The technology to map the way we move on a bike is rapidly growing and increasingly available to the masses. It certainly impacts the way we ride, but how does it shape the way we think?
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7 Apr 2015 - 11:32 PM  UPDATED 13 Apr 2015 - 3:38 PM

It's no secret that technology changes the way we move on our bikes. Bike materials and design are constantly evolving pushing performance abilities and experiences into new territory. Just ask a mountain biker about the wheel size debate, or Chris Froome about his new skinsuit.

As more technology develops to map the ways that we move, our approaches to training and racing radically change as well; how we think about movement, how we pace ourselves, how we reflect on a ride as being a good one or bad.

Earlier this week an article was published on Cycling Tips by Helen Kelly and Matt de Neef. It examined the power profile of Gracie Elvin on her way to winning the National Road Race last weekend.

What made the article such a good read is the way it takes complex data and puts it into terms that make sense to the keen cyclist. The numbers are discussed in relation to key points in the race: breaking away, catching a rider in front, reeled back in by the bunch, being dropped on the final climb, the screaming effort to claw back to the leaders, the sprint at the end.

I see these numbers and I think of the pain, motivation and training that is part and parcel of performance at this level.

I also think about how they work together with 5000 or so other 'one percenters' to make the performance stick: bike and equipment choices, nutrition and recovery strategies, the skills to read the race and make decisions in regards to her own plan, her team's plan, and how her body is responding on the day, environmental conditions, the list goes on.

Mostly, a basic knowledge of what it takes to produce those numbers makes one part of me wince, and another part simply marvel at Gracie as an athlete.

Data monitoring of the type in this example, or the more specialised scientific investigations as detailed earlier this week by Carmine Sellito on The Conversation drive our knowledge of sport, especially in the areas of tactics and training.

Analysis at this level is typically used by athletes at the higher end of a sport, or in the case of power meters, with the money to invest in reaching personal performance goals. Meanwhile, the uptake of Strava among cyclists brings post ride analysis to the masses in increasingly interesting ways.

I've written before about my thoughts on some of Strava's positives. And it certainly has some negs; an assumption about accuracy being one of them, and people's attitudes when they get fixated on segment hunting being another.

Yesterday, on a regular training loop I reached the bottom of a familiar hill. I decided to give it a crack.

I had two goals. The first was to see where my form is after a pre- and post-Christmas diet high in Toblerones and chocolate fudge brownie ice cream. The second was to hurt my legs a bit in preparation for more hurt next week at the Pure Tasmania Wildside mountain bike stage race.

Strava took care of timing the segment for me, which I appreciated, as I have no power meter or heart rate monitor and forgot to look at the clock. I rode hard, hurt, and remembered that I don't like hurting that much.

Then I thought about Gracie. I thought about the effort required for the numbers she put out, and the psychological experience of fighting hard on the climb, watching the competition edge away, pounding her way back to the leaders.

I thought about the motivation it takes to do all that and still be able to produce a massive effort at the end.

Thinking about someone else's hurt seemed to make mine disappear. Kind of like pinching your forearm so your knee stops hurting, or turning up the radio to block out the noise on the street. I figured Gracie must have hurt a whole lot more than I was, which made my hurt drop a few notches lower on a subjective scale of 10.

Later that day I found out that I'd annihilated my local little climb. (The Toblerones must have been helpful after all.)

Data, in this case, vague as it might be, is certainly a nice guide to have, particularly when it tells you that you're meeting your own goals or riding better than you expect. What I didn't expect was the impact that reading about someone else's efforts had on the mental approach I took to my own.

What interests me most, as a cyclist and as a Performance Studies academic, is the relationships between these new sources of data and how they make us think while we're pedaling; how our thoughts and motivations change to reflect new knowledge, insights and goals.

I find these new methods of measuring movement exciting. They give visibility to things we've previously found far more difficult to judge, reflect upon or see. With that comes different strategies, and new areas of performance to focus on, smaller goals along the way can quickly build to bigger picture outcomes.

Some days it's important to chat with your mates as you pedal or take in the view. On others, renewed motivation to push a little harder can be important too.

How you take new data driven ideas about cycling and use them to shake up your own riding goals…the answer to that is up to you.