A Computer Model of Drafting Effects on Collective Behavior in Elite 10,000 m Runners

Purpose Drafting in cycling influences collective behaviour of pelotons. Whilst evidence for collective behaviour in competitive running events exists, it is not clear if this results from energetic savings conferred by drafting. This study modelled 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 three different drafting quantities on collective behaviour: no drafting, drafting to 3m behind with up to ~8% energy savings (a realistic running draft); and drafting up to 3m behind with up to 38% energy savings (a realistic cycling draft). Three measures of collective behaviour were analysed 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±0.28m.s-1, 5.57±0.18 m.s-1, and 5.51±0.13 m.s-1 in the cycling draft, runner draft, and no draft conditions respectively (all P<0.001). RCR was lower in the cycling draft condition, but did not differ between the other two. Mean stretch did not differ between conditions. Conclusions Collective behaviours 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.

Risk Perception Influences Athletic Pacing Strategy.

PURPOSE: To examine risk-taking and risk-perception associations with perceived exertion, pacing and performance in athletes. METHODS: Two experiments were conducted in which risk-perception was assessed using the domain-specific risk-taking (DOSPERT) scale in 20 novice cyclists (Experiment 1) and 32 experienced ultra-marathon runners (Experiment 2). In Experiment 1, participants predicted their pace and then performed a 5 km maximum effort cycling time-trial on a calibrated KingCycle mounted bicycle. Split-times and perceived exertion were recorded every kilometer. In experiment 2, each participant predicted their split times before running a 100 km ultra-marathon. Split-times and perceived exertion were recorded at 7 check-points. In both experiments, higher and lower risk-perception groups were created using median split of DOSPERT scores. RESULTS: In experiment 1, pace during the first km was faster among lower compared to higher risk-perceivers, t(18)=2.0 P=0.03, and faster among higher compared lower risk-takers, t(18)=2.2 P=0.02. Actual pace was slower than predicted pace during the first km in both the higher risk perceivers, t(9)=-4.2 P=0.001, and lower risk-perceivers, t(9)=-1.8 P=0.049. In experiment 2, pace during the first 36 km was faster among lower compared to higher risk-perceivers, t(16)=2.0 P=0.03. Irrespective of risk-perception group, actual pace was slower than predicted pace during the first 18 km, t(16)=8.9 P<0.001, and from 18 to 36 km, t(16)=4.0 P<0.001. In both experiments there was no difference in performance between higher and lower risk-perception groups. CONCLUSIONS: Initial pace is associated with an individual's perception of risk, with low perceptions of risk being associated with a faster starting pace. Large differences between predicted and actual pace suggests the performance template lacks accuracy, perhaps indicating greater reliance on momentary pacing decisions rather than pre-planned strategy.