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Peloton

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For other uses, see Peloton (disambiguation).
The peloton during the 2005 Tour de France. Clustering of the teams is apparent.

In a road bicycle race, the peloton (from French, originally meaning 'platoon') is the main group or pack of riders. Riders in a group save energy by riding close (drafting or slipstreaming) to (particularly behind) other riders. The reduction in drag is dramatic; riding in the middle of a well-developed group, drag can be reduced to as little as 5%-10%.[1] Exploitation of this potential energy saving leads to very complex cooperative and competitive interactions between riders and teams in race tactics. The term is also used to refer to the community of professional cyclists in general, as in 'the professional peloton.'

Definition

Team Sky riding in a straight line to increase slipstreaming, thus reducing drag and conserving energy for cyclists behind, often for key riders such as sprinters or GC cyclists.

Formally, a peloton has been defined as "two or more cyclists riding in sufficiently close proximity to be located either in one of two basic positions: (1) behind cyclists in zones of reduced air pressure, referred to as ‘drafting’, or (2) in non-drafting positions where air pressure is highest. Cyclists in drafting zones expend less energy than in front positions." [2] A peloton has similarly been defined "as a group of cyclists that are coupled together through the mutual energy benefits of drafting, whereby cyclists follow others in zones of reduced air resistance. Although the interactions among individual cyclists are in principle very simple – each cyclist takes a turn leading and then returns to the pack – the collective behavior of the peloton is very complex."[3]

Peloton Behavior

The peloton travels as an integrated unit (similar in some respects to birds flying in formation) with each rider making slight adjustments in response to their adjacent riders (particularly the rider in front of them). Riders at the front are fully exposed to wind resistance, hence experience much higher fatigue loads. After a period of time at the front, they will manoeuvre farther back in the peloton to recover. With sufficient room to manoeuvre, the peloton appears in time lapse as a fluid cloud, with an endless stream of riders pushing from the back through to the leading edge, then falling away. In addition to bird flock formations, peloton behavior involving drafting or similar energy-saving mechanisms has been identified in a variety of biological systems.[4][5][6][7]

The shape or formation of the peloton changes according to many factors. A strong headwind or a hard effort tends to spread out or string out the riders into a long narrow formation, sometimes single file. A slow pace or brisk tailwind greatly relieves the fatigue penalty for riding in a formation that fills the road from one side to the other, and in these situations riders ride side by side. When two or more groups of riders have reason to contest control of the peloton, several lines may form, each seeking to impose debilitating fatigue on the other teams. Fatigue is a decisive factor in the outcome of every race. Cyclists' range of peripheral vision is a significant factor in peloton formation.[8]

Peloton formations have been described as exhibiting two main phases of behavior: a compact, low-speed formation, and a single-file, high-speed formation. [9] Peloton phases are indicated by thresholds in collective output that can be modelled mathematically and computationally.[10] [11] The principles of phase behavior identified by Trenchard et al. have been applied to optimize engineering problems.[12]

Similarly, these thresholds in peloton formations define transitions between peloton cooperative behavior and free-riding behavior. [13] Cooperation and free-riding in pelotons have been studied using game theory and as a social dilemma,[14][15][16] and have also been considered in terms of equivalencies to aspects of economic theory.[17]

Basic peloton behaviors have also been modelled with robots, [18][19] and principles of peloton behavior have also been considered in relation to the future of collective robot behavior.[20]

Riders' strategy in pelotons

While the riders at the very front encounter the greatest air resistance (and also those on the windward side when there is a significant crosswind), those behind the first few riders near the front enjoy critical advantages.

Being close to the front means that the rider can see and react to attacks from competitors, and changes in position, with far less effort. Gaps sometimes form in the peloton, and being close to the front reduces the risk of getting caught in the rear group if a break occurs in the peloton, for example, after a crash. Riders near the front are much less likely to have delays due to involvement in crashes.

There is increasing risk of delays or injury from involvement in crashes as one falls farther back in the peloton. In addition, riders are increasingly affected by the accordion effect (aka elastic band/concertina/slinky effect) in which a change in speed becomes amplified as it propagates to the back of the peloton. The riders following must anticipate and brake early to avoid collisions when the peloton slows. Touching wheels for even a moment normally results in a crash, which spreads across the field in chain reaction as the densely packed riders cannot avoid hitting downed riders and bikes. The entire peloton behind the crash may be stopped.

