Diving into a pool of data: Using principal component analysis to optimize performance prediction in women's short-course swimming.
This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women's freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including star...
| Publicado en: | Journal of Sports Sciences Vol. 42; no. 6; pp. 519 - 527 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Mar2024
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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=177218101&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177218101 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02640414 5BV jtl: Journal of Sports Sciences issn: 02640414 maglogo: Y pubinfo: dt: Mar2024 vid: 42 iid: 6 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 177218101 177025199 177218101 177218101 10.1080/02640414.2024.2346670 177218101 ppf: 519 ppct: 8 formats: tig: atl: Diving into a pool of data: Using principal component analysis to optimize performance prediction in women's short-course swimming. aug: au: Staunton, Craig A. Romann, Michael Björklund, Glenn Born, Dennis-Peter affil: Swedish Winter Sports Research Centre, Department of Health Sciences, Mid Sweden University, Östersund, Sweden sug: subj: Factor Analysis Swimming Time Factors Prediction Models Task Performance and Analysis Human Female Multiple Linear Regression Descriptive Statistics Confidence Intervals Data Analysis Probability Female ab: This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women's freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA −7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs −0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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