Predicting the outcome of CS:GO games using machine learning

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dc.contributor.authorBjörklund, Arvid
dc.contributor.authorJohansson Visuri, William
dc.contributor.authorLindevall, Fredrik
dc.contributor.authorSvensson, Philip
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:55:02Z
dc.date.available2019-07-03T14:55:02Z
dc.date.issued2018
dc.description.abstractThis work analyzes the possibility of predicting the result of a Counter Strike: Global Offensive (CS:GO) match using machine learning. Demo files from 6000 CS:GO games of the top 1000 ranked players in the EU region were downloaded from FACEIT.com and analyzed using an open source library to parse CS:GO demo files. Players from the matches were then clustered, using the kmeans algorithm, based on their style of play. To achieve stable clusters and remove the influence of individual win rate on the clusters, a genetic algorithm was implemented to weight each feature before the clustering. For the final part a neural network was trained to predict the outcome of a CS:GO match by analyzing the combination of players in each team. The results show that it is indeed possible to predict the outcome of CS:GO matches by analyzing the team compositions. The results also show a clear correlation between the number of clusters and the prediction accuracy.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256129
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titlePredicting the outcome of CS:GO games using machine learning
dc.type.degreeExamensarbete för kandidatexamensv
dc.type.degreeBachelor Thesisen
dc.type.uppsokM2
local.programmeSoftware Engineering (300 hp)
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