Anomaly Detection in Player Performances in Multiplayer Online Battle Arena Games

Xin Qian, Rafet Sifa, Xuefei Liu, Shreyashi Ganguly, Borchuluun Yadamsuren, Diego Klabjan, Anders Drachen, Simon Demediuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Esports are digital video games that are played professionally. In recent years there has been a growing need to improve the broadcast experience by incorporating real-time data-driven analytics. In these same games, when played by the general public, there is a growing issue of cheating. Using the popular esport and video game DOTA 2 as a case study, we present a novel application of Archetype Analysis that can be used for anomaly detection in player performance. We show how these anomalies can be utilised for both esports broadcasting and cheat detection.

Original languageEnglish (US)
Title of host publicationProceedings of 2022 Australasian Computer Science Week, ACSW 2022
PublisherAssociation for Computing Machinery
Pages23-30
Number of pages8
ISBN (Electronic)9781450396066
DOIs
StatePublished - Feb 14 2022
Event2022 Australasian Computer Science Week, ACSW 2022 - Virtual, Online, Australia
Duration: Feb 14 2022Feb 17 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2022 Australasian Computer Science Week, ACSW 2022
Country/TerritoryAustralia
CityVirtual, Online
Period2/14/222/17/22

Keywords

  • datasets
  • gaze detection
  • neural networks
  • text tagging

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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