Predicting playing status in junior Australian Football using physical and anthropometric parameters.

OBJECTIVES To use physical and anthropometric parameters to predict playing status in junior Australian Football. DESIGN Cross-sectional observational. METHODS Participants were recruited from the under 18 competition within the West Australian Football League and classified into two groups; elite (state representative; n=50; 17.9 ± 0.5 y; 184.8 ± 6.9 cm; 80.6 ± 9.4 kg) and sub-elite (non-state representative; n=50; 17.8 ± 0.6 y; 179.8 ± 5.4 cm; 74.4 ± 7.9 kg). Both groups completed physical/anthropometric tests inclusive of a 5 m, 10 m and 20 m sprint, an agility test, stationary vertical jump, dynamic dominant and non-dominant foot vertical jump, 20 m multistage fitness test, standing height and body mass. A multivariate analysis of variance was used to test the main effect of 'status' on the physical/anthropometric parameters, whilst logistic regression models were used to predict playing status using the physical/anthropometric parameters. RESULTS On average, the elite group were taller, heavier, had a greater stationary vertical jump, dynamic dominant and non-dominant foot vertical jump and higher maximal aerobic capacity as measured by the multistage fitness test (p<0.05). The combination of standing height, dynamic vertical jump non-dominant foot and the 20 m multistage fitness test were the strongest predictors of status (Akaike's Information Criterion=96.35). CONCLUSIONS Despite mean differences in a number of parameters, the combination of standing height, dynamic vertical jump non-dominant foot and the multistage fitness test were the strongest predictors of status and thus important tests for initially identifying potential talent in junior Australian Football.

[1]  David R Bassett,et al.  Test of the classic model for predicting endurance running performance. , 2010, Medicine and science in sports and exercise.

[2]  Geraldine Naughton,et al.  Draft-camp predictors of subsequent career success in the Australian Football League. , 2012, Journal of science and medicine in sport.

[3]  J. Cook,et al.  Biological maturity influences running performance in junior Australian football. , 2013, Journal of science and medicine in sport.

[4]  K. Janz Growth, maturation, and physical activity, 2nd edition , 2004 .

[5]  B Dawson,et al.  Physiological and anthropometric characteristics of starters and non-starters and playing positions in elite Australian Rules Football: a case study. , 2005, Journal of science and medicine in sport.

[6]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[7]  Lana Ruzić,et al.  Predicting the 1000m rowing ergometer performance in 12-13-year-old rowers: the basis for selection process? , 2008, Journal of science and medicine in sport.

[8]  Rebecca K. Tanner,et al.  Physiological tests for elite athletes , 2000 .

[9]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[10]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[11]  J. Mihalik,et al.  The National Football League Combine: Performance Differences Between Drafted and Nondrafted Players Entering the 2004 and 2005 Drafts , 2008, Journal of strength and conditioning research.

[12]  Geraldine Naughton,et al.  Quantifying the gap between under 18 and senior AFL football: 2003-2009. , 2012, International journal of sports physiology and performance.

[13]  J Keogh,et al.  The use of physical fitness scores and anthropometric data to predict selection in an elite under 18 Australian rules football team. , 1999, Journal of science and medicine in sport.

[14]  Warren B Young,et al.  Relationship between pre-season anthropometric and fitness measures and indicators of playing performance in elite junior Australian Rules football. , 2007, Journal of science and medicine in sport.

[15]  James P Veale,et al.  The Yo-Yo Intermittent Recovery Test (Level 1) to discriminate elite junior Australian football players. , 2010, Journal of science and medicine in sport.

[16]  D B Pyne,et al.  Fitness testing and career progression in AFL football. , 2005, Journal of science and medicine in sport.

[17]  O. Bar-or,et al.  Growth, Maturation and Physical Activity , 1992 .

[18]  M. Peruggia Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.) , 2003 .

[19]  Walter Krämer,et al.  Review of Modern applied statistics with S, 4th ed. by W.N. Venables and B.D. Ripley. Springer-Verlag 2002 , 2003 .

[20]  A. Lees,et al.  The biomechanics of kicking in soccer: A review , 2010, Journal of sports sciences.

[21]  L. Burkett,et al.  The National Football League Combine: A Reliable Predictor of Draft Status? , 2003, Journal of strength and conditioning research.