DOTA 2 match prediction through deep learning team fight models

Cheng Hao Ke, Haozhang Deng, Congda Xu, Jiong Li, Xingyun Gu, Borchuluun Yadamsuren, Diego Klabjan, Rafet Sifa, Anders Drachen, Simon Demediuk*

*Corresponding author for this work

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

5 Scopus citations

Abstract

Esports are complex computer games that are played competitively. DOTA 2 is one of the most popular esports titles worldwide. Commentators, audiences, and players face tremendous challenges to keep up with events happening during live matches due to a rapidly evolving gameplay across a large virtual arena. This complexity leads to the question of whether esports analytics could detect important events and their subsequent impact on the match. One such important event is team fights, which can often determine the outcome of a match. Despite their significance across strategy, gameplay, and audience experience, team fights remain relatively unexplored in the literature. Their role and potential to support match prediction models are not well understood. This paper presents a novel definition of team fights in DOTA 2 and proposes an algorithm to extract and quantity them for use in match prediction.

Original languageEnglish (US)
Title of host publication2022 IEEE Conference on Games, CoG 2022
PublisherIEEE Computer Society
Pages96-103
Number of pages8
ISBN (Electronic)9781665459891
DOIs
StatePublished - 2022
Event2022 IEEE Conference on Games, CoG 2022 - Beijing, China
Duration: Aug 21 2022Aug 24 2022

Publication series

NameIEEE Conference on Computatonal Intelligence and Games, CIG
Volume2022-August
ISSN (Print)2325-4270
ISSN (Electronic)2325-4289

Conference

Conference2022 IEEE Conference on Games, CoG 2022
Country/TerritoryChina
CityBeijing
Period8/21/228/24/22

Keywords

  • DOTA 2
  • Deep Learning
  • Esports
  • Game Analytics
  • Prediction
  • Recurrent Neural Networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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