Player optimal positioning analysis using FIFA video game data and classification models
| bracu.type.group | Research Publications | |
| datacite.rights | Metadata Only | |
| dc.contributor.author | Tanvir, Sifat | |
| dc.contributor.author | Shakerin, Tasmia | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2026-07-14T09:24:57Z | |
| dc.date.available | 2026-07-14T09:24:57Z | |
| dc.date.issued | 2023-01-01 | |
| dc.description.abstract | Team 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.version | Published | |
| dc.format.extent | 135-138 | |
| dc.identifier.doi | 10.1109/JCSSE58229.2023.10202045 | |
| dc.identifier.issn | 9798350300505 | |
| dc.identifier.other | 2-s2.0-85169296247 | |
| dc.identifier.uri | https://hdl.handle.net/10361/28540 | |
| dc.language.iso | en_US | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.hasversion | 10.1109/JCSSE58229.2023.10202045 | |
| dc.relation.ispartof | Proceedings of Jcsse 2023 20th International Joint Conference on Computer Science and Software Engineering | |
| dc.relation.ispartofseries | Proceedings of Jcsse 2023 20th International Joint Conference on Computer Science and Software Engineering | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/10202045 | |
| dc.rights | false | |
| dc.subject | Class imbalance | |
| dc.subject | Decision tree | |
| dc.subject | Feature encoding | |
| dc.subject | Feature selection | |
| dc.subject | FIFA video game | |
| dc.subject | KNN | |
| dc.subject | Naïve bayes | |
| dc.subject | Random forest | |
| dc.subject.lcsh | Computational intelligence. | |
| dc.subject.lcsh | Decision trees. | |
| dc.subject.lcsh | Image processing. | |
| dc.subject.lcsh | Televised soccer games. | |
| dc.title | Player optimal positioning analysis using FIFA video game data and classification models | |
| dc.type | Conference Proceeding | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.identifier.scopus-author-id | 57222386558 | |
| person.identifier.scopus-author-id | 58556776500 |