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Player optimal positioning analysis using FIFA video game data and classification models

bracu.type.groupResearch Publications
datacite.rightsMetadata Only
dc.contributor.authorTanvir, Sifat
dc.contributor.authorShakerin, Tasmia
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2026-07-14T09:24:57Z
dc.date.available2026-07-14T09:24:57Z
dc.date.issued2023-01-01
dc.description.abstractTeam formation is a crucial factor in any team's success in football. A player's performance varies on the position that they are playing at. To tap into the maximum potential of a player, finding t he appropriate position for that player is a must. The goal of this research is to determine and predict whether a player is actually playing in the most optimal position or not based on the skill-set, physique, and preference. The same concept can be applied in both video games and in real life. Eight datasets, each from one of the FIFA video games from year 2015 to 2020 have been used in this work. How reduction of certain categorical classes improved the accuracy has been discussed. Simultaneously, the reputation of players has also been predicted with satisfactory accuracy to analyze the quality of the datasets.
dc.description.versionPublished
dc.format.extent135-138
dc.identifier.doi10.1109/JCSSE58229.2023.10202045
dc.identifier.issn9798350300505
dc.identifier.other2-s2.0-85169296247
dc.identifier.urihttps://hdl.handle.net/10361/28540
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.hasversion10.1109/JCSSE58229.2023.10202045
dc.relation.ispartofProceedings of Jcsse 2023 20th International Joint Conference on Computer Science and Software Engineering
dc.relation.ispartofseriesProceedings of Jcsse 2023 20th International Joint Conference on Computer Science and Software Engineering
dc.relation.urihttps://ieeexplore.ieee.org/document/10202045
dc.rightsfalse
dc.subjectClass imbalance
dc.subjectDecision tree
dc.subjectFeature encoding
dc.subjectFeature selection
dc.subjectFIFA video game
dc.subjectKNN
dc.subjectNaïve bayes
dc.subjectRandom forest
dc.subject.lcshComputational intelligence.
dc.subject.lcshDecision trees.
dc.subject.lcshImage processing.
dc.subject.lcshTelevised soccer games.
dc.titlePlayer optimal positioning analysis using FIFA video game data and classification models
dc.typeConference Proceeding
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.identifier.scopus-author-id57222386558
person.identifier.scopus-author-id58556776500

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