Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/112364
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dc.contributorSchool of Design-
dc.creatorLiu, M-
dc.creatorKong, A-
dc.creatorLau, N-
dc.creatorFeng, Z-
dc.creatorLiu, X-
dc.date.accessioned2025-04-09T00:51:43Z-
dc.date.available2025-04-09T00:51:43Z-
dc.identifier.urihttp://hdl.handle.net/10397/112364-
dc.language.isoenen_US
dc.publisherFrontiers Research Foundationen_US
dc.rights© 2024 Liu, Kong, Lau, Feng and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rightsThe following publication Liu M, Kong A, Lau N, Feng Z and Liu X (2024) Basketball self-evaluation matrix: discrepancy between self-confidence and decision-making performance on psychological profiling of players. Front. Sports Act. Living 6:1404701 is available at https://doi.org/10.3389/fspor.2024.1404701.en_US
dc.subjectDecision-makingen_US
dc.subjectPsychological profilingen_US
dc.subjectSelf-confidenceen_US
dc.subjectSelf-evaluationen_US
dc.subjectYouth basketball playersen_US
dc.titleBasketball self-evaluation matrix : discrepancy between self-confidence and decision-making performance on psychological profiling of playersen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume6-
dc.identifier.doi10.3389/fspor.2024.1404701-
dcterms.abstractBackground: In basketball training, self-evaluation plays a crucial role in the decision-making and execution of movements of players. The self-evaluation of players is influenced by their perception of own basketball ability and self-confidence state. This study aimed to explore potential discrepancies between self-confidence levels of players and their decision-making performance, while also characterizing different types of players.-
dcterms.abstractMethod: Data was collected from 20 youth basketball players who participated in a decision-making video task and a self-confidence assessment. Based on data from their self-confidence and decision-making awareness assessment, the K-means cluster analysis was used to categorize the players into different groups. Then, ANOVA and post hoc Scheffe test were conducted to compare these clusters.-
dcterms.abstractResults: The cluster analysis identified four distinct profiles of players and the results of the ANOVA and post hoc Scheffe tests revealed significant differences between the four clusters. The “High Self-confidence & Low Decision-making Awareness” players might display an overconfident mindset, while the “High Self-confidence & High Decision-making Awareness” players potentially demonstrated the better performance and maintained a consistent and confident attitude. The “Low Self-confidence & High Decision-making Awareness” players appeared to lack confidence and needed to foster greater faith in their abilities. Finally, the “Low Self-confidence & Low Decision-making Awareness” players required a long-term and comprehensive training program to improve their skills.-
dcterms.abstractDiscussion: These preliminary findings informed the development of a self-evaluation matrix, designed to help coaches better understand player profiles and design tailored interventions. Moreover, this study contributes on sport calibration and enhances understanding of the behavioral and psychological states of players.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationFrontiers in sports and active living, 2024, v. 6, 1404701-
dcterms.isPartOfFrontiers in sports and active living-
dcterms.issued2024-
dc.identifier.scopus2-s2.0-85206971094-
dc.identifier.eissn2624-9367-
dc.identifier.artn1404701-
dc.description.validate202504 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextHong Kong Polytechnic University; Laboratory for Artificial Intelligence in Design under the InnoHK Research Clusters, Government of the Hong Kong Special Administrative Regionen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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