Team pursuit: how positional drafting changes the CdA of the 2nd, 3rd and 4th rider
For twenty years coaches assumed team pursuit drafting reduced CdA uniformly: every rider behind the leader saved the same percentage. Blocken's (2018) CFD simulations and Íñiguez's (2009) wind tunnel measurements proved that assumption was not only imprecise, it was dangerous. Ordering the quartet wrong costs seconds that no amount of fitness recovers.
The real measurement, by position
Blocken ran CFD simulations on a pack of four cyclists in tight formation (wheel-to-wheel gap of 15 cm) at 65 km/h. Íñiguez measured the same in a wind tunnel with real quartets. Both agreed on the magnitude, with nuances at the 2nd rider:
| Position | CdA reduction (Blocken) | CdA reduction (Íñiguez) | Power saved at 60 km/h |
|---|---|---|---|
| 1 (leader) | 0% | 0% | — (reference) |
| 2 | −41% | −38% | ≈ 180 W |
| 3 | −45% | −43% | ≈ 200 W |
| 4 | −46% | −45% | ≈ 205 W |
Translation: the 4th rider saves slightly more than the 3rd. The 3rd, slightly more than the 2nd. And the 2nd, much less than the two at the back. The gap between position 2 and 3 is 4-5%: almost 20 W at 60 km/h.
Why the 2nd rider suffers more than it looks
Against intuition, the 2nd rider is not sitting in "the best draft in the world". They are in a close turbulent wake zone where airflow has not fully reorganised. Riders 3 and 4 benefit from the cumulative effect: the air reaches them already "cleaned" by the work of the leader and 2nd. This hierarchy changes the logic of relay order.
Tight formation: why 15 cm is not 30 cm
Blocken also quantified the effect of wheel-to-wheel gap. Doubling the distance from 15 to 30 cm does not double the loss, but the magnitude is substantial:
- 15 cm gap: 2nd −41%, 3rd −45%, 4th −46%
- 30 cm gap: 2nd −34%, 3rd −38%, 4th −39%
- 45 cm gap: 2nd −27%, 3rd −31%, 4th −32%
Losing 15 cm of compactness costs 30-40 W per rider. In a team riding at 480 W average, that is the difference between 3:47 and 3:52 for 4 km. Training tight formation is not an aesthetic issue: it is aerodynamic training.
Ordering the quartet by physiological profile
The classic rule "strongest at the front" is incomplete. With real drafting asymmetries, the winning order depends on the distribution of strength across the quartet.
Case 1: homogeneous quartet
All four have similar CP. Rotating every half lap (125 m) keeps W' spending balanced and exploits drafting best. Nobody gets dropped because nobody empties before the others.
Case 2: one engine and three finishers
One rider has 20 W more CP than the other three. Having them pull 40% of the time (2 long relays in the middle phase) protects the rest and uses their advantage where it matters most, not in the anaerobic start.
Case 3: a start specialist
One rider has high W' but low CP. They lead the first 30-45 seconds, then drop to 4th where drafting lets them recover and never pull again. This "sacrifice" tactic is common in young teams with a reusable sprinter.
The quartet's effective CdA: the metric that matters
Team speed does not depend on each rider's CdA in isolation but on the average CdA weighted by time at the front. A quartet with a poorly rotating leader is slower than one with less raw power but better formation. Example:
| Quartet | Avg. leader CdA | Effective team CdA | Speed at 480 W avg. |
|---|---|---|---|
| Tight formation, good rotation | 0.190 | 0.118 | 63.1 km/h |
| Open 30 cm formation | 0.190 | 0.132 | 61.4 km/h |
| Tight formation, poor leader posture | 0.210 | 0.129 | 61.7 km/h |
A tight formation with an average leader beats an open formation with an aerodynamic leader. Compacting is cheaper than recruiting.
Design the optimal formation with your athletes' real CdA
AthletePro implements Blocken 2018, Íñiguez 2009 and the aggressive CFD model. Order your quartet by profile, change the gap, and see the effective CdA in real time.
Start free trialReferences: Blocken B. et al. (2018), J. Wind Eng. Ind. Aerodyn.. Íñiguez-de-la-Torre I. et al. (2009), wind tunnel drafting. Boillet A. et al. (2024), Sci. Rep.. Heimans L. et al. (2017), team pursuit optimisation.