Being close to the front is also critical in strong crosswind conditions. Cross winds create a significant fatigue penalty for everyone, unless riders form moving groups called echelons in which riders collaborate to form a 'paceline' in a racetrack pattern angled across the road, with the leading rider on the upwind side of the road. Riders for a paceline, such as an echelon, sequentially change positions at short intervals so that no one rider must long accumulate excessive fatigue from facing maximum wind resistance at the leading edge. Echelons are necessarily limited in size by the roadway's width.

When a large peloton is exposed to a significant crosswind on a narrow road, the peloton cannot avoid breaking into a number of small echelons. Teams aware of wind conditions ahead, strong enough to move to the front, well experienced in echelon riding, can gain an important time advantage in these circumstances.

It is critical for riders in contention to win a race to remain near (but not at) the front of the peloton, especially when approaching sharp turns that require braking. Resuming pace after a sharp turn (especially into wind) routinely causes division in a peloton. Once a division occurs, if the will and collective strength of those wisely placed at the front is greater than those behind, the gap between the groups will remain (or increase) to the end of the race, because the extra air resistance for a single rider attempting to move forward to reach the front group imposes an extravagant fatigue penalty, as compared to those who remained aerodynamically protected in the peloton. This is particularly true at high speed on flat roads.

When a team maneuvers to the front of the peloton, it has placed itself in position to dictate the tempo of the race. Teams of riders may prefer a faster or slower tempo depending on the team's tactics.

Being near or at the front of the peloton is critical when initiating a breakaway.

A few strong riders will always attempt to break away from the main peloton, attempting to build such a commanding lead early in the race that the peloton cannot catch up before the finish. Breakaways may succeed when break riders are strong, especially if none of the riders in the break is a danger man (in contention for a win in the overall contest), and if they all pull together as a team. The rider (or riders) who are in the lead and have also successfully broken away from the peloton are referred to as Tête de la Course (a French expression meaning “head of the race”). The peloton will not allow a break with a danger man to get far ahead. Strong teams who want to bring their sprinter into contention for the win come to the front of the peloton and dictate a harsh pace, imposing fatigue on rivals, meanwhile breakaway riders (who individually must spend much more time exposed to the wind than peloton members) sequentially succumb to fatigue and are normally caught. Otherwise successful breaks often fall into disarray just before the finish, where rider calculations regarding personal chances for victory destroy the uneasy break alliance, meanwhile the peloton is catching up quickly.

Tactical factors also apply.[21][22][23][24][25] [26]Team tactics generally involve clustering their members within the peloton in order to maximize their ability to affect the peloton. For example, if a team member is currently in a breakaway group out in front of the main peloton, the remaining team members will normally make no attempt to accelerate the peloton, to maximize the chances of success for their breakaway group rider. Rarely, they may move to the front of the peloton, and actively seek to check the progress of the peloton at a critical moment. This tactic has the best chance of success on narrow roads, with tight turns, where a single team can fill the road from one side to the other.

In races where the finish is on flat roads, within a few kilometers from the finish, strong teams form into lines, with their principal sprint contender at the rear. The leading rider of each contending team drives forward at the highest pace he can achieve, until he reaches the limit of his endurance, when he then pulls off to the side, allowing the succeeding team member in line to drive forward to his limit. The team sprinter slipstreams at the rear to minimize fatigue due to air resistance until the last hundred meters or so, when the sprinter will choose the moment to dash out from behind his leadout rider to charge to the finish at the highest possible speed.

