Physical predictors of elite skeleton start performance

Research output: Contribution to journalArticle

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Abstract

Purpose: An extensive battery of physical tests is typically employed to evaluate athletic status and/or development often resulting in a multitude of output variables. We aimed to identify independent physical predictors of elite skeleton start performance overcoming the general problem of practitioners employing multiple tests with little knowledge of their predictive utility. Methods: Multiple two day testing sessions were undertaken by 13 high level skeleton athletes across a 24-week training season and consisted of flexibility, dry-land push-track, sprint, countermovement jump and leg press tests. To reduce the large number of output variables to independent factors, principal component analysis was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple regression analysis and K-fold validation assessed model stability. Results: Principal component analysis revealed three components underlying the physical variables, which represented sprint ability, lower limb power and strength power characteristics. Three variables, which represented these components (unresisted 15-m sprint time, 0-kg jump height and leg press force at peak power, respectively), significantly contributed (P < 0.01) to the prediction (R2 = 0.86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs. actual R2 = 0.77; 1.97% standard error of estimate). Conclusions: Only three physical test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring potentially benefitting sports scientists, coaches and athletes alike.
LanguageEnglish
Pages81-89
JournalInternational Journal of Sports Physiology and Performance
Volume12
Issue number1
DOIs
StatusPublished - Jan 2017

Fingerprint

Skeleton
Athletes
Principal Component Analysis
Sports
Leg
General Practitioners
Lower Extremity
Regression Analysis

Keywords

  • athletes
  • testing
  • multivariate
  • PCA
  • validation

Cite this

Physical predictors of elite skeleton start performance. / Colyer, Steffi; Stokes, Keith; Bilzon, James; Cardinale, Marco; Salo, Aki.

In: International Journal of Sports Physiology and Performance, Vol. 12, No. 1, 01.2017, p. 81-89.

Research output: Contribution to journalArticle

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abstract = "Purpose: An extensive battery of physical tests is typically employed to evaluate athletic status and/or development often resulting in a multitude of output variables. We aimed to identify independent physical predictors of elite skeleton start performance overcoming the general problem of practitioners employing multiple tests with little knowledge of their predictive utility. Methods: Multiple two day testing sessions were undertaken by 13 high level skeleton athletes across a 24-week training season and consisted of flexibility, dry-land push-track, sprint, countermovement jump and leg press tests. To reduce the large number of output variables to independent factors, principal component analysis was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple regression analysis and K-fold validation assessed model stability. Results: Principal component analysis revealed three components underlying the physical variables, which represented sprint ability, lower limb power and strength power characteristics. Three variables, which represented these components (unresisted 15-m sprint time, 0-kg jump height and leg press force at peak power, respectively), significantly contributed (P < 0.01) to the prediction (R2 = 0.86, 1.52{\%} standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs. actual R2 = 0.77; 1.97{\%} standard error of estimate). Conclusions: Only three physical test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring potentially benefitting sports scientists, coaches and athletes alike.",
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