Carl T Woods1, Sam Robertson2, Neil French Collier3, Anne L Swinbourne4, Anthony S Leicht5. 1. Discipline of Sport and Exercise Science, James Cook University, Townsville, QLD, Australia. carl.woods@jcu.edu.au. 2. Institute of Sport, Exercise and Activity Living (ISEAL), Victoria University, Melbourne, VIC, Australia. 3. Faculty of Sustainability, Leuphana University Luneburg, Luneburg, Germany. 4. Psychology, James Cook University, Townsville, QLD, Australia. 5. Discipline of Sport and Exercise Science, James Cook University, Townsville, QLD, Australia.
Abstract
BACKGROUND: Learning transfer is defined as an individual's capability to apply prior learnt perceptual, motor, or conceptual skills to a novel task or performance environment. In the sport sciences, learning transfers have been investigated from an athlete-specific perspective. However, sport scientists should also consider the benefits of cross-disciplinary learning to aid critical thinking and metacognitive skill gained through the interaction with similar quantitative scientific disciplines. OBJECTIVE: Using team sports performance analysis as an example, this study aimed to demonstrate the utility of a common analytical technique in ecology in the sports sciences, namely, nonmetric multidimensional scaling. METHODS: To achieve this aim, three novel research examples using this technique are presented, each of which enables the analysis and visualization of athlete (organism), team (aggregation of organisms), and competition (ecosystem) behaviors. RESULTS: The first example reveals the technical behaviors of Australian Football League Brownlow medalists from the 2001 to 2016 seasons. The second example delineates dissimilarity in higher and lower ranked National Rugby League teams within the 2016 season. Lastly, the third example shows the evolution of game play in the basketball tournaments between the 2004 and 2016 Olympic Games. CONCLUSIONS: In addition to the novel findings of each example, the collective results demonstrate that, by embracing cross-disciplinary learning and drawing upon an analytical technique common to ecology, novel solutions to pertinent research questions within sports performance analysis could be addressed in a practically meaningful way. Cross-disciplinary learning may subsequently assist sport scientists in the analysis and visualization of multivariate datasets.
BACKGROUND: Learning transfer is defined as an individual's capability to apply prior learnt perceptual, motor, or conceptual skills to a novel task or performance environment. In the sport sciences, learning transfers have been investigated from an athlete-specific perspective. However, sport scientists should also consider the benefits of cross-disciplinary learning to aid critical thinking and metacognitive skill gained through the interaction with similar quantitative scientific disciplines. OBJECTIVE: Using team sports performance analysis as an example, this study aimed to demonstrate the utility of a common analytical technique in ecology in the sports sciences, namely, nonmetric multidimensional scaling. METHODS: To achieve this aim, three novel research examples using this technique are presented, each of which enables the analysis and visualization of athlete (organism), team (aggregation of organisms), and competition (ecosystem) behaviors. RESULTS: The first example reveals the technical behaviors of Australian Football League Brownlow medalists from the 2001 to 2016 seasons. The second example delineates dissimilarity in higher and lower ranked National Rugby League teams within the 2016 season. Lastly, the third example shows the evolution of game play in the basketball tournaments between the 2004 and 2016 Olympic Games. CONCLUSIONS: In addition to the novel findings of each example, the collective results demonstrate that, by embracing cross-disciplinary learning and drawing upon an analytical technique common to ecology, novel solutions to pertinent research questions within sports performance analysis could be addressed in a practically meaningful way. Cross-disciplinary learning may subsequently assist sport scientists in the analysis and visualization of multivariate datasets.
Authors: D Richard Cutler; Thomas C Edwards; Karen H Beard; Adele Cutler; Kyle T Hess; Jacob Gibson; Joshua J Lawler Journal: Ecology Date: 2007-11 Impact factor: 5.499