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Modelling mood states in athletic performance.

I M Cockerill1, A M Nevill, N Lyons

  • 1School of Sport and Exercise Sciences, University of Birmingham, UK.

Journal of Sports Sciences
|January 1, 1991
PubMed
Summary
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Elite athletes' mood profiles are not always predictive of performance. A new model using the POMS inventory accurately predicted cross-country running performance based on mood factors like tension, anger, and depression.

Area of Science:

  • Sport Psychology
  • Exercise Science
  • Performance Psychology

Background:

  • The traditional 'iceberg profile' model suggests elite athletes consistently display specific mood states.
  • This model's descriptive nature limits its predictive power for athletic success.
  • Previous evidence relies heavily on observational studies, questioning its universal applicability.

Purpose of the Study:

  • To challenge the static 'iceberg profile' theory in sport psychology.
  • To develop a predictive model of athletic performance using mood states.
  • To investigate the relationship between specific mood factors and cross-country running performance.

Main Methods:

  • Utilized the Profile of Mood States (POMS) inventory with experienced male cross-country runners.

Related Experiment Videos

  • Collected race times from two competitive events for performance data.
  • Employed a multiple-regression model to analyze mood factors (tension, anger, depression) and predict race outcomes.

    • Main Results:

    • A multiple-regression model incorporating tension, anger, and depression accurately predicted the rank order of finishing positions (r = 0.74, P < 0.01).
    • The predictive model demonstrated acceptable accuracy for cross-country running performance.
    • This study offers a prescriptive rather than purely descriptive approach to mood research in sports.

    Conclusions:

    • Mood states, specifically tension, anger, and depression, can be prescriptive predictors of athletic performance in cross-country running.
    • The developed model offers a novel, predictive approach to understanding the link between mood and athletic success.
    • Alternative predictive models may be necessary for sports with different physiological and psychological demands.