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Identification of movement categories and associated velocity thresholds for elite Gaelic football and hurling referees

Brady, Aidan J. orcid logoORCID: 0000-0002-9427-5771, Scriney, Michael orcid logoORCID: 0000-0001-6813-2630, Moyna, Niall orcid logoORCID: 0000-0003-1061-8528 and McCarren, Andrew orcid logoORCID: 0000-0002-7297-0984 (2021) Identification of movement categories and associated velocity thresholds for elite Gaelic football and hurling referees. International Journal of Performance Analysis in Sport, 21 (5). pp. 741-753. ISSN 2474-8668

Abstract
The purpose of this study was to generate movement category velocity thresholds for elite Gaelic football (GF) and hurling referees using a two-stage unsupervised clustering technique. Activity data from 41 GF and 38 hurling referees was collected using global positioning system technology during 338 and 221 competitive games, respectively. The elbow method was used in stage one to identify the number of movement categories in the datasets. In stage two, the respective velocity thresholds for each category were identified using spectral clustering. The efficacy of these thresholds was examined using a regression analysis performed between the median of each of the velocity thresholds and the raw velocity data. Five velocity thresholds were identified for both GF and hurling referees (mean ± standard deviation: GF referees; 0.70±0.09, 1.66±0.19, 3.28±0.41, 4.87±0.61, 6.49±0.50 m·s−1; hurling referees; 0.69±0.11, 1.60±0.25, 3.09±0.52, 4.63±0.58, 6.35±0.43 m·s−1). With the exception of the lowest velocity threshold, all other thresholds were significantly higher for GF referees. The newly generated velocity thresholds were more strongly associated with the raw velocity data than traditional generic categories. The provision of unique velocity thresholds will allow applied practitioners to better quantify the activity profile of elite GF and hurling referees during training and competition.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:GPS; data mining; zones; unsupervised learning; activity profile; team sport
Subjects:Computer Science > Machine learning
Medical Sciences > Performance
Medical Sciences > Physiology
Medical Sciences > Sports sciences
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:Routledge (Taylor & Francis)
Official URL:https://doi.org/10.1080/24748668.2021.1942659
Copyright Information:© 2021 Taylor & Francis
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Irish Research Council [EPSPG/2017/338]
ID Code:26324
Deposited On:12 Nov 2021 13:58 by Aidan Brady . Last Modified 15 Dec 2021 14:11
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