Transferring an analytical technique from ecology to the sport sciences

Woods, Carl, Robertson, Sam, Collier, Neil, Swinbourne, Anne, and Leicht, Anthony (2018) Transferring an analytical technique from ecology to the sport sciences. Sports Medicine, 48 (3). pp. 725-732.

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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 to the sports sciences; namely, non-metric multidimensional scaling. Methods: To achieve this aim, three novel research examples using this technique are presented, each of which enables the analysis and visualisation of athlete (organism), team (aggregation of organisms) and competition (ecosystem) behaviours.

Results: The first example reveals the technical behaviours of Australian Football League Brownlow medallists 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 to 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 visualisation of multivariate datasets.

Item ID: 49876
Item Type: Article (Research - C1)
ISSN: 1179-2035
Keywords: transfer of learning; cross-disciplinary learning; sports performance analysis; data visualisation
Date Deposited: 21 Aug 2017 05:34
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1106 Human Movement and Sports Science > 110699 Human Movement and Sports Science not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970106 Expanding Knowledge in the Biological Sciences @ 100%
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