Sam Robertson1, Carl Woods2, Paul Gastin3. 1. Centre for Exercise and Sports Science, School of Exercise and Nutrition Sciences, Deakin University, Australia. Electronic address: sam.robertson@deakin.edu.au. 2. School of Exercise and Health Sciences, Edith Cowan University, Australia. 3. Centre for Exercise and Sports Science, School of Exercise and Nutrition Sciences, Deakin University, Australia.
Abstract
OBJECTIVES: To develop a physiological performance and anthropometric attribute model to predict Australian Football League draft selection. DESIGN: Cross-sectional observational. METHODS: Data was obtained (n=4902) from three Under-18 Australian football competitions between 2010 and 2013. Players were allocated into one of the three groups, based on their highest level of selection in their final year of junior football (Australian Football League Drafted, n=292; National Championship, n=293; State-level club, n=4317). Physiological performance (vertical jumps, agility, speed and running endurance) and anthropometric (body mass and height) data were obtained. Hedge's effect sizes were calculated to assess the influence of selection-level and competition on these physical attributes, with logistic regression models constructed to discriminate Australian Football League Drafted and National Championship players. Rule induction analysis was undertaken to determine a set of rules for discriminating selection-level. RESULTS: Effect size comparisons revealed a range of small to moderate differences between State-level club players and both other groups for all attributes, with trivial to small differences between Australian Football League Drafted and National Championship players noted. Logistic regression models showed multistage fitness test, height and 20 m sprint time as the most important attributes in predicting Draft success. Rule induction analysis showed that players displaying multistage fitness test scores of >14.01 and/or 20 m sprint times of <2.99 s were most likely to be recruited. CONCLUSIONS: High levels of performance in aerobic and/or speed tests increase the likelihood of elite junior Australian football players being recruited to the highest level of the sport.
OBJECTIVES: To develop a physiological performance and anthropometric attribute model to predict Australian Football League draft selection. DESIGN: Cross-sectional observational. METHODS: Data was obtained (n=4902) from three Under-18 Australian football competitions between 2010 and 2013. Players were allocated into one of the three groups, based on their highest level of selection in their final year of junior football (Australian Football League Drafted, n=292; National Championship, n=293; State-level club, n=4317). Physiological performance (vertical jumps, agility, speed and running endurance) and anthropometric (body mass and height) data were obtained. Hedge's effect sizes were calculated to assess the influence of selection-level and competition on these physical attributes, with logistic regression models constructed to discriminate Australian Football League Drafted and National Championship players. Rule induction analysis was undertaken to determine a set of rules for discriminating selection-level. RESULTS: Effect size comparisons revealed a range of small to moderate differences between State-level club players and both other groups for all attributes, with trivial to small differences between Australian Football League Drafted and National Championship players noted. Logistic regression models showed multistage fitness test, height and 20 m sprint time as the most important attributes in predicting Draft success. Rule induction analysis showed that players displaying multistage fitness test scores of >14.01 and/or 20 m sprint times of <2.99 s were most likely to be recruited. CONCLUSIONS: High levels of performance in aerobic and/or speed tests increase the likelihood of elite junior Australian football players being recruited to the highest level of the sport.
Authors: Stephen J Kelly; Mark L Watsford; Michael J Rennie; Rob W Spurrs; Damien Austin; Matthew J Pine Journal: PLoS One Date: 2019-02-28 Impact factor: 3.240