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Meaning in Gameplay: Filtering Variables, Defining Metrics, Extracting Features and Creating Models for Gameplay Analysis

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Game Analytics

Abstract

Analyzing game-related data, at its core, is a process that involves being able to articulate knowledge and meaning from apparently meaningless data. Analysis often consists of imposing order, establishing categories and seeing patterns in disorderly, continuous and heterogeneous streams of information, especially when dealing with gameplay telemetry data, which directly emanates from players’ behavior. Since human behavior represents the response of an organism to its ecosystem, it possesses no intrinsic meaning; rather it needs to be interpreted. It is mostly through interpretation of data that actionable knowledge and pertinent meaning can be massaged into existence according to the assumption that player motivations, desires, beliefs and personality are encoded in a player’s behavior and it is sufficient to interpret metrics data to unravel extensive information about players. Ludwig Wittgenstein, in his Tractatus logico-philosophicus, (Wittgenstein 2001) said that “the limits of my language mean the limits of my world” implying that the logical possibilities available within a certain domain are constrained by the language used to talk about such a domain. In the specific case of game data analysis, the verbs used to talk about player behavior are defined by the game variables measured and tracked by the telemetry system. These variables, once measured, become metrics, and from metrics, features are extracted; the selection of which features to use is a pivotal component of game data analysis. This chapter presents strategies to aid in this process, specifically, in the selection of variables, their measurement and the treatment of the resulting features to obtain meaningful models. The process of selecting game variables to be monitored for further analysis is not a trivial one since it is exactly this process of selection that defines which analyses can be carried out and enables analysts to draw inferences from the game.

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Authors and Affiliations

  1. College of Arts, Media and Design, Northeastern University, Boston, MA, USA

    Alessandro Canossa Ph.D.

  2. Center for Computer Games Research, IT University of Copenhagen, Copenhagen, Denmark

    Alessandro Canossa Ph.D.

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  1. Alessandro Canossa Ph.D.
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Correspondence to Alessandro Canossa Ph.D. .

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Editors and Affiliations

  1. College of Arts, Media and Design, Northeastern University, Huntington Avenue 360, Boston, 02115, Massachusetts, USA

    Magy Seif El-Nasr

  2. College of Arts, Media and Design, Northeastern University, Huntington Avenue 360, Boston, 02115, Massachusetts, USA

    Anders Drachen

  3. College of Arts, Media and Design, Northeastern University, Huntington Avenue 360, Boston, 02115, Massachusetts, USA

    Alessandro Canossa

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About the Author

Alessandro Canossa, Ph.D. is Associate Professor in the College of Arts, Media and Design at Northeastern University, he obtained a MA in Science of Communication from the University of Turin in 1999 and in 2009 he received his PhD from The Danish Design School and The Royal Danish Academy of Fine Arts, School of Architecture. His doctoral research was carried out in collaboration with Io Interactive, a Square Enix game development studio, and it focused on user-centric design methods and approaches: prototypical player behaviors are described procedurally and from those profiles game environments emerge that are able to accommodate all of the possibilities for action. In the course of his research, Dr. Canossa has published more than 30 articles, book chapters, and journal contributions, he has presented at several international conferences including Future Play, GDC San Francisco, GDC Canada, Mindtrek, IFIP Interact, IEEE Conference on Computational Intelligence and Games, ACM Foundations of Digital Games and DiGRA discussing topics from game user research, HCI, game metrics analysis and player experience. He has also received a Best Paper award at the largest media conference in Northern Europe, MindTrek, in 2009. Beside his academic activities he still maintain contact with the industry: his work has been commented on and used by companies such as Ubisoft, Electronic Arts, Microsoft, and Square Enix. Within Square Enix he carries on an ongoing collaboration with IO Interactive, Crystal Dynamics and Beautiful Games Studio, where he has worked on titles such as Kane & Lynch: Dead Men, Tomb Raider: Underworld and Kane & Lynch: Dog Days.

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Canossa, A. (2013). Meaning in Gameplay: Filtering Variables, Defining Metrics, Extracting Features and Creating Models for Gameplay Analysis. In: Seif El-Nasr, M., Drachen, A., Canossa, A. (eds) Game Analytics. Springer, London. https://doi.org/10.1007/978-1-4471-4769-5_13

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  • DOI: https://doi.org/10.1007/978-1-4471-4769-5_13

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  • Online ISBN: 978-1-4471-4769-5

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