Int J Sports Med 2022; 43(09): 773-782
DOI: 10.1055/a-1673-6829
Training & Testing

Prediction of Distance Running Performances of Female Runners Using Nomograms

1   UFR STAPS, CETAPS, Université de Rouen-Normandie, Mont-Saint-Aignan, France
Brice Guignard
1   UFR STAPS, CETAPS, Université de Rouen-Normandie, Mont-Saint-Aignan, France
Ghazi Racil
2   Physiology, Université de la Manouba Institut Supérieur du Sport et de l'Éducation Physique de Ksar Saïd, Manouba, Tunisia
Mohamed Chedly Jlid
2   Physiology, Université de la Manouba Institut Supérieur du Sport et de l'Éducation Physique de Ksar Saïd, Manouba, Tunisia
Eric Held
3   Orthodynamica, Clinique Mathildle II, Rouen, France
Jeremy Bernard Coquart
1   UFR STAPS, CETAPS, Université de Rouen-Normandie, Mont-Saint-Aignan, France
4   Uninv, Lille, Univ, Artois, Univ. Littoral côte d'Opale, ULR 7369 - URePSSS - Unité de Recherche Pluridiscplinaire Sport Santé Société, Lille, France
› Author Affiliations


This study examined the validity, precision and accuracy of the predictions of distance running performances in female runners from three nomograms. Official rankings of French women for the 3000-m, 5000-m, and 10 000-m track-running events from 2005 to 2019 were examined. Only female runners who performed in the three distance events within the same year were included (n=158). Each performance over any distance was predicted using the three nomograms from the two other performances. The 3000-m, 5000-m and 10 000-m performances were 11min17 s±1min20 s, 19min29 s±2min20 s, 41min18 s±5min7 s, respectively. No difference was found between the actual and predicted running performances regardless of the nomogram (p>0.05). All predicted running performances were significantly correlated with the actual ones, with a very high correlation coefficient (p<0.001; r>0.90). Bias and 95% limits of agreement were acceptable because, whatever the nomogram, they were less than or equal to − 0.0±6.2% on the 3000-m, 0.0±3.7% on the 5000-m, and 0.1±9.3% on the 10 000-m. The study confirms the validity of the three nomograms to predict track-running performance with a high level of accuracy. The predictions from these nomograms are similar and may be used in training programs and competitions.

Publication History

Received: 08 April 2021

Accepted: 14 October 2021

Accepted Manuscript online:
19 October 2021

Article published online:
25 April 2022

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  • References

  • 1 Coquart JB, Mercier D, Tabben M. et al. Influence of sex and specialty on the prediction of middle-distance running performances using the Mercier et al.’s nomogram. J Sports Sci 2015; 33: 1124-1131
  • 2 Tabben M, Bosquet L, Coquart JB. Effect of performance level on the prediction of middle-distance-running performances using a nomogram. Int J Sports Physiol Perform 2016; 11: 623-626
  • 3 Blythe DAJ, Király FJ. Prediction and quantification of individual athletic performance of runners. PLoS One 2016; 11: e0157257
  • 4 Billat LV, Koralsztein JP, Morton RH. Time in human endurance models. From empirical models to physiological models. Sports Med 1999; 27: 359-379
  • 5 Ettema JH. Limits of human performance and energy-production. Int Z Angew Physiol Einschl Arbeitsphysiol 1966; 22: 45-54
  • 6 Scherrer J, Monod H. Le travail musculaire local et la fatigue chez l'homme. Presse Med 1960; 68: 1717-1717
  • 7 Coquart J, 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 2010; 24: 2920-2926
  • 8 Zinoubi B, Vandewalle H, Driss T. Modeling of running performances in humans: comparison of power laws and critical speed. J Strength Cond Res 2017; 31: 1859-1867
  • 9 Vandewalle H. A nomogram of performances in endurance running based on logarithmic model of péronnet-thibault. Am J Eng Res 2017; 6: 78-85
  • 10 Vandewalle H. Modelling of running performances: comparisons of power-law, hyperbolic, logarithmic, and exponential models in elite endurance runners. BioMed Res Int 2018; 2018: 8203062
  • 11 Gamelin FX, Coquart JM, Ferrari N. et al. Prediction of one-hour running performance using constant duration tests. J Strength Cond Res 2006; 20: 735-739
  • 12 Mercier D, Léger L, Desjardins M. Nomogramme pour prédire la performance, le VO2max et l’endurance relative en course de fond. Médecine du Sport 1984; 58: 181-187
  • 13 Tokmakidis SP, Léger L, Mercier D. et al. New approaches to predict VO2max and endurance from running performances. J Sports Med Phys Fitness 1987; 27: 401-409
  • 14 Coquart J, Alberty M, Bosquet L. Validity of a nomogram to predict long distance running performance. J Strength Cond Res 2009; 23: 2119-2123
  • 15 Péronnet F, Thibault G. Mathematical analysis of running performance and world running records. J Appl Physiol (1985) 1989 67: 453-465
  • 16 Smyth B, Muniz-Pumares D. Calculation of critical speed from raw training data in recreational marathon runners. Med Sci Sports Exerc 2020; 52: 2637-2645
  • 17 Emig T, Peltonen J. Human running performance from real-world big data. Nat Commun 2020; 11: 4936
  • 18 Harriss DJ, MacSween A, Atkinson G. Ethical standards in sport and exercise science research: 2020 update. Int J Sports Med 2019; 40: 813-817
  • 19 Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016; 15: 155-163
  • 20 Cohen J. Statistical power analysis. Curr Dir Psychol Sci 1992; 1: 98-101
  • 21 Munro BH. Correlation. In: Statistical Methods for Healthcare Research. Philadelphia, PA: Lippincott-Raven; 1997: 224–245
  • 22 Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 327: 307-310
  • 23 Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999; 8: 135-160
  • 24 Di Prampero PE, Atchou G, Brückner JC. et al. The energetics of endurance running. Eur J Appl Physiol 1986; 55: 259-266
  • 25 Péronnet F, Thibault G. Physiological analysis of running performance: revision of the hyperbolic model. J Physiol (Paris) 1987; 82: 52-60
  • 26 Lerebourg L, Coquart JB. Changes in performances/characteristics of French female runners over the last 12 years. Res Sports Med 2021; 29: 185–195
  • 27 Bosquet L, Léger L, Legros P. Methods to determine aerobic endurance. Sports Med 2002; 32: 675-700