Player Performance Evaluation in Team-Based First-Person Shooter eSport

  • David Bednárek
  • Martin Kruliš
  • Jakub Yaghob
  • Filip ZavoralEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 814)


Electronic sports or pro gaming have become very popular in this millenium and the increased value of this new industry is attracting investors with various interests. One of these interest is game betting, which requires player and team rating, game result predictions, and fraud detection techniques. This paper discusses several aspects of analysis of game recordings in Counter-Strike: Global Offensive game including decoding the game recordings, matching of different sources of player data, quantifying player performance, and evaluation of economical aspects of the game.


E-sport Data analysis Data integration Data quality Player rating 



This work was supported by project PROGRES Q48.


  1. 1.
    Bednarek, D., Krulis, M., Yaghob, J., Zavoral, F.: Data preprocessing of eSport game records - counter-strike: Global offensive. In: Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, pp. 269–276. INSTICC, SciTePress (2017)Google Scholar
  2. 2.
    Breu, L.: Online-games: Traffic analysis of popular game servers (counter strike: Source) (2007)Google Scholar
  3. 3.
    Christen, P.: Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, Heidelberg (2012). Scholar
  4. 4.
    Hamerly, G., Elkan, C.: Alternatives to the k-means algorithm that find better clusterings. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, CIKM 2002, pp. 600–607. ACM, New York (2002).
  5. 5.
    Hirschberg, D.S.: A linear space algorithm for computing maximal common subsequences. Commun. ACM 18(6), 341–343 (1975)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Jain, P., Kumaraguru, P., Joshi, A.: @i seek ‘’: Identifying users across multiple online social networks. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1259–1268. WWW 2013 Companion. ACM, New York (2013).
  7. 7.
    Jamjuntra, L., Chartsuwan, P., Wonglimsamut, P., Porkaew, K., Supasitthimethee, U.: Social network user identification. In: 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 132–137, February 2017Google Scholar
  8. 8.
    Kuhn, H.W.: The hungarian method for the assignment problem. Nav. Res. Logistics Q. 2(1–2), 83–97 (1955). Scholar
  9. 9.
    Milovanovic, P.: What is that rating thing in stats? (2010). Accessed 21 Nov 2017
  10. 10.
    Milovanovic, P.: Introducing Rating 2.0 (2017). Accessed 21 Nov 2017
  11. 11.
    Peled, O., Fire, M., Rokach, L., Elovici, Y.: Entity matching in online social networks. In: 2013 International Conference on Social Computing, pp. 339–344, September 2013Google Scholar
  12. 12.
    Varda, K.: Protocol buffers: Googles data interchange format. Google Open Source Blog, Available at least as early as July 2008Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • David Bednárek
    • 1
  • Martin Kruliš
    • 1
  • Jakub Yaghob
    • 1
  • Filip Zavoral
    • 1
    Email author
  1. 1.Parallel Architectures/Algorithms/Applications Research GroupCharles UniversityPragueCzech Republic

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