Physiological predictors of short-course triathlon performance

Sleivert, Gordon G.; Wenger, Howard A.

doi:10.1249/00005768-199307000-00017

Cited by 71 publications

(48 citation statements)

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“…These predictions have been based upon correlation analysis, (Butts, Henry & McLean, 1991;Dengel et al, 1989) and to a much lesser extent on multiple linear regression analysis (Sleivert & Wenger, 1993 of analysis within a unidimensional approach, i.e., examining only physiological variables. The use of both multivariate techniques and multidimensional designs has been recommended to enhance the possibilities of performance prediction, whether performance is operationalised in terms of differences between successful/qualifier -nonsuccessful / nonqualifier or in terms of some direct measurement of ability.…”

Section: Attribution Theorymentioning

confidence: 99%

Self-efficacy and its relationship to selected sport psychological constructs in the prediction of performance in ironman triathlon

Heazlewood¹,

Burke²

2011

JHSE

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Heazlewood I, Burke S. Self-efficacy and its relationship to selected sport psychological constructs in the prediction of performance in ironman triathlon. J. Hum. Sport Exerc. Vol. 6, No. 2, pp. 328-350, 2011. The focus of this study was to apply this approach to an Ironman TM (3.8km swim, 180km cycle, and 42.2km run) triathlon event. The methodology utilized physiological measures (VO 2max , adiposity, height, weight), history of performance and sport psychological constructs (self-efficacy, motivation, sport confidence, cognitive and somatic anxiety) to predict total performance time and individual swim, cycle and run performance times. The results, utilizing correlation, regression and path analysis indicated that performance can be predicted more accurately when variables are assessed from a variety of Human Movement domains. The correlation analysis indicated only outcome orientations (r=0.68, p<0.001) and performance orientation (r=0.70, p<0.001) were significantly correlated with triathlon performance as compared to the non-predictive value of the CSAI-2 component of somatic anxiety, the attribution variables of internal, luck and powerful others, and history of previous performance. The path model explained a significant 44% of the variance of race performance. An investigation of the mediation of efficacy on performance in the race revealed that when direct paths were drawn from self-estimations to performance, non-significant regression weights were reported for the indirect paths to predict performance.

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Section: Attribution Theorymentioning

confidence: 99%

Self-efficacy and its relationship to selected sport psychological constructs in the prediction of performance in ironman triathlon

Heazlewood¹,

Burke²

2011

JHSE

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“…Performance prediction is commonly assessed by means of correlation and regression analyses (Butts & McLean, 1991;Sleivert & Wenger, 1993;Van Schuylenbergh, Vanden Eynde, & Hespel, 2004), signifi cance tests (Kohrt, Morgan, Bates, & Skinner, 1987;Heiden & Burnett, 2003), analysis of variance (Burke & Jin, 1996;Landers, Blanksby, Ackland, & Smith, 2000), and multidimensional linear regresion and regresion equations (Schabort, Killian, St Clair Gibson, Hawley, & Noakes, 2000;Fröhlich, Klein, Pieter, Emrich, & Gießing, 2008). Studies have not brought any complex approach to performance prediction yet; our aim was to apply a structural approach to the issue of triathlon talent selection and develop a testing profi le predicting performance in the triathlon by means of factor analysis (Blahuš, 1980;Blahuš, 1985;McDonald, 1991).…”

Section: Methodology Of Talent Selection For Short Triathlonmentioning

confidence: 99%

Verification of the model of predisposition in triathlon - structural model of confirmative factor analysis

Kovářová¹,

Kovář²

2012

Acta Gymnica

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BACKGROUND:The triathlon is a combination of three diff erent types of sport -swimming, cycling, and running. Each of these requires diff erent top level predispositions and complex approach to talent selection is a rather diffi cult process. Attempts to identify assumptions in the triathlon have so far been specifi c and focused only on some groups of predispositions (physiology, motor tests, and psychology). The latest studies missed the structural approach and were based on determinants of sport performance, theory of sports training and expert assessment.OBJECTIVE: The aim of our study was to verify the model of predisposition in the short triathlon for talent assessment of young male athletes age 17-20 years.METHODS: The research sample consisted of 55 top level triathletes -men, who were included in the Government supported sports talent programme in the Czech Republic at the age of 17-20 years. We used a confi rmative factor analysis (FA) and Path diagram to verify the model, which allow us to explain mutual relationships among observed variables. For statistical data processing we used a structure equating modeling (SEM) by software Lisrel L88. RESULTS:The study confi rms best structural model for talent selection in triathlon at the age of 17-20 years old men, which composed seventeen indicators (tests) and explained 91% of all cross-correlations (Goodness of Fit Index /GFI/ 0.91, Root Mean Square Residual /RMSR/ 0.13). Tests for predispositions in triathlons were grouped into fi ve items, three motor predispositions (swimming, cycling and running skills), aerobic and psychological predispositions. Aerobic predispositions showed the highest importance to the assumptions to the general factor (1.00; 0). Running predispositions were measured as a very signifi cant factor (-0.85; 0.28) which confi rms importance of this critical stage of the race. Lower factor weight showed clusters of swimming (-0.61; 0.63) and cycling (0.53; 0.72) predispositions. The psychological predispositions were found in the lowest eff ect (0.36; 0.87).CONCLUSIONS: We confi rm predispositions and tests for measurement of its level for top level male triathletes in the junior category. The model is in practice today in the Czech Republic, where we used the test standards for dividing Government support of athletic talent.

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“…Previous studies mostly tested recreational triathletes (Kohrt, Morgan, Bates, & Skinner, 1987;Millet et al, 2011;Miura, Kitagawa, & Ishiko, 1997;Sleivert & Wenger, 1993) because of the availability of a larger number of potential athletes. National squads normally consist of 4-5 athletes, which makes it very difficult to get a sufficient sized sample.…”

Section: Introductionmentioning

confidence: 99%

Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks

Hoffmann

Moeller

Seidel

et al. 2017

International Journal of Computer Science in Sport

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Two different computational approaches were used to predict Olympic distance triathlon race time of German male elite triathletes. Anthropometric measurements and two treadmill running tests to collect physiological variables were repeatedly conducted on eleven male elite triathletes between 2008 and 2012. After race time normalization, exploratory factor analysis (EFA), as a mathematical preselection method, followed by multiple linear regression (MLR) and dominance paired comparison (DPC), as a preselection method considering professional expertise, followed by nonlinear artificial neural network (ANN) were conducted to predict overall race time. Both computational approaches yielded two prediction models. MLR provided R² = 0.41 in case of anthropometric variables (predictive: pelvis width and shoulder width) and R² = 0.67 in case of physiological variables (predictive: maximum respiratory rate, running pace at 3-mmol·L -1 blood lactate and maximum blood lactate). ANNs using the five most important variables after DPC yielded R² = 0.43 in case of anthropometric variables and R² = 0.86 in case of physiological variables. The advantage of ANNs over MLRs was the possibility to take non-linear relationships into account. Overall, race time of male elite triathletes could be well predicted without interfering with individual training programs and season calendars.

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Physiological predictors of short-course triathlon performance

Cited by 71 publications

References 0 publications

Self-efficacy and its relationship to selected sport psychological constructs in the prediction of performance in ironman triathlon

Self-efficacy and its relationship to selected sport psychological constructs in the prediction of performance in ironman triathlon

Verification of the model of predisposition in triathlon - structural model of confirmative factor analysis

Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks

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