Tony Martin won his third Worlds ITT title yesterday with an extraordinary average speed of 32.3mph over 35.3 miles. Let's put Martin's performance in context: his ITT time was faster than all but five TTT times put down on the same course the previous Sunday (Martin's OPQS team won that competition with an average speed of 32.9mph).
In other words, Tony Martin alone came within 3 seconds of beating Fabian Cancellara + RadioShack.
Because this year's TTT and ITT courses were exactly the same, we have the rare chance to compare performances of teams and individuals. We can assume identical independent variables (in this case, environmental factors that influence speed such as road surface, distance, elevation) excluding wind* are identical.
Here are the performance curves of the two:
The speed of top individual riders is surprising. You can see if you click on the above graphic how the top few riders in the ITT are surprisingly faster from the rest. You can see this in the ITT curve; it has a slight upward twist at the end. The average difference between all the competitors is .08mph; this sets the slope of the red line. The red line is steeper at the top because the margin between average speeds among the top ten finishers jumps to 0.13mph.
This defies expectations for a number of reasons, but especially for two reasons: (1) the graduated way we think of human performance and (2) physics; notably, the exponential relationship between power and speed.
Here's what I mean (using this calculator):
12mph requires 60 Watts
15mph requires 100 Watts
An increase of 3mph from 12 - 15mph requires 40 more Watts.
25mph requires 387 Watts
28 requires 500 Watts
An increase of 3mph from 25 - 28mph requires 113 Watts.
Here's a graphic showing the relationship:
Here's another way of looking at it: cohorts
1. 7 men averaged between 31-33mph;
2. 50 averaged 29-31.
3. 19 averaged 26-29
This is not quite the classic bell curve, but for a small sample size (77 starters), it's fairly close.
And this is strange, because the bell curve, as I understand it, is supposed to apply to the general population. You get a fat center where most people are; then you get the freaks of nature where almost everyone at Worlds is. They are the very edge of the bell curve. Yet, they also form a bell curve.
The TTT curve looks more like what we'd expect--it levels off as speeds increase.
Why might this be?
My guess is that the deviation of TTT times is a product of the TTT rules, since the finishing time is set by the fourth-fastest rider on the team. Doing so means OPQS's time is not set so much by Martin as it is by the ability of his teammates to keep up.
In sum, the fourth fastest riders on pro teams (think, Nikki Terpstra, Daryl Impey) tend to be more close in ability to each other than the world's best TTers are to each other.
In short, having teams lessens the outlier effect.
Another interesting thing to note--how the TTT takes different kinds of skills that the ITT. Orica lost to OPQS by .8 seconds. OPQS has arguable three of the top twenty time trial riders in the world in Martin, Chavanel, and Terpstra. Velits and Kwiatkowski are certainly also capable of winning time trials.
Orica riders finished in the top 30 in this year's ITT, and only Svein Tuft and Luke Durbridge can be considered among the top time trialists.
OPQS relied on its powerhouses; Orica countered with balance.
Orica's Svein Tuft notes the different physiological demands of the two disciplines:
I've only been training specifically for the TTT. It's such a different effort - it's really hard to go from these two minute or 1.5-minute full gas efforts and recovery to a long steady hour, or hour-ten minute steady state effort. It's a totally different type of training.Tuft may be right about the different demands of TTTs and ITTs, but this year, Tony Martin came out tops in both events, suggesting at least some crossover.
I've previously spent some time questioning the methods of cycling experts like Antoine Vayer who estimate W/kg using VAM. I like the purpose and the goal of it; the tool is still too rough to say much conclusively.
Why do these experts limit themselves to climbs? Why not use similar tools to estimate ITT efforts?
Furthermore, why not look at context and standard deviation rather than just a certain W/kg threshold (e.g., anything about 6.8w/kg is considered dope-ish territory)?
The marathon, by analogy, is a roughly two-hour event, and the margin by which records--course, national, and world--are set in it are increasingly small. If these records were being broken by large margins suddenly, we might want an explanation.
How do explain, for instance, the many large improvements in the women's marathon record times between 2000 and 2004? Two factors: (1) the increased participation of African women and (2) Paula Radcliffe, who lopped off three minutes of the record on her own.
Sometimes the best explanation for performance is simply the performer: a human being at the edge of being human.
Or maybe they're just doping.
In either case, I think one aspect of being a cycling fan is to have perspective on performances--to be able recognize those rides that are great deviation from even the norm, not only of us recreation riders, but of other professional cyclists and their abilities.
Tony Martin's ride yesterday falls into this category, but I'd also include the other six riders who finished at the top. They are clearly made (or are training and recovering) differently than the other professionals out on the course.
*Weather reports on both days indicated 6mph NE winds during the TTT and 4mph SW speeds during the ITT. This may matter in comparing the ITT and TTT times.