Predicting the outcome of CS:GO games using machine learning

Examensarbete för kandidatexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/256129
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Type: Examensarbete för kandidatexamen
Bachelor Thesis
Title: Predicting the outcome of CS:GO games using machine learning
Authors: Björklund, Arvid
Johansson Visuri, William
Lindevall, Fredrik
Svensson, Philip
Abstract: This 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.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/256129
Collection:Examensarbeten för kandidatexamen // Bachelor Theses



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