Institutional members access full text with Ovid®

Share this article on:

Precision in the Prediction of Middle Distance-Running Performances Using Either a Nomogram or the Modeling of the Distance-Time Relationship

Coquart, Jeremy B J1,2; Bosquet, Laurent3,4

Journal of Strength & Conditioning Research: November 2010 - Volume 24 - Issue 11 - pp 2920-2926
doi: 10.1519/JSC.0b013e3181c69278
Original Research

Coquart, JBJ and Bosquet, L. Precision in the prediction of middle distance-running performances using either a nomogram or the modeling of the distance-time relationship. J Strength Cond Res 24(11): 2920-2926, 2010-The purpose was to determine the levels of precision in the prediction of middle-distance performances in running using the modeling of the distance-time relationship and a nomogram. Official French running rankings for the men's 3,000; 5,000; and 10,000 m were scrutinized from 1996 to 2007. Only runners who competed over the 3 distances within the same year were included (n = 100). The distance-time relationship was modeled using a linear 2-parameter model from the plot of 2 performances to predict a third one. The nomogram of Mercier was also used to predict 1 performance from the use of the other 2. Actual and predicted performances were significantly different, except for the 5,000- and 10,000-m performances predicted from the nomogram (p > 0.05). Effect sizes (ESs) were lower when the performance was predicted by the nomogram (−0.25 < ES < 0.05) compared with the linear 2-parameter model (−0.99 < ES < 0.47). The predicted performances were significantly correlated to the actual performances (r > 0.46; p < 0.01). The bias ± limits of agreement for the 3,000-; 5,000-; and 10,000-m performances were 1.0 ± 12.8, −0.1 ± 6.9, and 0.1 ± 20.8% and 3.7 ± 15.5, −1.4 ± 6.2, and 2.5 ± 10.6% for prediction from the nomogram and distance-time relationship, respectively. Although the modeling of the distance-time relationship does not enable middle-running performances to be accurately predicted, the precision in the predictions from the nomogram suggests that the nomogram may be used to prescribe adapted training intensities and determine the optimal strategy during the race.

1Laboratory of Human Movement Studies, Faculty of Sports Sciences and Physical Education, University of Lille 2, Ronchin, France; 2Quality Department, Germon and Gauthier Hospital, Béthune, France; 3Laboratory of Exercise Physiology, Department of Kinesiology, University of Montreal, Montreal, Canada; and 4Laboratory of Exercise-Induced Physiological Adaptations, Faculty of Sports Sciences, University of Poitiers, Poitiers, France

Address correspondence to Jérémy Coquart,

© 2010 National Strength and Conditioning Association