Wednesday, March 7, 2012

How fast is a team time trial?

HOW FAST IS A TEAM TIME TRIAL? On Wednesday's team time trial prologue of Tirreno-Adriatico, Garmin-Barracude entered the final kilometer with 17.57 on their clock. They crossed the line at 18:58 - a kilometer in 1:01. That puts their final kilometer at almost 60kph, just over 37mph, at the end of a nearly twenty-minute all-out effort that shed four of their riders. Their 18:58 time gave them an average speed of 53.4kph (33.2mph) over the 16.9km route.

HOW DOES THIS COMPARE TO OTHER DISCIPLINES? Well, the UCI's 1-kilometer track time trial record is 58.875 seconds, or an average speed of 61.2 kph / 38 mph for just under a minute - starting from a stop! And, the UCI's World Hour Record is 49.7 kilometers in one hour - averaging 49.7 kph, naturally, or nearly 31mph for an hour - three times longer than Garmin-Barracuda's Tirreno-Adriatico time trial, and with one-ninth the number of riders.

WHAT KIND OF SPEED DIFFERENCES RESULT IN TIME DIFFERENCES?

Greenedge, the winner, rode at 54.2kph - quite a bit faster than Columbia-Coldeportes' (the last team on the day) average speed of 49.6. But take a look at Saxo Bank, who finished 6th, just off the (extended) podium. They finished 8 seconds behind Astana, due to an average speed differential of just .35kph. And Lampre-ISD missed out on a top-ten ride by a speed differential with Lotto-Belisol of just .04kph.

Time trials pose great examples of the accumulation of marginal gains. A more aerodynamic position, a faster wheel, an unfavorable start position and changing weather - an extra half-kilometer-per-hour of speed all add up when the officials stop your clock. By charting these average speeds we can see where the slimmest margins are. Those who finished 6th through 17th all had speeds within less than 1kph of each other.

That looks like a pretty big drop in performance until we scale the y-axis all the way down to zero, more accurately showing the difference as a proportion of the overall (removing this "perspective" is one of the fundamental ways to mislead using statistics).

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