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Hugh Trenchard, Andrew Renfree and Derek M. Peters


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.


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.


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.


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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Michael Koh, Leslie Jennings, Bruce Elliott and David Lloyd

The Yurchenko layout vault is the base vault from which more advanced forms of the Yurchenko family of vaults have evolved. The purpose of the study was to predict an individual’s optimal Yurchenko layout vault by modifying selected critical mechanical variables. The gymnast’s current performance characteristics were determined using the Peak-Motus video analysis system. Body segment parameters were determined using the elliptical zone mathematical modeling technique of Jensen (1978). A 5-segment computer simulation model was personalized for the gymnast comprising the hands, upper limbs, upper trunk, lower trunk, and lower limbs. Symmetry was assumed, as the motion was planar in nature. An objective function was identified which translated the subjective points-evaluation scheme of the Federation of International Gymnastics (FIG) Code of Points to an analytic expression that was mathematically tractable. The objective function was composed of performance variables that, if maximized, would result in minimal points being deducted and bonus points being allocated. A combined optimal control and optimal parameter selection approach was applied to the model to determine an optimum technique. The predicted optimal vault displayed greater postflight amplitude and angular momentum when compared with the gymnast’s best trial performance. Increased angular velocity, and consequently greater angular momentum at impact and greater shoulder flexion angle at impact with the horse, were related with this optimum technique. The impact phase therefore serves to increase the angular momentum during horse contact. Since the optimized parameters at impact with the horse were within the accepted physical capacity limits observed for the individual, the predicted vault is viable.

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Eric J. Sprigings and Doris I. Miller

Optimized computer simulation, using a mathematical model of a diver, was employed to gain insight into the primary mechanical factors responsible for producing height and rotation in dives from the reverse group. The performance variable optimized was the total angular displacement of the diver as measured from last contact to the point where the diver's mass center passed the level of the springboard or platform. The times of onset, and lengths of activation for the joint torque actuators, were used as the control variables for the optimization process. The results of the platform simulation indicated that the magnitude of the hip torque was approximately twice that generated by the knee joint during the early extension phase of the takeoff. Most of the knee extension for the simulation model coincided with the period of reduced hip torque during the later phase of takeoff, suggesting that the knee torque served mainly to stabilize the lower limbs so that the force from the powerful hip extension could be delivered through to the platform. Maintaining a forward tilt of the lower legs (~50° from the horizontal) during hip and knee extension appeared to be paramount for successful reverse somersaults. Although the movement pattern exhibited by the springboard model was limited by the torque activation strategy employed, the results provided insight into the timing of knee extension. Peak knee extension torque was generated just prior to maximum springboard depression, allowing the diver's muscular efforts to be exerted against a stiffer board. It was also apparent that the diver must maintain an anatomically strong knee position (~140°) at maximum depression to resist the large upward force being exerted by the springboard against the diver's feet. The optimization process suggested that, as the number of reverse somersaults increases, both the angle of the lower legs with respect to the springboard and the angle of knee extension at completion of takeoff should decrease.

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David Lilley

“inspirational” stories of disabilities overcome. They provide a vocational account of sport by recounting the efforts of disabled athletes and their partners in exertion (pp. 82–88). Hargaden initially has readers asking, “Wait, where is this going?” with a detailed description of a computer simulation called

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Nobuaki Tottori, Tadashi Suga, Yuto Miyake, Ryo Tsuchikane, Mitsuo Otsuka, Akinori Nagano, Satoshi Fujita and Tadao Isaka

) is correlated with a higher performance in sprinters. Similar to QF, ADD, especially ADD magnus, contributes to producing knee extensor moment during human locomotion, as examined using a computer simulation ( 5 ). Taken together, although other muscles may also play an important role in sprint

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Nicholas J. Smeeton, Matyas Varga, Joe Causer and A. Mark Williams

disguise have on the anticipation of throw direction. As an alternative to the conventional manipulations used in previous studies, with the aid of computer simulation or willful actions being performed, for example, the design of three different garments were altered to disguise advance cues or deceive

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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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Nicole C. George, Charles Kahelin, Timothy A. Burkhart and David M. Andrews

. 2010 ; 43 : 364 – 369 . PubMed doi:10.1016/j.jbiomech.2009.06.058 19840881 10.1016/j.jbiomech.2009.06.058 8. Kentel BB , King MA , Mitchell SR . Evaluation of a subject-specific, torque-driven computer simulation model of one-handed tennis backhand ground

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Akihiro Tamura, Kiyokazu Akasaka and Takahiro Otsudo

common with forceful valgus motion and external or internal rotation with the knee close to full extension. 6 In a computer simulation, a small knee-flexion angle and a large knee valgus moment were shown to be risk factors for noncontact ACL injuries. 7 Comparing genders, it is known that knee valgus

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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