Change search
ReferencesLink to record
Permanent link

Direct link
Result Prediction by Mining Replays in Dota 2
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2015 (English)Student thesis
Abstract [en]

Context: Real-time games like Dota 2 lack the extensive mathematical modeling of turn-based games that can be used to make objective statements about how to best play them. Understanding a real-time computer game through the same kind of modeling as a turn-based game is practically impossible. Objectives: In this thesis an attempt was made to create a model using machine learning that can predict the winning team of a Dota 2 game given partial data collected as the game progressed. A couple of different classifiers were tested, out of these Random Forest was chosen to be studied more in depth. Methods: A method was devised for retrieving Dota 2 replays and parsing them into a format that can be used to train classifier models. An experiment was conducted comparing the accuracy of several machine learning algorithms with the Random Forest algorithm on predicting the outcome of Dota 2 games. A further experiment comparing the average accuracy of 25 Random Forest models using different settings for the number of trees and attributes was conducted. Results: Random Forest had the highest accuracy of the different algorithms with the best parameter setting having an average of 88.83% accuracy, with a 82.23% accuracy at the five minute point. Conclusions: Given the results, it was concluded that partial game-state data can be used to accurately predict the results of an ongoing game of Dota 2 in real-time with the application of machine learning techniques.

Place, publisher, year, edition, pages
2015. , 29 p.
Keyword [en]
Dota 2, Machine Learning, Random Forest, Result Prediction
National Category
Computer Science
URN: urn:nbn:se:bth-2288Local ID: diva2:829556
Educational program
PAACI Master of Science in Game and Software Engineering
Available from: 2015-04-22 Created: 2015-03-10 Last updated: 2016-02-22Bibliographically approved

Open Access in DiVA

fulltext(2400 kB)268 downloads
File information
File name FULLTEXT01.pdfFile size 2400 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science and Engineering
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 268 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 246 hits
ReferencesLink to record
Permanent link

Direct link