feel more urgent as race season approaches. By the time spring arrives, swimming drops to one session a week, then zero.
The problem: my races are in June and July. I was arriving at my A-races with my swim fitness at its annual low point.
I knew I was a weak swimmer. I didn't know that my training schedule was structured in a way that guaranteed I'd stay one.
This is the kind of thing that is genuinely hard to see when you're logging sessions week by week. You know you swam less this week than last week. You don't know you've been doing this exact thing every April for three years.
The bike discovery
My long rides were long, but not very hard. I have a lot of data showing me riding for three, four, five hours at what Strava classifies as moderate intensity. I thought these were productive training sessions. They looked like productive training sessions.
What the cumulative picture showed: my average power on these long rides had barely changed in two years. I was accumulating volume without accumulating adaptation. I was getting fitter at riding slowly for a long time. This is a real fitness quality. It is not the one that improves triathlon performance.
The athletes I was racing against on the bike — the ones who came out of T1 a minute behind me and caught me before the turnaround — were probably not riding more than me. They were riding harder, more specifically, more often.
What I did with it
Built a training plan that addressed the actual weaknesses revealed by the data, rather than the weaknesses I'd assumed I had.
More swimming in April, May, and June — specifically, not as a vague intention. Scheduled, non-negotiable sessions that didn't disappear when the weather got good and the bike felt more appealing.
Shorter, harder bike rides alongside the long ones. One threshold session per week where the goal was quality over duration. This felt less impressive on Strava — 90 minutes versus four hours — and produced significantly better results on race day.
The broader lesson
Three years of data with no analytical layer is just a diary. It tells you what you did. It doesn't tell you what it meant.
The value of tracking isn't the tracking — it's being able to ask the data questions. Why was June 2023 my fastest race and June 2024 significantly slower? What does the six weeks before my best performance actually look like? What changed?
If you've been training for more than a year and logging consistently, you have more useful information than you realise. The question is whether it's stored somewhere that lets you use it, or whether it's sitting in a spreadsheet or an app waiting for you to scroll back through it manually.
Your past training is the best predictor of your future performance. The data is already there. You just need a way to look at it.