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Predicting Winning Team and Probabilistic Ratings in “Dota 2” and “Counter-Strike: Global Offensive” Video Games

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Analysis of Images, Social Networks and Texts (AIST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10716))

Abstract

In this paper, we present novel winning team predicting models and compare the accuracy of the obtained prediction with TrueSkill model of ranking individual players impact based on their impact in team victory for the two most popular online games: “Dota 2” and “Counter-Strike: Global Offensive”. In both cases, we present game analytics for predicting winning team based on game statistics and TrueSkill.

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Acknowledgments

The work was supported by the Russian Science Foundation under grant 17-11-01294 and performed at National Research University Higher School of Economics, Russia. We would like to thank Alexander Semenov and Petr Romov for their piece of advice.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Ilya Makarov, Dmitry Savostyanov, Boris Litvyakov & Dmitry I. Ignatov

Authors
  1. Ilya Makarov
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  2. Dmitry Savostyanov
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  3. Boris Litvyakov
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  4. Dmitry I. Ignatov
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Corresponding author

Correspondence to Ilya Makarov .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Wil M.P. van der Aalst

  2. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  3. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  4. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  5. Skolkovo Institute of Science and Technology, Moscow, Russia

    Victor Lempitsky

  6. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  7. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  8. LORIA, Campus Scientifique, Vandœuvre lès Nancy, France

    Amedeo Napoli

  9. University of Hamburg, Hamburg, Germany

    Alexander Panchenko

  10. University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  11. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  12. Indiana University, Bloomington, Indiana, USA

    Stanley Wasserman

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Makarov, I., Savostyanov, D., Litvyakov, B., Ignatov, D.I. (2018). Predicting Winning Team and Probabilistic Ratings in “Dota 2” and “Counter-Strike: Global Offensive” Video Games. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2017. Lecture Notes in Computer Science(), vol 10716. Springer, Cham. https://doi.org/10.1007/978-3-319-73013-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-73013-4_17

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