Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny

Yaser Norouzzadeh Ravari, Pieter Spronck, Rafet Sifa, Anders Drachen

    Research output: Contribution to conferencePosterOther research output

    Abstract

    Competitive multi-player game play is a common feature in major commercial titles, and has formed the foundation for esports. In this paper, the question whether it is possible to predict match outcomes in First Person Shooter-type multiplayer competitive games with mixed genres is addressed.
    The case employed is Destiny, which forms a hybrid title combining Massively Multi-player Online Role-Playing game features and First-Person Shooter games. Destiny provides the opportunity to investigate prediction of the match
    outcome, as well as the influence of performance metrics on the match results in a hybrid multi-player major commercial title. Two groups of models are presented for predicting match results: One group predicts match results for
    each individual game mode and the other group predicts match results in general, without considering specific game modes. Models achieve a performance between 63% and 99% in terms of average precision, with a higher performance
    recorded for the models trained on specific multi-player game modes, of which Destiny has several. We also analyzed performance metrics and their influence for each model. The results show that many key shooter performance metrics such
    as Kill/Death ratio are relevant across game modes, but also that some performance metrics are mainly important for specific competitive game modes. The results indicate that reliable match prediction is possible in FPS-type esports games.
    Original languageEnglish
    Publication statusPublished - 5 Oct 2017

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