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Performance of Machine Learning Algorithms in Predicting Game Outcome from Drafts in Dota 2

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 661)

Abstract

In this paper we suggest the first systematic review and compare performance of most frequently used machine learning algorithms for prediction of the match winner from the teams’ drafts in Dota 2 computer game. Although previous research attempted this task with simple models, weve made several improvements in our approach aiming to take into account interactions among heroes in the draft. For that purpose we’ve tested the following machine learning algorithms: Naive Bayes classifier, Logistic Regression and Gradient Boosted Decision Trees. We also introduced Factorization Machines for that task and got our best results from them. Besides that, we found that model’s prediction accuracy depends on skill level of the players. We’ve prepared publicly available dataset which takes into account shortcomings of data used in previous research and can be used further for algorithms development, testing and benchmarking.

Keywords

Online games Predictive models Dota 2 Factorization machines MOBA 

Notes

Acknowledgements

This paper was prepared within the framework of a subsidy granted to HSE by the Government of Russian Federation for implementation of the Global Competitiveness Program.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.International Laboratory for Applied Network ResearchNational Research University Higher School of EconomicsMoscowRussia
  2. 2.Yandex Data FactoryMoscowRussia
  3. 3.Moscow Institute of Physics and TechnologyMoscowRussia
  4. 4.National Research University Higher School of EconomicsMoscowRussia
  5. 5.Institute for Information Transmission ProblemsMoscowRussia

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