This post will be not cover particularly novel ground if you pay any attention to professional cycling. Many of the conclusions are obvious. However, if you find yourself in the know already, trust that this post is the necessary building block for more interesting work.
Fundamentally, cycling races can be viewed as war of attrition. As I laid out during this post and Dr. Seiler discusses in his video here, races normally feature a long stretch of steady efforts, before the pace is ramped up towards the end. This steadily increasing pace towards the back-end of the race is what creates separation between riders in most races. The exception is in some primarily flatter races which just do not feature the type of topography which results in time gaps and so the peloton finishes together in a bunch sprint. In all other races, separation is typically created – particularly on hills and/or mountainous sections of the race – but also on cobbled roads, gravel/poorly surfaced roads, in crosswinds, etc.
So that is a fan’s understanding, informed by some limited studying of power outputs on significant climbs across a large sample of races. However, we can leverage an even larger data-set of individual race segments on all types of flat, uphill, downhill, poor surface roads, etc. I’ve gathered a data-set of rider speeds on different length race segments primarily from 2020 professional season to do just that. There are 22,500 unique segments in this data-set covering 177 races.
What Produces Separation?
The metric of choice for showing separation is the time difference between 90th percentile in speed and 10th percentile in speed on a segment, divided by the median speed over that segment. Eg, if 90th percentile is 27 km/h, 10th percentile is 20 km/h, and median is 22 km/h the Separation Factor is about 32%. That is fairly high among all segments where the mean is 12% and median is 7%. The max Separation Factor for the average race is around 48% – typically a short segment.
Essentially treat the Separation Factor as the percentage difference in speed between riders racing the fastest and those racing the slowest. On the nine decisive final climbs in the 2020 Tour de France the Separation Factors averaged 29%, ranging between 18% for Col de la Loze and 39% for Orcieres-Merlette.
Separation is primarily created by higher gradients. This is maybe the most blindingly obviously statement I’ve ever made, but there it is. Flatter or downhill segments created very little separation among the group on average, while uphill segments create increasingly more as the gradient increases from about 3% to over 10%.
When comparing segments on cobbles vs similar gradient segments on normal roads the rougher roads show a highly statistically significant difference of about 5 to 8% larger Separation Factor for cobbled sections vs normal roads, depending on how it is modelled (a model with gradient included tends to diminish the impact as many cobbled/white road sections are also uphill). The impact here is roughly a Separation Factor of 9% for a flat, non-cobbled segment vs 14% or higher for a flat, cobbled segment.
And replicating the work done previously showing that power varied more in later stages of the races, segments further towards the end of a race provide for more separation than those earlier in the race, with the most significant increase in roughly the last third of the race.
Where is Separation Largest?
Clearly segments further through the race have the highest separation between fastest and slowest riders. But where does the moment with the largest separation occur in these races? For this sample of 177 races, the key moment on average is 88% through the race, with about 40% of races having this key moment in the last 3% of the race (or last 5km for a typical 180km race). It’s important to note a segment is counted as occurring based on where it ends within a race.
Again, to any fan of cycling the knowledge that the largest time gaps occur near the end of a race – particularly on summit finish climbs – is not novel. However, this data does show how rare it is for the segments which produce the largest time gaps to occur anywhere in the first half of the race.
Science of the Peloton 