I like numbers. That probably won’t come as a surprise to anyone who knows me particularly well.
Weight, heart rate, pace, power, distance, calories, training load, recovery, sleep — whether they’re going up or down, they give me something tangible to interact with. I’ve realised that they’re becoming a surprisingly important part of this journey, not because I want to reduce everything to a spreadsheet, and certainly not because every session needs to produce a personal best, but because numbers tell a story.
Sometimes they tell me that what I’m doing is working. Sometimes they tell me that it isn’t. Both are useful.
It’s very easy to look at fitness data and decide that one direction is good and the other is bad. Weight down: good. Running pace faster: good. Cycling power up: good. Heart rate lower for the same effort: good. But I don’t think that’s quite how I see it anymore.
If my weight increases, that’s information. If my running pace gets slower, that’s information. If my heart rate is unusually high for an effort that normally feels comfortable, that’s information too. The number itself isn’t really the problem; what matters is what I do with it.
Numbers either give me reassurance that I’m moving in the direction I want to go, or they encourage me to ask why I’m not. That interaction is something I find motivating. Instead of relying entirely on whether I feel fitter, I’m able to look for evidence that something is changing.
Sometimes the progress is obvious. Other times it’s buried in something quite small: a few extra watts on the bike, a slightly lower average heart rate, another percentage point closer to 100% of my goal weight, or a run at the same pace that suddenly feels easier. None of those things is transformational on its own, but together they build evidence.
One of the more interesting things about having years of training history is being able to look backwards, not nostalgically, but analytically. I’ve got enough running and weight data now to look at the relationship between how much I’ve weighed and how quickly I’ve been able to run.
Across roughly eight years of data, the relationship works out at approximately 4.7 seconds per kilometre slower for every additional kilogram of body weight.
That’s quite a number.
It would be tempting to extrapolate a straight-line performance gain from that relationship, but of course it isn’t that simple. Weight and training are very obviously linked. When I’ve been lighter, I’ve generally also been training more consistently. My cardiovascular fitness has been better, I’ve been running more frequently and my body has been more accustomed to running. Likewise, the periods where my weight has increased have often coincided with less training.
So this isn’t a laboratory experiment proving that one kilogram equals exactly 4.7 seconds. It’s a relationship within my own historical data, and that’s what makes it useful.
It gives me another way of visualising what I’m working towards. If my progress continues towards 100% of my goal weight while the training continues to build, there’s every reason to expect the running performance to improve with it. The interesting bit will be watching whether it does.
There is probably a personality trait at work here too. I’m naturally inquisitive. I like understanding why things happen. If something changes, I want to know what caused it, and if something can be measured, there’s a fairly good chance I’ll want to measure it.
That overlaps rather neatly with another fairly significant part of my life: motorsport.
I’ve been involved in motorsport for many years and I’m a former car racing champion, having won a championship in 2019. Motorsport might look from the outside like somebody simply driving a car as quickly as possible, but at virtually every level performance is increasingly driven by data.
Lap times are the most obvious measurement, but underneath them are dozens — sometimes hundreds — of other pieces of information. Speed, throttle position, brake pressure, engine parameters, steering input, tyre temperatures, sector times, corner speeds, longitudinal acceleration, lateral acceleration — all of it helps build a picture of what the car and driver are doing.
A driver can say, “The car doesn’t feel quite right through that corner,” but the data can help answer why.
Perhaps the driver is braking five metres earlier. Perhaps they’re carrying less minimum speed. Perhaps they’re getting onto the throttle later. Perhaps the problem isn’t actually the corner they’re complaining about at all, but the way they’ve positioned the car several seconds earlier.
Feeling identifies the question. Data helps interrogate it.
I’m beginning to realise that I’m applying exactly the same mentality to myself. Except this time, I’m the machine.
If a bike session feels unusually difficult, I can look at heart rate and power. If my running improves, I can compare pace with heart rate and progress towards my goal weight. If I’m struggling to recover, I can look at training volume, sleep and nutrition. If my progress towards my goal weight stalls, there’s data available to help work out why.
That doesn’t mean blindly following numbers. Data without context can be just as misleading as having no data at all. A terrible night’s sleep, unusually hot weather, illness, stress or accumulated fatigue can change a workout considerably.
The skill is in understanding what the numbers might be telling me rather than allowing a single number to dictate how I feel about myself.
That’s exactly the same in motorsport. One lap doesn’t necessarily tell you very much. Patterns do.
The scales are inevitably one of the most prominent measurements in what I’m trying to achieve, but I’m increasingly conscious that progress towards my goal weight alone would be a pretty poor way to judge whether this is working.
Suppose my progress towards 100% of my goal weight barely changes for a fortnight but my cycling power increases. That’s progress. If I can run further at the same heart rate, that’s progress. If my resting heart rate improves, that’s progress. If I recover more quickly, train more consistently or simply feel capable of doing more, that’s progress too.
And sometimes the opposite is valuable.
If those numbers start moving backwards, I don’t necessarily need to be disappointed. I need to be curious.
What’s changed? Have I increased the training too quickly? Am I recovering properly? Is my nutrition appropriate? Am I simply having a bad week?
The numbers aren’t there to pass judgement. They’re there to ask questions.
That is also why I’ve become increasingly interested in developing the technology behind Shift-Spark. At the moment, the information is scattered everywhere. Garmin knows some things. Strava knows others. My nutrition app knows what I’ve eaten. The scales know my progress towards my goal weight. The bike knows my power.
Each platform presents its own little view of me, but what I’d eventually like Shift-Spark to do is pull those pieces together.
Not because the world desperately needs another fitness dashboard, but because I want to see my own story developing in the numbers.
Imagine being able to look back over a year and see progress towards 100% of my goal weight, cycling power, running pace, training volume and heart rate moving alongside one another, then compare that against what I was writing and how I was feeling at the time.
That would turn this website from simply being a collection of posts into a genuine record of the journey.
There is also one measurement missing from Shift-Spark at the moment: lap time.
Motorsport is still a huge part of what motivates me, but right now I’m not in a position where I feel particularly happy getting back into a racing car. That’s something I want to change.
In 2019, the numbers were helping me understand how to make myself and a racing car faster. Right now, I’m using numbers to understand how to make myself healthier, fitter and more capable.
I hope that somewhere further down this journey I’ll be able to write the opposite version of this article.
Instead of taking what I’ve learned from motorsport and applying it to my fitness, I’ll be comfortable enough to climb back into a racing car and start applying the numbers in their more traditional direction again.
Heart rate and progress towards my goal weight might give way to throttle traces, braking points, corner speeds and lap deltas. Perhaps there will even be another championship to chase.
That’s a long way down the road.
For now, though, the data has a simpler job: keep measuring, keep questioning and keep learning.
When the numbers change — whether they’re going in the direction I expected or not — I want to pay attention, because they’re probably trying to tell me something.
