Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76666
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJin, Seng Thung-
dc.contributor.authorJianhong, Gao-
dc.contributorResearch Division, China Institute of Sports Science, Chinaen_US
dc.contributorFaculty of Kinesiology, Shanghai University of Sport, China,en_US
dc.contributorResearch and Innovation Division, National Sports Institute of Malaysia, Malaysiaen_US
dc.contributorDepartment of Sport and Exercise Science, Tunku Abdul Rahman University College, Malaysiaen_US
dc.creatorLianyee, Kok-
dc.date.accessioned2022-11-01T02:39:28Z-
dc.date.available2022-11-01T02:39:28Z-
dc.date.issued2021-
dc.identifier.citationMovement, Health & Exercise (MoHE), vol.10(2), 2021, pages 121-127en_US
dc.identifier.issn2231-9409 (printed)-
dc.identifier.issn2289-9510 (online)-
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/76666-
dc.descriptionLink to publisher's homepage at https://www.mohejournal.org/aboutus.aspen_US
dc.description.abstractIntroduction: Vault kinematic variables have been found to be strongly correlated with vault difficulty (DV) values and judges’ scores. However, the Fédération Internationale de Gymnastique Code of Points (COP) was updated after every Olympic Games rendering previous regression models inadequate. Therefore, the objective of this study was to develop a prediction model for vault performance based on judges’ scores. Methods: Handspring vaults (n = 70) were recorded during the Men’s Artistic Gymnastic qualifying round of the 2017 China National Artistic Gymnastics Championship using a video camera placed 50 m perpendicular to the vault table. Kinematic data were coded and correlated with judges’ official competition final scores (FSs). The vault samples were used to develop a mathematical model (n = 65) and to verify the scores against the predicted model (n = 5). Partial least squares regression was established using the statistical software to calibrate and cross validate the model. Results: The goodness-of-fit of a 3-factor model was utilised (R2 cal = 90.13% and R2 val = 87.30%) and a significant and strong relationship was observed between predicted Y (FS) and reference Y (FS) in both the calibration and validation models (rcal = 0.949, rval = 0.932) with Y-calibration error (RMSEC = 0.1727) and Y-prediction error (RMSEP = 0.1990). Maximum height, 2nd-flight-time and DV were the key variables against FS. Using JSPM, 40% of new samples were within the acceptable range. Conclusion: Kinematic variables and known DV seem adequate to form a JSPM that could offer coaches an alternative scientific approach to monitor vault training.en_US
dc.language.isoenen_US
dc.publisherKementerian Pendidikan Tinggi (KPT), Malaysiaen_US
dc.subject.otherMultivariate regression analysisen_US
dc.subject.otherKinematic variablesen_US
dc.subject.otherDifficulty valueen_US
dc.subject.otherVault performanceen_US
dc.subject.otherGymnasticsen_US
dc.titleMultivariate regression modeling of Chinese artistic gymnastic handspring vaulting kinematic performance based on judges scoresen_US
dc.typeArticleen_US
dc.contributor.urlkokly@tarc.edu.myen_US
Appears in Collections:Movement, Health and Exercise (MoHE)

Files in This Item:
File Description SizeFormat 
Multivariate regression modeling of Chinese artistic.pdfMain article1.09 MBAdobe PDFView/Open


Items in UniMAP Library Digital Repository are protected by copyright, with all rights reserved, unless otherwise indicated.