See also

References

  1. ^ Blocken, Bert (2018-06-30). "Aerodynamic drag in cycling pelotons: New insights by CFD simulation and wind tunnel testing". Journal of Wind Engineering and Industrial Aerodynamics. 179: 1. doi:10.1016/j.jweia.2018.06.011.
  2. ^ Trenchard, Hugh, 2013. "Peloton phase oscillations," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 194-201.
  3. ^ Trenchard, Hugh & Richardson, Ashlin & Ratamero, Erick & Perc, Matjaž, 2014. "Collective behavior and the identification of phases in bicycle pelotons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 92-103.
  4. ^ Trenchard, Hugh and Matjalž Perc. “Energy saving mechanisms, collective behavior and the variation range hypothesis in biological systems: A review.” Bio Systems 147 (2016): 40-66 .
  5. ^ Trenchard, Hugh, Carlton E. Brett and Matjalž Perc. “Trilobite ‘pelotons’: possible hydrodynamic drag effects between leading and following trilobites in trilobite queues.” Palaeontology 60 (2017): 557-569.
  6. ^ Trenchard, Hugh. “Cell pelotons: A model of early evolutionary cell sorting, with application to slime mold Dictyostelium discoideum.” Journal of theoretical biology 469 (2019): 75-95 .
  7. ^ Trenchard, Hugh. “American coot collective on-water dynamics.” Nonlinear dynamics, psychology, and life sciences 17 2 (2012): 183-203.
  8. ^ Belden, J., Mansoor, M.M., Hellum, A., Rahman, S.R., Meyer, A., Pease, C., Pacheco, J., Koziol, S. and Truscott, T.T., 2019. How vision governs the collective behaviour of dense cycling pelotons. Journal of the Royal Society Interface, 16(156), p.20190197.
  9. ^ ibid 3
  10. ^ ibid 3
  11. ^ Trenchard, Hugh & Ratamero, Erick & Richardson, Ashlin & Perc, Matjaž, 2015. "A deceleration model for bicycle peloton dynamics and group sorting," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 24-3
  12. ^ Poitras, G., Cormier, G. and Nobelle, AS. (2018) Novel Optimization Algorithm for Composite Steel Deck Floor Systems: Peloton Dynamics Optimization (PDO). Building Tomorrow's Society, Canadian Society for Civil Engineering annual conference, June 13-16, 2018
  13. ^ Trenchard, Hugh, 2015. "The peloton superorganism and protocooperative behavior," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 179-192
  14. ^ Brouwer, T., & Potters, J. (2019). Friends for (almost) a day: Studying breakaways in cycling races. Journal of Economic Psychology, 75(part B), [102092]. https://doi.org/10.1016/j.joep.2018.08.001
  15. ^ Hoenigman, Rhonda, Elizabeth Bradley, and Allen Lim. "Cooperation in bike racing—when to work together and when to go it alone." Complexity 17, no. 2 (2011): 39-44.
  16. ^ Mignot, J.F., 2016. Strategic behavior in road cycling competitions. In The economics of professional road cycling (pp. 207-231). Springer, Cham.
  17. ^ Trenchard, Hugh, and Matjaz Perc. "Equivalences in biological and economical systems: Peloton dynamics and the rebound effect." PloS one 11, no. 5 (2016): e0155395.
  18. ^ Bedruz, Rhen Anjerome, Argel A. Bandala, Ryan Rhay Vicerra, Ronnie Concepcion, and Elmer Dadios. "Design of a Robot Controller for Peloton Formation Using Fuzzy Logic." In 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA), pp. 83-88. IEEE, 2019.
  19. ^ Bedruz, Rhen Anjerome R., Jose Martin Z. Maningo, Arvin H. Fernando, Argel A. Bandala, Ryan Rhay P. Vicerra, and Elmer P. Dadios. "Dynamic Peloton Formation Configuration Algorithm of Swarm Robots for Aerodynamic Effects Optimization." In 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA), pp. 264-267. IEEE, 2019.
  20. ^ Trenchard, H., 2018. When Robots Get Bored and Invent Team Sports: A More Suitable Test than the Turing Test?. Information, 9(5), p.118.
  21. ^ Macur, Juliet (2009-07-06). "Sixth Sense Has Armstrong in Third Place". New York Times. Archived from the original on 2011-07-20.
  22. ^ Ratamero, E. Martins. "MOPED: an agent-based model for peloton dynamics in competitive cycling." International Congress on Sports Science Research and Technology Support, Vilamoura, icSPORTS. 2013.
  23. ^ Olds, Tim. "The mathematics of breaking away and chasing in cycling." European Journal of Applied Physiology and Occupational Physiology 77.6 (1998): 492–497.
  24. ^ Ratamero, Erick Martins. "Modelling Peloton Dynamics in Competitive Cycling: A Quantitative Approach." International Congress on Sports Science Research and Technology Support. Springer International Publishing, 2013.
  25. ^ Scelles, N., Mignot, J.-F., Cabaud, B. and François, A. (2018), "Temporary organizational forms and coopetition in cycling: What makes a breakaway successful in the Tour de France?", Team Performance Management, Vol. 24 No. 3/4, pp. 122-134. https://doi.org/10.1108/TPM-03-2017-0012
  26. ^ Wolf, S. and Saupe, D., 2017. How to stay ahead of the pack: optimal road cycling strategies for two cooperating Riders. International Journal of Computer Science in Sport, 16(2), pp.88-100.
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