Visualization of Player Movement Patterns with Line Integral Convolution and Alpha Shapes

Games are frequently viewed as spatial constructs, where the game space enables play, creates challenge, and enhances and facilitates immersion. The spatial analysis of player behavior thus constitutes a key element in games user research and game analytics to help understand how players navigate game environments. However, the complexity of movement calls for visual solutions that clearly define and communicate patterns in player behavior. In this paper, we address this challenge by introducing a method for visualizing aggregated player trajectories, also in conjunction with other behavioral metrics. This way movement is not viewed in isolation but contextualized within the broader player behavior. Line integral convolution textures are used to summarize the structural patterns of the movement while additional data can be displayed simultaneously through encoding it in the visual channels of the texture. Further, α -shapes are used to highlight and describe the spatial shape of the traversed parts of the game environment. We demonstrate the approach by applying it to the popular esports games Dota 2 and Starcraft: Brood War and discuss its generalizability within and outside esports.