Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/67814
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Vilela, Tiago | por |
dc.contributor.author | Portela, Filipe | por |
dc.contributor.author | Santos, Manuel | por |
dc.date.accessioned | 2020-10-28T15:11:54Z | - |
dc.date.issued | 2018 | - |
dc.identifier.isbn | 978-3-319-77699-6 | - |
dc.identifier.issn | 2194-5357 | - |
dc.identifier.uri | https://hdl.handle.net/1822/67814 | - |
dc.description.abstract | Football, which is a popular world-wide sport, has become one of the most practiced sports but also, with more study cases. Scouting and game analysis that is currently made has offered the possibility to improve the competition and increase the performance levels within a team. Taking this into account it emerged the term Scouting. The objective of this study is to streamline the Scouting process in Football, through Data Mining (DM) techniques and following the Cross Industry Standard Process for Data Mining (CRIPS-DM) methodology. The goal of DM was to develop and evaluate predictive models capable of forecasting a score of a football player’s performance. Based on this target, 2808 classification models and 936 regression models were developed and evaluated. For the classification, the maximum accuracy percentage was centered at 94% for the Forward player position, while for the regression the minimum error value was 0.07 for the Forward position. The results obtained allow to streamline the Scouting process in Football thus enhancing the sporting advantage. | por |
dc.description.sponsorship | This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT –Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013 | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147280/PT | - |
dc.rights | restrictedAccess | por |
dc.subject | Data mining | por |
dc.subject | Football | por |
dc.subject | Knowledge discovery in databases | por |
dc.subject | Scouting | por |
dc.title | Towards a pervasive intelligent system on football scouting - a data mining study case | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-319-77700-9_34 | por |
oaire.citationStartPage | 341 | por |
oaire.citationEndPage | 351 | por |
oaire.citationVolume | 747 | por |
dc.date.updated | 2020-10-28T11:56:17Z | - |
dc.identifier.doi | 10.1007/978-3-319-77700-9_34 | por |
dc.date.embargo | 10000-01-01 | - |
sdum.export.identifier | 7406 | - |
sdum.journal | Advances in Intelligent Systems and Computing | por |
sdum.conferencePublication | WorldCIST'18: World Conference on Information Systems and Technologies | por |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
2018 - PIS - Towards a Pervasive Intelligent System on Football Scouting - A Data Mining Study Case.pdf Acesso restrito! | 335,31 kB | Adobe PDF | Ver/Abrir |