Weekly Seasonal Player Population Patterns in Online Games: A Time Series Clustering Approach

Abstract

With the continuous technological advancement in the game industry, millions of players engage in various online games everyday. Player population size of games ebb and flow through time as a complex series. Analyzing these player population numbers in a shorter time window, such as weekly, could help generate insights that enrich the understanding about low-level population fluctuation patterns of online games. However, this area of game data analytics still has space for further enhancement. This study focuses on discovering patterns of weekly player population fluctuations, that could aid in comprehending how the population of various kind of games change within a framing window of a week. We use player population time series of 1963 games available on Steam. Utilizing several trend removal techniques and conducting seasonality detection we identify that 77% of games display a recurring weekly pattern in player population fluctuations. Moreover, our dynamic time warping based cluster analysis shows that there are 9 diverse weekly player population fluctuation patterns. Among these 9, the governing pattern visible in the majority of games displays that the player population is higher towards the weekend. Finally, we scrutinize the tags, age requirements and overall population size of games in each cluster associated with the diverse patterns to generate insights about the characteristics of games associated with each weekly population pattern.