Belgian Groundhog Day

Another classics season approaches and so does another chance to put lessons learned back into use again. Only each time the riders return the scenarios change and if you tried to replay each race the results would vary. The varying conditions and the random elements are an essential part of what makes the cobbled classics so compelling.

“If I knew then what I now know” is a maxim to experience and wisdom. It counts everywhere including sport and in particular in cycling where the top riders can often be in their late 20s and early 30s. Peter Sagan is the exception at 26 but still older than all the other high earners. At the last Olympic games the average age of endurance cyclists participating, whether on road or MTB, was significantly older at just above 30 than most other sports except for triathlon, shooting and equestrian sports.

Experience counts for plenty in the spring classics where there’s a lot to learn from technique to the lie of the land, knowing which side of the road to be on in order to get the best line into a crucial corner, which stretch of pavé has a smooth side and where to find shelter from buildings or woodland and the opposite, which stretches of road are wide open to a slight breeze. If this was a computer game you could start the level again but in cycling the Omloop, Ronde and Enfer du Nord are only once a year. Besides, make as many notes as you want from one year and you’ll return the next year to find the wind blowing from a different direction or the course has been changed. Valuable experience and knowledge can take years to acquire.

The Monte Carlo Groundhog

Imagine you wake up every day and find it’s Sunday 26 February 2015 again. A Belgian version of Groundhog Day where you find yourself in Gent and the Omloop Het Nieuwsblad is about to start. It could be a dream for some or a cruel nightmare. You know you’re trapped in this loop but the rest of the world is oblivious. The sun rises, the riders assemble and the race starts but there’s no guarantee the race plays out the same. It’s like rolling dice, there’s a probability of some outcomes but each roll, each time is different. Statisticians talk of “Monte Carlo simulations” where a computer crunches every scenario in a model with a range of random outcomes. No computer can re-run the Omloop so use your imagination instead. For example on the second day you wake up Sep Vanmarcke deviates three millimetres to left of the Haaghoek cobbles and misses the object that made him puncture last year and it changes the outcome of the race. On the third day something else happens and so on, each time the race is different, each roll of the dice different. Of course the strongest riders will crowd out the weak ones but the results, outcome and story will vary each time. Compared to Groundhog Day the film, the sports version would deliver more varied scripts each time.

Of course ex post we can rationalise and create a story to explain how the winner won but we’re wise enough to know that however good they were their victory owed itself to a set of tactical circumstances too, which they of course exploited along the way. It’s this that makes watching the classics so compelling, the random elements mean that the strongest rider cannot just ride away. Run a Monte Carlo simulation of a mountain stage or, worse, a time trial and the outcomes would be much more predictable. A rider with a set power, a known weight and a fixed position on the bike will repeat performances. A mountain pass quickly refines the peloton and creates an instant hierarchy based on watts and kilos while a time trial removes the tactical interaction.

If this lucky aspect of the spring classics is cruel it’s what makes the viewing exciting, we don’t know what’s going to happen next. It’s not always unfair because if a rider loses out in this Saturday’s Omloop there’s Kuurne-Brussels-Kuurne on Sunday. There are races several times a week until Paris-Roubaix. So if a rider is strong but unfortunate they’ll get their chance soon enough. Up to them to seize it.