Predicting higher selection in elite junior Australian Rules football: The influence of physical performance and anthropometric attributes

Journal of Science and Medicine in Sport

Volumn 18, Issue 5, 2015, Pages 601-606

  Robertson, Sam ^a   Woods, Carl ^b   Gastin, Paul ^a  

^a DEAKIN UNIVERSITY   (Australia)

^b EDITH COWAN UNIVERSITY   (Australia)

Author keywords

Logistic regression; Prediction; Relative age effect; Rule induction; Talent identification

Indexed keywords

AGILITY; ANTHROPOMETRY; ARTICLE; ATHLETE; AUSTRALIA; BODY HEIGHT; BODY MASS; CROSS-SECTIONAL STUDY; ENDURANCE; FOOTBALL; HUMAN; JUMPING; OBSERVATIONAL STUDY; PHYSICAL PERFORMANCE; RUNNING; VELOCITY; ATHLETIC PERFORMANCE; MALE; PHYSIOLOGY; RANDOMIZATION; STANDARDS; STATISTICAL MODEL;

ATHLETIC PERFORMANCE; AUSTRALIA; BODY HEIGHT; BODY MASS INDEX; CROSS-SECTIONAL STUDIES; FOOTBALL; HUMANS; LOGISTIC MODELS; MALE; RANDOM ALLOCATION; RUNNING;

EID: 84938691875     PISSN: 14402440     EISSN: 18781861     Source Type: Journal    
DOI: 10.1016/j.jsams.2014.07.019     Document Type: Article

References (30)

1. Gee C.J., John C.M., King J.F. Should coaches use personality assessments in the talent identification process? A 15 year predictive study on professional hockey players. Int J Sports Sci Coach 2010, 4(1):25-34.
2. Gastin P.B., Fahrner B., Meyer D., et al. Influence of physical fitness, age, experience, and weekly training load on match performance in elite Australian football. J Strength Cond Res 2013, 27(5):1272-1279.
3. Mooney M.G., Hunter J.R., O'Brien B.J., et al. Reliability and validity of a novel intermittent peak running speed test for Australian football. J Strength Cond Res 2011, 25(4). PMID: 60170229.
4. Young W., Newton R., Doyle T., et al. Physiological and anthropometric characteristics of starters and non-starters and playing positions in elite Australian rules football: a case study. J Sci Med Sport 2005, 8(3):333-345.
5. Gastin P.B., Bennett G., Cook J. Biological maturity influences running performance in junior Australian football. J Sci Med Sport 2013, 16(2):140-145.
6. Woods C., Raynor A.J., Bruce L., et al. Predicting playing status in junior Australian Football using physical and anthropometric parameters. J Sci Med Sport 2014, 10.1016/j.jsams.2014.02.006.
7. Hoare D.G. Predicting success in junior elite basketball players - the contribution of anthropometric and physiological attributes. J Sci Med Sport 2000, 3(4):391-405.
8. Gabbett T., Georgieff B., Domrow N. The use of physiological, anthropometric, and skill data to predict selection in a talent-identified junior volleyball squad. J Sports Sci 2007, 25(12):1337-1344.
9. Pyne D., Gardner A., Sheehan K., et al. Fitness testing and career progression in AFL football. J Sci Med Sport 2005, 8(3):321-332.
10. Veale J.P., Pearce A.J., Koehn S., et al. Performance and anthropometric characteristics of prospective elite junior Australian footballers: a case study in one junior team. J Sci Med Sport 2008, 11(2):227-230.
11. Keogh J. The use of physical fitness scores and anthropometric data to predict selection in an elite under 18 Australian rules football team. J Sci Med Sport 1999, 2(2):125-133.
12. Coutts A.J., Kempton T., Vaeyens R. Relative age effects in Australian Football League national draftees. J Sports Sci 2014, 32(7):623-628.
13. Pyne D., Gardner A., Sheehan K., et al. Positional differences in fitness and anthropometric characteristics in Australian football. J Sci Med Sport 2006, 9(1-2):143-150.
14. Burgess D., Naughton G., Hopkins W. Draft-camp predictors of subsequent career success in the Australian Football League. J Sci Med Sport 2012, 15(6):561-567.
15. Gore C.J. Physiological tests for elite athletes 2000, Human Kinetics, Lower Mitcham. 2nd ed.
16. Cooper H.M., Hedges L.V., Valentine J.C. The handbook of research synthesis and meta-analysis 2009, Russell Sage Foundation, New York, USA. 2nd ed.
17. Hopkins W.G. Measures of reliability in sports medicine and science. Sports Med 2000, 30(1):1-15.
18. Cohen W. Fast effective rule induction. Twelfth international conference on machine learning 1995, 115-123.
19. Hall M., Frank E., Holmes G., et al. The WEKA data mining software: an update. SIGKDD Explor 2009, 11(1):10-18.
20. Hornik K., Buchta C., Zeileis A. Open-source machine learning: R meets Weka. Comput Stat 2009, 24(2):225-232.
21. Cohen J. Statistical power analysis for the behavioral sciences 1988, L. Erlbaum Associates, Hillsdale, USA. 2nd ed.
22. Witten I.H., Frank E., Hall M.A. Data mining: practical machine learning tools and techniques 2011, Morgan Kaufmann, San Francisco, USA. 3rd ed.
23. Australian Football League AFL Youth Coaching Manual 2012, Available at: http://www.aflcommunityclub.com.au/fileadmin/user_upload/Coach_AFL/coaching_manual/2012_Youth_Coaching_Manual.pdf [accessed 03.03.14].
24. Vaeyens R., Lenoir M., Williams A.M., et al. Talent identification and development programmes in sport: current models and future directions. Sports Med 2008, 38(9):703-714.
25. Russell M., Benton D., Kingsley M. Reliability and construct validity of soccer skills tests that measure passing, shooting, and dribbling. J Sports Sci 2010, 28(13):1399-1408.
26. Ali A., Williams C., Hulse M., et al. Reliability and validity of two tests of soccer skill. J Sports Sci 2007, 25(13):1461-1470.
27. Vaeyens R., Lenoir M., Williams A.M., et al. Mechanisms underpinning successful decision making in skilled youth soccer players: an analysis of visual search behaviors. J Motor Behav 2007, 39(5):395-408.
28. Lorains M., Ball K., MacMahon C. Expertise differences in a video decision-making task: speed influences on performance. Psychol Sport Exerc 2013, 14(2):293-297.
29. Robertson S.J., Burnett A.F., Newton R.U. Development and validation of the Approach-Iron Skill Test for use in golf. Eur J Sport Sci 2013, 13(6):615-621.
30. Robertson S.J., Burnett A.F., Newton R.U., et al. Development of the Nine-Ball Skills Test to discriminate elite and high-level amateur golfers. J Sports Sci 2012, 30(5):431-437.