The Validity of Automated Tackle Detection in Women's Rugby League.
Cummins, C, Charlton, G, Naughton, M, Jones, B, Minahan, C, and Murphy, A. The validity of automated tackle detection in women's rugby league. J Strength Cond Res 36(7): 1951–1955, 2022—This study assessed the validity of microtechnology devices to automatically detect and differentiate tackles in e...
| Publicado en: | Journal of Strength & Conditioning Research Vol. 36; no. 7; pp. 1951 - 1956 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jul2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157582936&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157582936 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10648011 JSC jtl: Journal of Strength & Conditioning Research issn: 10648011 maglogo: N pubinfo: dt: Jul2022 vid: 36 iid: 7 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 157582936 157582936 157582936 10.1519/JSC.0000000000003745 157582936 ppf: 1951 ppct: 5 formats: tig: atl: The Validity of Automated Tackle Detection in Women's Rugby League. aug: au: Cummins, Cloe Charlton, Glen Naughton, Mitchell Jones, Ben Minahan, Clare Murphy, Aron affil: School of Science and Technology, University of New England, Armidale, NSW, Australia sug: subj: Athletes, Female Sporting Events Rugby Automation Equipment and Supplies Equipment Reliability Human Female Athletes, Elite Algorithms False Positive Results False Negative Results Sensitivity and Specificity Descriptive Statistics Female ab: Cummins, C, Charlton, G, Naughton, M, Jones, B, Minahan, C, and Murphy, A. The validity of automated tackle detection in women's rugby league. J Strength Cond Res 36(7): 1951–1955, 2022—This study assessed the validity of microtechnology devices to automatically detect and differentiate tackles in elite women's rugby league match-play. Elite female players (n = 17) wore a microtechnology device (OptimEye S5 device; Catapult Group International) during a representative match, which involved a total of 512 tackles of which 365 were defensive and 147 were attacking. Tackles automatically detected by Catapult's tackle detection algorithm and video-coded tackles were time synchronized. True positive, false negative and false positive events were utilized to calculate sensitivity (i.e., when a tackle occurred, did the algorithm correctly detect this event) and precision (i.e., when the algorithm reported a tackle, was this a true event based on video-coding). Of the 512 video-derived attacking and defensive tackle events, the algorithm was able to detect 389 tackles. The algorithm also produced 81 false positives and 123 false negatives. As such when a tackle occurred, the algorithm correctly identified 76.0% of these events. When the algorithm reported that a tackle occurred, this was an actual event in 82.8% of circumstances. Across all players, the algorithm was more sensitive to the detection of an attacking event (sensitivity: 78.2%) as opposed to a defensive event (sensitivity: 75.1%). The sensitivity and precision of the algorithm was higher for forwards (sensitivity: 81.8%; precision: 92.1%) when compared with backs (sensitivity: 64.5%; precision: 66.1%). Given that understanding the tackle demands of rugby league is imperative from both an injury-prevention and physical-conditioning perspective there is an opportunity to develop a specific algorithm for the detection of tackles within women's rugby league. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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