Understanding game sessions through provenance

• Published in: Entertainment computing 2018-08, Vol.27, p.110-127
• Main Authors: Costa Kohwalter, Troy, de Azeredo Figueira, Felipe Machado, de Lima Serdeiro, Eduardo Assis, da Silva Junior, Jose Ricardo, Gresta Paulino Murta, Leonardo, Walter Gonzalez Clua, Esteban
• Format: Article
• Language: English
• Subjects: Game analytics; Provenance graph; Tracked game data
• ISSN: 1875-9521; 1875-953X
• DOI: 10.1016/j.entcom.2018.05.001

•Game analysis techniques normally do not consider cause-and-effect relationship.•Concrete framework that uses provenance for representing cause-and-effect in games.•Shows the analysis of a game session using the framework over two existing games.•Provenance analysis in early stages of game development for game balancing.•Experiment with twelve participants for game balance and difficult. The outcome of a gameplay session is derived from a series of events, decisions, and interactions made during the game. Many techniques have been developed by the game industry to understand a gameplay session. A successful technique is game analytics, which aims at understanding behavior patterns to improve game quality. However, current methods are not sufficient to capture underlying cause-and-effect relationships that occur during a gameplay session, which would allow designers to better identify possible mistakes in the mechanics or fine-tune their game. Recently, it was proposed a conceptual framework based on provenance to capture these relationships. In this paper, we present a concrete framework to capture provenance data, allowing developers to add provenance gathering capabilities to their games. We instantiated our framework in two games, showing how it can be used in practice, and we developed a new game to demonstrate how provenance could be employed in early stages of game development to assist balancing the difficulty. We conducted an experiment with twelve volunteers and used the gathered provenance data to answer designers’ frequent questions when trying to understand game sessions and balancing the difficulty of their games. This supports the relevance of collecting provenance data from games.