What Are You Looking At? Team Fight Prediction Through Player Camera

Marko Tot, Michelangelo Conserva, Alan Pedrassoli Chitayat, Athanasios Kokkinakis, Sagarika Patra, Simon Demediuk, Alvaro Caceres Munoz, Oluseji Olarewaju, Marian Ursu, Ben Kirmann, Jonathan Hook, Florian Block, Anders Drachen, Diego Perez-Liebana

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


Esport is a large and still growing industry with vast audiences. Multiplayer Online Battle Arenas (MOBAs), a sub-genre of esports, possess a very complex environment, which often leads to experts missing important coverage while broadcasting live competitions. One common game event that holds significant importance for broadcasting is referred to as a team fight engagement. Professional player's own knowledge and understanding of the game may provide a solution to this problem. This paper suggests a model that predicts and detects ongoing team fights in a live scenario. This approach outlines a novel technique of deriving representations of a complex game environment by relying on player knowledge. This is done by analysing the positions of the in-game characters and their associated cameras, utilising this data to train a neural network. The proposed model is able to both assist in the production of live esport coverage as well as provide a live, expert-derived, analysis of the game without the need of relying on outside sources.

Original languageEnglish
Title of host publication2021 IEEE Conference on Games, CoG 2021
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9781665438865
Publication statusPublished - 7 Dec 2021
Event2021 IEEE Conference on Games, CoG 2021 - Copenhagen, Denmark
Duration: 17 Aug 202120 Aug 2021

Publication series

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


Conference2021 IEEE Conference on Games, CoG 2021

Bibliographical note

Funding Information:
This paper is part-funded by EPSRC CDT in Intelligent Games and Game Intelligence (lOGI) EP/LOl5846/1 and the Audience of the Future program by UK Research and Innovation through the Industrial Strategy Challenge Fund (grant no.I04775) as part of the Weavr project (weavr.tv).

Publisher Copyright:
© 2021 IEEE.


  • engagement
  • esports
  • MOBA
  • neural network
  • player analytics
  • team fight

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