The Playtime Principle: Large-scale cross-games interest modeling

The collection and analysis of behavioral telemetry in digital games has in the past five years become an integral part of game development. One of the key challenges in game analytics is the development of methods for characterizing and predicting player behavior as it evolves over time. Characterizing behavior is necessary for monitoring player populations and gradually improve game design and the playing experience. Predicting behavior is necessary to describe player engagement and prevent future player churn. In this paper, methods and theory from kernel archetype analysis and random process models are utilized to evaluate the playtime behavior, i.e. time spent playing specific games as a function of time, of over 6 million players, across more than 3000 PC and console games from the Steam platform, covering a combined playtime of more than 5 billion hours. A number of conclusions can be derived from this large-scale analysis, notably that playtime as a function of time, across the thousands of games in the dataset, and irrespective of local differences in the playtime frequency distribution, can be modeled using the same model: the Weibull distribution. This suggests that there are fundamental properties governing player engagement as it evolves over time, which we here refer to as the Playtime Principle. Additionally, the analysis shows that there are distinct clusters, or archetypes, in the playtime frequency distributions of the investigated games. These archetypal groups correspond to specific playtime distributions. Finally, the analysis reveals information about player behavior across a very large dataset, showing for example that the vast majority of games are players for less than 10 hours, and very few players spend more than 30-35 hours on any specific game.