ReviewSports medicine (Auckland, N.Z.)2018
Transferring an Analytical Technique from Ecology to the Sport Sciences.
Review in Sports medicine (Auckland, N.Z.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Operational Insights into Analysing Team and Player Performance in Elite Rugby League: A Narrative Review with Case Examples.Sports medicine - open · 2022Review
- Effect of a Seeding System on Competitive Performance of Elite Players During Major Tennis Tournaments.Frontiers in psychology · 2020Article
- Comparing subjective and objective evaluations of player performance in Australian Rules football.PloS one · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLearning 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.
objectiveUsing 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.
methodsTo 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.
resultsThe 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.
conclusionsIn 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.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.