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Hugh Trenchard, Andrew Renfree and Derek M. Peters

Purpose:

Drafting in cycling influences collective behavior of pelotons. Although evidence for collective behavior in competitive running events exists, it is not clear if this results from energetic savings conferred by drafting. This study modeled the effects of drafting on behavior in elite 10,000-m runners.

Methods:

Using performance data from a men’s elite 10,000-m track running event, computer simulations were constructed using Netlogo 5.1 to test the effects of 3 different drafting quantities on collective behavior: no drafting, drafting to 3 m behind with up to ~8% energy savings (a realistic running draft), and drafting up to 3 m behind with up to 38% energy savings (a realistic cycling draft). Three measures of collective behavior were analyzed in each condition: mean speed, mean group stretch (distance between first- and last-placed runner), and runner-convergence ratio (RCR), which represents the degree of drafting benefit obtained by the follower in a pair of coupled runners.

Results:

Mean speeds were 6.32 ± 0.28, 5.57 ± 0.18, and 5.51 ± 0.13 m/s in the cycling-draft, runner-draft, and no-draft conditions, respectively (all P < .001). RCR was lower in the cycling-draft condition but did not differ between the other 2. Mean stretch did not differ between conditions.

Conclusions:

Collective behaviors observed in running events cannot be fully explained through energetic savings conferred by realistic drafting benefits. They may therefore result from other, possibly psychological, processes. The benefits or otherwise of engaging in such behavior are as yet unclear.

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Philippe Hellard, Robin Pla, Ferran A. Rodríguez, David Simbana and David B. Pyne

∼43% to 57%. 4 Another study combining field measurements with modeling of muscle energy metabolism using computer simulation estimated the energy distribution as ∼41% to 59%. 5 These discrepancies could relate to different testing methods (eg, direct exercise measurements vs backward extrapolation

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Owen Jeffries, Mark Waldron, Stephen D. Patterson and Brook Galna

– 442 . PubMed ID: 18523040 doi:10.1136/bjsm.2008.047787 18523040 10.1136/bjsm.2008.047787 15. Terblanche E , Wessels JA , Stewart RI , Koeslag JH . A computer simulation of free-range exercise in the laboratory . J Appl Physiol . 1999 ; 87 : 1386 – 1391 . PubMed ID: 10517768 doi:10

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Lachlan J.G. Mitchell, Ben Rattray, Paul Wu, Philo U. Saunders and David B. Pyne

. Rodriguez F , Mader A . Energy metabolism during 400 and 100-m crawl swimming: computer simulation based on free swimming measurement . In: Chatard J.C. ed. Biomechanics and Medicine in Swimming IX . Saint-Étienne, France : Publications de l’Université de Saint-Étienne ; 2003 : 373 – 378 . 11

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Thomas Haugen, Jørgen Danielsen, Leif Olav Alnes, David McGhie, Øyvind Sandbakk and Gertjan Ettema

acceleration performance through computer simulation . Sports Biomech . 2015 ; 14 : 232 – 245 . PubMed doi:10.1080/14763141.2015.1052748 10.1080/14763141.2015.1052748 26102345 20. Mann R , Murphy A . The Mechanics of Sprinting and Hurdling . CreateSpace Independent Publishing Platform ; 2015 . 21

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Edward J. Bradley, Bob Hogg and David T. Archer

Exerc . 1997 ; 29 : 246 – 255 . PubMed ID: 9247922 22. Silvestros P , Cazzola D . Cervical spine injuries in rugby union scrummaging: is buckling the most likely injury mechanism? Paper presented at: XVI International Symposium on Computer Simulation in Biomechanics ; July 20–22 , 2017

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Conall F. Murtagh, Christopher Nulty, Jos Vanrenterghem, Andrew O’Boyle, Ryland Morgans, Barry Drust and Robert M. Erskine

, Komura T , Fukashiro S . Optimal coordination of maximal-effort horizontal and vertical jump motions–a computer simulation study . Biomed Eng Online . 2007 ; 6 ( 1 ): 20 – 29 . doi:10.1186/1475-925X-6-20 10.1186/1475-925X-6-20 14. Fukashiro S , Besier TF , Barrett R , Cochrane J

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Jonathon J.S. Weakley, Dale B. Read, Hugh H.K. Fullagar, Carlos Ramirez-Lopez, Ben Jones, Cloe Cummins and John A. Sampson

effect of augmented feedback type and frequency on velocity-based training-induced adaptation and retention [published online ahead of print February 14, 2018]. J Strength Cond Res . PubMed ID: 29461412 doi:10.1519/JSC.0000000000002514 29461412 38. Hubbard M . Computer simulation in sport and

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Kurtis Pankow, Amber D. Mosewich and Nicholas L. Holt

-Avers , K.E. , & Mumford , M.D. ( 2009 ). Impact of situational framing and complexity on charismatic, ideological and pragmatic leaders: Investigation using a computer simulation . Leadership Quarterly, 20, 383 – 404 . doi:10.1016/j.leaqua.2009.03.007 10.1016/j.leaqua.2009.03.007 Hunter , S

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Kurtis Pankow, Amber D. Mosewich and Nicholas L. Holt

Research, 15 , 1277 – 1288 . PubMed ID: 16204405 doi:10.1177/1049732305276687 10.1177/1049732305276687 Hunter , S.T. , Bedell-Avers , K.E. , & Mumford , M.D. ( 2009 ). Impact of situational framing and complexity on charismatic, ideological and pragmatic leaders: Investigation using a computer