The Di PRAMPRERO model used by Vayer and Portoleau requires a lot of information and some of them are not easily collectible. If the results of this calculation were not as dangerous for the image of a sport still on the hot seat, approximations would be accepted. We are often in the accusation without solid evidence!!
Among these variables:
• the frontal surface: difficult to assess and we can easily imagine that Egan BERNAL compared to Van Avermatt, positioned differently on different bikes does not push the same amount of air.
• weather conditions: we must be able regularly measured weather data on all climbs on which we would like to evaluate a power. The use of weather stations at different intervals is a solution but requires significant resources to obtain the temperature, atmospheric pressure, wind strength and direction ... which is not the case today because the calculations are performed behind a TV screen. And we know that in the mountains, elements like the wind are precious friends and then terrible opponents at each U-turns.
• finally, the weight of the cyclist, the road performance or the quality of the bearings must be defined because bikes have got a bottom bracket, wheel bearings and derailleurs of variable quality. Thanks to product comparisons, we know that even ceramic bearings are not always as good as simple steel bearings !!
We can add the influence of the drafting, the presence of Republican Guard motorcycles when the crowd is dense in famous climbs like Alpe d'Huez or Ventoux on the cyclist’s energy uptake. Contador or Nibali in a climb with a headwind will be very happy to be hidden behind a competitor or a teammate to save efforts.
And we never take into account the presence of spectators on the sides of the road !!
The weight of the cyclist is also very variable between the start of a stage and the finish: some athletes can lose more than 1.5 liters of water during exercise in a hot atmosphere (despite the 5 to 8 liters of drink during the stage) accompanied by 2kg of stored sugar and fat. We can easily imagine that the weight to ride on top of Hautacam or Izoard will have an impact.
Grégoire MILLET, specialist in altitude training, add a note on his Facebook account in 2017: "What is the individual sensitivity of Froome (or another) to altitude?” Indeed, we know that some athletes can quickly adapt to the mountain air (we use the term good responder) but a doped athlete can escape the" radar "of the VAYER-PORTOLEAU if his ability to pick up oxygen is reduced by the altitude: the doping use gives him "normal" performances whereas he should have been in difficulty.
It’s a calculation performed in front of a TV, without direct access to the variables of the equation: like a desire to mix inaccuracies and mathematics ... it is incompatible!
What degree of error? Faced with the difficulty of collecting all the data to integrate into an equation, it seems inconceivable to judge the specific nature of a cyclist's performance. In "The proof by 21" of VAYER, he reports that, when all the data are known and validated, the margin of error is lower than 2%. But when the values are roughly defined ... We quickly pass from acceptable performance to "mutant" performance.
Can the powermeter help with doping control? The powermeter can, in my opinion, guide and only guide the search for cheaters thanks to the Power Profile.
Using a Power Profile for longitudinal monitoring as biological values, we will inevitably observe breaks in the performance curve:
- downward: overtraining, illness, deficiency ...
- on the rise: drug preparation, doping practice, etc.
Anti-doping agencies can finish the investigation with biological evidences!
The fight against doping is complex: the secret work of biological analysis (taking blood and urine) is often crushed by the media uproar of an ambitious but unreliable mathematical technique. The powermeter that the majority of the riders now uses could be a great help in finding the cycling bad boys.
Frederic HURLIN - cycling coach - www.azurperformance.fr
 Between 3% to 7% - Robergs and al. 1998.