Skip to main content

Pacing accuracy in collegiate and recreational runners


To examine runners’ ability to produce a prescribed pace, we compared prescribed versus actual 400 m splits for collegiate (COL, n = 12) and recreational runners (REC, n = 16). Participants completed a VO2max trial and on a 400 m track, three 3,200 m time trials. During three subsequent sessions, participants completed 800 m warm-up; then, based on their fastest 3,200 m steady pace, subjects completed six laps total at three prescribed paces: (a) 2× 400 m at 7% slower than steady pace (SLO), (b) 2× 400 m at steady pace (AT) and (c) 2× 400 m at 7% faster than steady pace (FAS). Instructions were to complete the sets of two laps in prescribed times (e.g., 75 s per 400 m) (no feedback). Deviation scores (absolute value of difference: prescribed vs. actual time) (s) for each 400 m lap were compared using a 2 (group) × 3 (trial) repeated measures ANOVA. Main effects for deviations among trials SLO (7.3 ± 6.5), AT (6.6 ± 6.9) and FAS (6.2 ± 5.7) were not significantly different (p > 0.05). However, group main effect for deviation scores was significantly (p < 0.05) lower (greater accuracy) for COL (2.9 ± 3.2 s) versus REC (9.5 ± 6.6 s). Deviation scores were also significantly different (p < 0.05) for SLO (COL: 3.1 ± 2.7 s, REC: 10.4 ± 6.7 s) and AT (COL: 1.9 ± 1.9 s, REC: 10.1 ± 7.2 s), with a trend for FAS (p = 0.06) (COL: 3.8 ± 4.3 s, REC: 7.9 ± 6.1 s). Bland–Altman plots showed better agreement (prescribed vs. actual) for COL. Experience and fitness of collegiate runners resulted in improved pacing accuracy.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3


  1. Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison studies. Statistician 32:307–317

    Article  Google Scholar 

  2. Ansley A, Schabort E, Gibson A, Lambert M, Noakes TD (2004) Regulation of pacing strategies during successive 4-km time trials. Med Sci Sports Exerc 36:1819–1825

    Article  PubMed  Google Scholar 

  3. Bannister R (2002) (Interview), website:

  4. Bland JM, Altman DG (1995) Statistical method for assessing agreement between two methods of clinical measurement. Lancet 346:1085–1087

    Article  CAS  PubMed  Google Scholar 

  5. Borg G (1982) Psychological bases of perceived exertion. Med Sci Sports Exerc 14:377–381

    CAS  PubMed  Google Scholar 

  6. Firth M (1998) From high-tech to low tech: another look at time trail pacing strategy. Coaching News 3:7–10

    Google Scholar 

  7. Foster C, Snyder AC, Thompson NN, Green MA, Foley M, Schrager M (1993) Effect of pacing strategy on cycle time trial performance. Med Sci Sports Exerc 25:383–388

    CAS  PubMed  Google Scholar 

  8. Maud PJ, Foster C (2006) Physiological assessment of human fitness. Human Kinetics, Champagne, IL

    Google Scholar 

  9. Pollock ML, Schmidt DH, Jackson AS (1980) Measurement of cardiorespiratory fitness and body composition in the clinical setting. Clin Therapy 6:12–27

    Google Scholar 

  10. Sapp AL, Green JM, Bishop PA, Richardson M, Kerr K, Pritchett RC, Curtner-Smith M, Geisen J (2007) Pacing strategies: effects of experience and gender in runners during a 3200-m time trial. Med Sci Sports Exerc 39:S349

    Google Scholar 

  11. Tucker R, Lambert MI, Noakes TD (2004) An analysis of pacing strategies during men’s world-record performances in track athletics. Int J Sports Physiol Perf 1:233–245

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to J. Matthew Green.

Additional information

Communicated by Susan Ward.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Green, J.M., Sapp, A.L., Pritchett, R.C. et al. Pacing accuracy in collegiate and recreational runners. Eur J Appl Physiol 108, 567–572 (2010).

Download citation


  • Running splits
  • Pacing
  • Racing
  • Fitness