research-article

Profiling Successful Team Behaviors in League of Legends

Authors:

Fernando Felix do Nascimento Junior,

Allan Sales da Costa Melo,

Igor Barbosa da Costa,

Leandro Balby MarinhoAuthors Info & Claims

WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web

Pages 261 - 268

https://doi.org/10.1145/3126858.3126886

Published: 17 October 2017 Publication History

Abstract

Despite the increasing popularity of electronic sports (eSports), there is still a scarcity of academic works exploring the playing behavior of teams. Understanding the features that help to discriminate between successful and unsuccessful teams would help teams improving their strategies, such as determine performance metrics to reach. In this paper, we identify and characterize team behavior patterns based on historical matches from the very popular eSpor League of Legends web API. By applying machine learning and statistical analysis, we clustered teams' performance and investigate for each cluster how and to what extent these features have an influence on teams' success and failure. Some clusters are more likely to have winning teams than others, the results of our study helped to discover the characteristics that are associated with this predisposition and allowed us to model performance metrics of successful and unsuccessful team profiles. At all, we found 7 profiles in which were categorized into four levels in terms of winning team proportion: very low, moderate, high and very high.

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Cited By

Costa LDrachen ASouza FXexéo G(2024)Artificial Intelligence in MOBA Games: A Multivocal Literature MappingIEEE Transactions on Games10.1109/TG.2023.328215716:2(250-269)Online publication date: Jun-2024
https://doi.org/10.1109/TG.2023.3282157
Bendell RWilliams JFiore SJentsch F(2024)Individual and team profiling to support theory of mind in artificial social intelligenceScientific Reports10.1038/s41598-024-63122-814:1Online publication date: 2-Jun-2024
https://doi.org/10.1038/s41598-024-63122-8
Melo SBicalho LCamacho de Oliveira Joia Lda Silva Junior JClua EPaes A(2023)Encoding feature set information in heterogeneous graph neural networks for game provenanceApplied Intelligence10.1007/s10489-023-04835-753:23(29024-29042)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-04835-7
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Index Terms

Profiling Successful Team Behaviors in League of Legends
1. Computing methodologies
  1. Machine learning
2. Information systems

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Published In

cover image ACM Other conferences

WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web

October 2017

522 pages

ISBN:9781450350969

DOI:10.1145/3126858

General Chairs:
Valter Roesler
UFRGS, Brazil
,
José Valdeni de Lima
UFRGS, Brazil
,
Program Chairs:
Celso Alberto Saibel Santos
UFES, Brazil
,
Roberto Willrich
UFSC, Brazil

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SBC: Brazilian Computer Society
CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
CGIBR: Comite Gestor da Internet no Brazil
CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2017

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Sponsor:

SBC
CNPq
CGIBR
CAPES

Webmedia '17: Brazilian Symposium on Multimedia and the Web

October 17 - 20, 2017

RS, Gramado, Brazil

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WebMedia '17 Paper Acceptance Rate 38 of 138 submissions, 28%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Costa LDrachen ASouza FXexéo G(2024)Artificial Intelligence in MOBA Games: A Multivocal Literature MappingIEEE Transactions on Games10.1109/TG.2023.328215716:2(250-269)Online publication date: Jun-2024
https://doi.org/10.1109/TG.2023.3282157
Bendell RWilliams JFiore SJentsch F(2024)Individual and team profiling to support theory of mind in artificial social intelligenceScientific Reports10.1038/s41598-024-63122-814:1Online publication date: 2-Jun-2024
https://doi.org/10.1038/s41598-024-63122-8
Melo SBicalho LCamacho de Oliveira Joia Lda Silva Junior JClua EPaes A(2023)Encoding feature set information in heterogeneous graph neural networks for game provenanceApplied Intelligence10.1007/s10489-023-04835-753:23(29024-29042)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-04835-7
Brown DBischof J(2023)Learning Movement Patterns for Improving the Skills of Beginner Level Players in Competitive MOBAsInventive Systems and Control10.1007/978-981-99-1624-5_45(613-624)Online publication date: 15-Jun-2023
https://doi.org/10.1007/978-981-99-1624-5_45
Bandeira ICastanho Ce Silva TSarmet MJacobi R(2023)Applicability of Psychophysiological and Perception Data for Mapping Strategies in League of Legends – An Exploratory StudyHCI in Games10.1007/978-3-031-35979-8_10(125-140)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35979-8_10
Liu HGuo QZhu HZhuang FYang SDou DXiong H(2022)Who will Win the Data Science Competition? Insights from KDD Cup 2019 and BeyondACM Transactions on Knowledge Discovery from Data10.1145/351189616:5(1-24)Online publication date: 5-Apr-2022
https://dl.acm.org/doi/10.1145/3511896
Mondal JZahin AManab MZahidul Hasan M(2022)Does A Support Role Player really Create Difference towards Triumph? Analyzing Individual Performances of Specific Role Players to Predict Victory in League of Legends2022 25th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT57492.2022.10055689(768-773)Online publication date: 17-Dec-2022
https://doi.org/10.1109/ICCIT57492.2022.10055689
Jung CKim H(2022)Win Prediction from the Snowball Effect Perspectives2022 IEEE Games, Entertainment, Media Conference (GEM)10.1109/GEM56474.2022.10017891(1-6)Online publication date: 27-Nov-2022
https://doi.org/10.1109/GEM56474.2022.10017891
Horst RLanvers Mvon Kacsoh LDörner R(2022)MOBA Coach: Exploring and Analyzing Multiplayer Online Battle Arena DataAdvances in Visual Computing10.1007/978-3-030-90439-5_16(197-209)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-90439-5_16
Silva ACorreia MSilva H(2021)Invisible ECG for High Throughput Screening in eSportsSensors10.3390/s2122760121:22(7601)Online publication date: 16-Nov-2021
https://doi.org/10.3390/s21227601
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