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Esports Analytics Through Encounter Detection

Schubert, Matthias; Drachen, Anders and Mahlmann, Tobias LU (2016) MIT Sloan Sports Analytics Conference In Proceedings of the MIT Sloan Sports Analytics Conference 2016
Abstract
Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these es, teams of players fight against each other in enclosed arena environs, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally,metrics for evaluating team performance during match runtime are proposed.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
esports, dota2, machine learning, team encounter
in
Proceedings of the MIT Sloan Sports Analytics Conference 2016
pages
18 pages
publisher
MIT Sloan
conference name
MIT Sloan Sports Analytics Conference
language
English
LU publication?
yes
id
37670941-2b22-4b72-84fc-7022700057df (old id 8569749)
alternative location
http://www.sloansportsconference.com/?p=17536
date added to LUP
2016-01-28 10:43:50
date last changed
2016-04-16 07:57:15
@inproceedings{37670941-2b22-4b72-84fc-7022700057df,
  abstract     = {Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these es, teams of players fight against each other in enclosed arena environs, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally,metrics for evaluating team performance during match runtime are proposed.},
  author       = {Schubert, Matthias and Drachen, Anders and Mahlmann, Tobias},
  booktitle    = {Proceedings of the MIT Sloan Sports Analytics Conference 2016},
  keyword      = {esports,dota2,machine learning,team encounter},
  language     = {eng},
  pages        = {18},
  publisher    = {MIT Sloan},
  title        = {Esports Analytics Through Encounter Detection},
  year         = {2016},
}