|The US Women's TP in full flight. Pic from Guy Swarbrick|
The TP is a pretty rad mix of anaerobic/aerobic demands, technique, and pacing strategy. I might crack open the MAOD simulator whirlygig soon to look at the physiological side soon, but since I haven't quite hacked the US National Team's power files yet, I figured I'd look at something simple regarding pacing, especially since the women's race is a good example of how different race situations call for different strategies.
One of the cool elements of TP (and team time trial, which I got to race some of in college) is that the team has to decide how to manage racing with riders of different strengths / aero profiles. Do stronger riders pull longer at the same speed, faster for the same duration, or somewhere in between? How would having a 6'3" rider drafting behind a 5'7" rider affect length/intensity of pulls? The conventional wisdom is that constant speed is better than equal-length pulls, since the added stress of accelerating/decelerating as different riders hit the front usually outweighs any benefit.
So is that how it works at the highest level? I got the lap splits from the men's and women's qualifying, first round, and finals races and plotted the standard deviation of lap times vs the team's final race time. On the graphs, closer to zero on the x-axis means more consistent pacing, and closer to zero on the y-axis means a faster time. First, the men:
There aren't a ton of data points, but generally, faster teams are also more consistent in their pacing. On average, for every additional second of lap inconsistency, you might expect a team to go 10 seconds slower (insert large grain of salt). The limit of just looking at split times is that obviously, all the teams don't start with the same horsepower, resources, training time together, etc. Given how specialized an event like the TP is, countries that put more resources into training a dialed TP team likely see benefits in both increased strength and technique. (Profound, I know.)
The positive correlation between lap time consistency and final time was stronger in the women's races--in the neighborhood of 18-21 seconds faster overall per second of lap consistency.
One way to look at the data points is to view points below the regression line as having more horsepower and less consistent pacing, while points above the line have better pacing but less horsepower. The most interesting example is in the women's final, where Canada beat the US 5:20.1 to 5:25.8. Despite being in the gold medal ride, the US women had the most lap variation of all eight finals runs. Here's what the lap splits between the US and Canada looked like:
The increase in the blue line in the last few laps is the US team losing some steam. One reason they may have slowed down is that they had to burn a big match in the first round (where they rode a 4:21.5) just to make it to the gold medal ride. Another may have to do with the structure of the competition affecting their strategy. If a team is trying to do the fastest run they can, a consistent pacing strategy is ideal. But the strategy changes once you're in the gold medal ride where the worst you can get is 2nd place, and you're facing an opponent that has been consistently stronger than you. In that case, the best option may be to try and match them, even if it means risking blowing up in the final kilometer. It looks like that's what happened to the US women's team. But nothing ventured, nothing gained! And they still wound up with a silver medal.
A cool problem would be looking at TP power files and coming up with a way to model the actual cost of inconsistency. But that will have to wait until I want to do something other than some rookie ax+b nonsense.