This article was downloaded by: [University of Waikato] On: 25 May 2014, At: 07:04 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Journal of Sports Sciences Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rjsp20
Modelling mood states in athletic performance a
a
Ian M. Cockerill , Alan M. Nevill & Noel Lyons
a
a
School of Sport and Exercise Sciences , University of Birmingham , PO Box 363, Birmingham, B15 2TT, UK Published online: 14 Nov 2007.
To cite this article: Ian M. Cockerill , Alan M. Nevill & Noel Lyons (1991) Modelling mood states in athletic performance, Journal of Sports Sciences, 9:2, 205-212, DOI: 10.1080/02640419108729881 To link to this article: http://dx.doi.org/10.1080/02640419108729881
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Journal of Sports Sciences, 1991, 9, 205-212
Modelling mood states in athletic performance IAN M. COCKERILL, ALAN M. NEVILL and NOEL LYONS School of Sport and Exercise Sciences, University of Birmingham, PO Box 363, Birmingham B15 2TT, UK
Accepted 19 November 1990
Downloaded by [University of Waikato] at 07:04 25 May 2014
Abstract
Because moods are transitory emotional states that can be influenced by a range of personality and environmental factors, the notion that elite athletes will always tend to produce a so-called iceberg profile of mood, and that less successful performers will not, is open to question. Evidence for such a claim is based principally upon descriptive studies. The present experiment used the POMS inventory as a predictor of cross-country running performance among a group of experienced male athletes. Race times from two competitive events were plotted against each of six mood factors. Using data from race 1, a multiple-regression model - incorporating the interdependence of tension, anger and depression was able to predict rank order of finishing positions for race 2 with acceptable accuracy (rs = 0.74, P =0.069 and /"=0.092 respectively. When the runners' mood states for each of the six factors from race 1 were plotted against their race times, no clear linear or curvilinear relationships were found. However, when all six POMS factors were analysed using the MINITAB multiple-regression routine 'BREG', the best subset of mood factors was found to be: Perf(min) = 62.6 - 0.266(Ten)+0.246(Dep) - 0.317(Ang)
(1)
where Perf(min) = race time, Ten = tension, Dep=depression and Ang = anger. The BREG command carries out a 'best subsets' regression analysis that uses the maximum coefficient of determination R2 as the criterion. The output provides four criterion statistics for each model under investigation: the coefficient of determination R2; the adjusted coefficient of determination (adj R2); Mallow's criterion (Cp); and V the standard deviation of errors about the regression line. The model chosen, and shown in equation (1), produced the maximum adj R2 = 33.7 (F 377 = 4.31, P
Journal of Sports Sciences Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rjsp20
Modelling mood states in athletic performance a
a
Ian M. Cockerill , Alan M. Nevill & Noel Lyons
a
a
School of Sport and Exercise Sciences , University of Birmingham , PO Box 363, Birmingham, B15 2TT, UK Published online: 14 Nov 2007.
To cite this article: Ian M. Cockerill , Alan M. Nevill & Noel Lyons (1991) Modelling mood states in athletic performance, Journal of Sports Sciences, 9:2, 205-212, DOI: 10.1080/02640419108729881 To link to this article: http://dx.doi.org/10.1080/02640419108729881
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Journal of Sports Sciences, 1991, 9, 205-212
Modelling mood states in athletic performance IAN M. COCKERILL, ALAN M. NEVILL and NOEL LYONS School of Sport and Exercise Sciences, University of Birmingham, PO Box 363, Birmingham B15 2TT, UK
Accepted 19 November 1990
Downloaded by [University of Waikato] at 07:04 25 May 2014
Abstract
Because moods are transitory emotional states that can be influenced by a range of personality and environmental factors, the notion that elite athletes will always tend to produce a so-called iceberg profile of mood, and that less successful performers will not, is open to question. Evidence for such a claim is based principally upon descriptive studies. The present experiment used the POMS inventory as a predictor of cross-country running performance among a group of experienced male athletes. Race times from two competitive events were plotted against each of six mood factors. Using data from race 1, a multiple-regression model - incorporating the interdependence of tension, anger and depression was able to predict rank order of finishing positions for race 2 with acceptable accuracy (rs = 0.74, P =0.069 and /"=0.092 respectively. When the runners' mood states for each of the six factors from race 1 were plotted against their race times, no clear linear or curvilinear relationships were found. However, when all six POMS factors were analysed using the MINITAB multiple-regression routine 'BREG', the best subset of mood factors was found to be: Perf(min) = 62.6 - 0.266(Ten)+0.246(Dep) - 0.317(Ang)
(1)
where Perf(min) = race time, Ten = tension, Dep=depression and Ang = anger. The BREG command carries out a 'best subsets' regression analysis that uses the maximum coefficient of determination R2 as the criterion. The output provides four criterion statistics for each model under investigation: the coefficient of determination R2; the adjusted coefficient of determination (adj R2); Mallow's criterion (Cp); and V the standard deviation of errors about the regression line. The model chosen, and shown in equation (1), produced the maximum adj R2 = 33.7 (F 377 = 4.31, P
