Abstract
Providing a fair matchmaking system is an essential issue, while developing every online video game. In the article, we show that the currently existing matchmaking system in League of Legends, one of the most popular online video games currently existing, is built on a base of conditions which do not hold true in the presence of empirical data. This, in short, decreases the effectiveness of the ranking system, and negatively affects users experience. Therefore, we propose a new ranking system, which genuinely answers the needs, which arise from League of Legends gameplay. As League of Legends gameplay model is nowadays highly popular amid online video games, the proposed system can be easily generalized and adopted by other online video games that are currently popular among gamers.
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References
Blackburn, J., Kwak, H.: Stfu noob!: predicting crowdsourced decisions on toxic behavior in online games. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 877–888. International World Wide Web Conferences Steering Committee (2014)
Bradley, R.A., Terry, M.E.: Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 324–345 (1952)
Dangauthier, P., Herbrich, R., Minka, T., Graepel, T.: Trueskill through time: revisiting the history of chess. In: Advances in Neural Information Processing Systems 20, pp. 931–938. MIT Press (2008). http://research.microsoft.com/apps/pubs/default.aspx?id=74417
Elo, A.E.: The rating of chessplayers, past and present. Arco Pub., New York (1978)
Glickman, M.E.: Parameter estimation in large dynamic paired comparison experiments. Journal of the Royal Statistical Society: Series C (Applied Statistics) 48(3), 377–394 (1999)
Guo, S., Sanner, S., Graepel, T., Buntine, W.: Score-based bayesian skill learning. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part I. LNCS, vol. 7523, pp. 106–121. Springer, Heidelberg (2012). http://research.microsoft.com/apps/pubs/default.aspx?id=193839
Harkness, K.: Official Chess Handbook. McKay Company (1967)
Herbrich, R., Minka, T., Graepel, T.: Trueskill(tm): a bayesian skill rating system. In: Advances in Neural Information Processing Systems 20, pp. 569–576. MIT Press, January 2007. http://research.microsoft.com/apps/pubs/default.aspx?id=67956
Hinnant, N.C.: Practicing work, perfecting play: League of legends and the sentimental education of e-sports (2013)
Warner, D.E., Raiter, M.: Social context in massively-multiplayer online games (mmogs): Ethical questions in shared space. International Review of Information Ethics 4(7) (2005)
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Myślak, M., Deja, D. (2015). Developing Game-Structure Sensitive Matchmaking System for Massive-Multiplayer Online Games. In: Aiello, L., McFarland, D. (eds) Social Informatics. SocInfo 2014. Lecture Notes in Computer Science(), vol 8852. Springer, Cham. https://doi.org/10.1007/978-3-319-15168-7_25
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DOI: https://doi.org/10.1007/978-3-319-15168-7_25
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