Fuzzy regression model of goal difference of the Korean National Football Team based on ELO rating and dividend
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Woo-Joo | - |
dc.contributor.author | Jhang, Hyo-Jin | - |
dc.contributor.author | Choi, Seung Hoe | - |
dc.date.accessioned | 2023-08-16T07:43:59Z | - |
dc.date.available | 2023-08-16T07:43:59Z | - |
dc.date.issued | 2020-04 | - |
dc.identifier.issn | 1064-1246 | - |
dc.identifier.issn | 1875-8967 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114188 | - |
dc.description.abstract | Soccer recently has become an intellectual game by predicting the outcome of games. In this study, we use path analysis to find the variable that affects the outcome of the Korea National Football Team (KNFT) matches the most, and consequently the odds of victory, defeat, or a draw, as announced by the betting company. We will also investigate the influence of the variables inferred from the path analysis and Korea's ELO Rating on the difference between scoring and losing points of the KNFT. We will represent the dividend and the difference between scoring and losing points as fuzzy numbers using the fuzzy decomposition, and then infer the fuzzy regression model for the result of the KNFT's match. For this purpose, we use data on 113 games of the KNFT from September 2011 to June 2019 and the dividend rate of the KNFT obtained from Wise Toto company. | - |
dc.format.extent | 7 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IOS Press | - |
dc.title | Fuzzy regression model of goal difference of the Korean National Football Team based on ELO rating and dividend | - |
dc.type | Article | - |
dc.publisher.location | 네델란드 | - |
dc.identifier.doi | 10.3233/JIFS-191288 | - |
dc.identifier.wosid | 000534641700086 | - |
dc.identifier.bibliographicCitation | Journal of Intelligent and Fuzzy Systems, v.38, no.4, pp 4537 - 4543 | - |
dc.citation.title | Journal of Intelligent and Fuzzy Systems | - |
dc.citation.volume | 38 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 4537 | - |
dc.citation.endPage | 4543 | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | ODDS | - |
dc.subject.keywordAuthor | ELO rating | - |
dc.subject.keywordAuthor | dividend | - |
dc.subject.keywordAuthor | path analysis | - |
dc.subject.keywordAuthor | fuzzy partition | - |
dc.subject.keywordAuthor | regression analysis | - |
dc.identifier.url | https://eds.s.ebscohost.com/eds/pdfviewer/pdfviewer?vid=1&sid=c685f7dc-79b9-4560-9d24-5bcefc6822f8%40redis | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.