Cycling has been part of my life for as long as I can remember. As a kid, I was already fascinated by the Tour de France — the jerseys, the teams, the numbers and statistics behind the race. For years, I had the idea of creating a cycling blog, but I was missing the right angle. With Pelotonomics, I think I found it: looking at professional cycling through the lens of business, economics and data.

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That connection feels natural to me. I have always enjoyed comparing sport and business: how organisations compete, how money influences performance, how brands invest, and why some strategies succeed while others fail. Professional cycling is a particularly fascinating case, with its unusual business model, changing sponsors, increasingly powerful teams and race organisers, and a sport that is becoming more global and professional every year.

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My own cycling story started relatively late, around the age of 20, initially on a mountain bike. MTB remained my main discipline for many years, with a top-100 finish at the Cape Epic in 2015 as a personal highlight. I later moved towards road cycling and, more recently, gravel. As I get closer to 50, the road has somehow become the part of cycling I enjoy most.

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Pelotonomics is also a personal experiment with AI. Professionally, I wanted to better understand the rapidly evolving possibilities of artificial intelligence, and building a real project seemed more useful than simply reading about it. AI therefore plays a role throughout this blog — helping with research and writing, exploring datasets, building charts and creating visuals — while the topics, angles, analysis and editorial choices remain mine.

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In a way, Pelotonomics brings together several things that have interested me for years: cycling, business, numbers and technology. The goal is simple: to look beyond the racing itself and better understand the economics of the peloton.

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