Predição de Vencedores em Jogos MOBA

Detalhes bibliográficos
Autor(a) principal: Almeida, Carlos E. M. [UNESP]
Data de Publicação: 2017
Outros Autores: Correia, Ronaldo C. M. [UNESP], Eler, Danilo M. [UNESP], Olivete-Jr, Celso [UNESP], Garci, Rogerio E. [UNESP], Scabora, Lucas C., Spadon, Gabriel
Tipo de documento: Artigo de conferência
Idioma: por
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.23919/CISTI.2017.7975774
http://hdl.handle.net/11449/175026
Resumo: Multiplayer Online Battle Arena (MOBA) games are very popular in the current eSport scenario, being highlighted in several competitions around the world. However, the domain of knowledge contained in these games is large, which makes it difficult to discover and predict the course of a match. The present work proposes the application of classification algorithms to determine the team with more chances to win a match. Two classifications procedures were used, one based on the composition of heroes in each team and another considering the duration of the match. The experiments were performed on data collected from 123,326 matches of Dota 2, showing that it was possible to achieve approximately 77% accuracy. The results demonstrate the effectiveness of the application when using techniques assisted by computers, and when using the methodology described in championships or other similar games that require the definition of strategies.
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spelling Predição de Vencedores em Jogos MOBAPrediction of winners in MOBA gamesClassificationData MiningStrategic GamesMultiplayer Online Battle Arena (MOBA) games are very popular in the current eSport scenario, being highlighted in several competitions around the world. However, the domain of knowledge contained in these games is large, which makes it difficult to discover and predict the course of a match. The present work proposes the application of classification algorithms to determine the team with more chances to win a match. Two classifications procedures were used, one based on the composition of heroes in each team and another considering the duration of the match. The experiments were performed on data collected from 123,326 matches of Dota 2, showing that it was possible to achieve approximately 77% accuracy. The results demonstrate the effectiveness of the application when using techniques assisted by computers, and when using the methodology described in championships or other similar games that require the definition of strategies.Departamento de Matemática e Computação (DMC) Universidade Estadual Paulista (FCT/UNESP) Presidente PrudenteDepartamento de Ciências de Computação (SCC) Instituto de Ciências Matemáticas e de Computação (ICMC) Universidade de São Paulo (USP)Departamento de Matemática e Computação (DMC) Universidade Estadual Paulista (FCT/UNESP) Presidente PrudenteUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Almeida, Carlos E. M. [UNESP]Correia, Ronaldo C. M. [UNESP]Eler, Danilo M. [UNESP]Olivete-Jr, Celso [UNESP]Garci, Rogerio E. [UNESP]Scabora, Lucas C.Spadon, Gabriel2018-12-11T17:13:54Z2018-12-11T17:13:54Z2017-07-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.23919/CISTI.2017.7975774Iberian Conference on Information Systems and Technologies, CISTI.2166-07352166-0727http://hdl.handle.net/11449/17502610.23919/CISTI.2017.79757742-s2.0-850270789582616135175972629Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporIberian Conference on Information Systems and Technologies, CISTI0,136info:eu-repo/semantics/openAccess2024-06-19T14:32:26Zoai:repositorio.unesp.br:11449/175026Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:48:50.901072Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Predição de Vencedores em Jogos MOBA
Prediction of winners in MOBA games
title Predição de Vencedores em Jogos MOBA
spellingShingle Predição de Vencedores em Jogos MOBA
Almeida, Carlos E. M. [UNESP]
Classification
Data Mining
Strategic Games
title_short Predição de Vencedores em Jogos MOBA
title_full Predição de Vencedores em Jogos MOBA
title_fullStr Predição de Vencedores em Jogos MOBA
title_full_unstemmed Predição de Vencedores em Jogos MOBA
title_sort Predição de Vencedores em Jogos MOBA
author Almeida, Carlos E. M. [UNESP]
author_facet Almeida, Carlos E. M. [UNESP]
Correia, Ronaldo C. M. [UNESP]
Eler, Danilo M. [UNESP]
Olivete-Jr, Celso [UNESP]
Garci, Rogerio E. [UNESP]
Scabora, Lucas C.
Spadon, Gabriel
author_role author
author2 Correia, Ronaldo C. M. [UNESP]
Eler, Danilo M. [UNESP]
Olivete-Jr, Celso [UNESP]
Garci, Rogerio E. [UNESP]
Scabora, Lucas C.
Spadon, Gabriel
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Almeida, Carlos E. M. [UNESP]
Correia, Ronaldo C. M. [UNESP]
Eler, Danilo M. [UNESP]
Olivete-Jr, Celso [UNESP]
Garci, Rogerio E. [UNESP]
Scabora, Lucas C.
Spadon, Gabriel
dc.subject.por.fl_str_mv Classification
Data Mining
Strategic Games
topic Classification
Data Mining
Strategic Games
description Multiplayer Online Battle Arena (MOBA) games are very popular in the current eSport scenario, being highlighted in several competitions around the world. However, the domain of knowledge contained in these games is large, which makes it difficult to discover and predict the course of a match. The present work proposes the application of classification algorithms to determine the team with more chances to win a match. Two classifications procedures were used, one based on the composition of heroes in each team and another considering the duration of the match. The experiments were performed on data collected from 123,326 matches of Dota 2, showing that it was possible to achieve approximately 77% accuracy. The results demonstrate the effectiveness of the application when using techniques assisted by computers, and when using the methodology described in championships or other similar games that require the definition of strategies.
publishDate 2017
dc.date.none.fl_str_mv 2017-07-11
2018-12-11T17:13:54Z
2018-12-11T17:13:54Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.23919/CISTI.2017.7975774
Iberian Conference on Information Systems and Technologies, CISTI.
2166-0735
2166-0727
http://hdl.handle.net/11449/175026
10.23919/CISTI.2017.7975774
2-s2.0-85027078958
2616135175972629
url http://dx.doi.org/10.23919/CISTI.2017.7975774
http://hdl.handle.net/11449/175026
identifier_str_mv Iberian Conference on Information Systems and Technologies, CISTI.
2166-0735
2166-0727
10.23919/CISTI.2017.7975774
2-s2.0-85027078958
2616135175972629
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv Iberian Conference on Information Systems and Technologies, CISTI
0,136
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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