Feature-based projections for effective playtrace analysis

Abstract

Visual data mining is a powerful technique allowing game designers to analyze player behavior. Playtracer, a new method for visually analyzing play traces, is a generalized heatmap that applies to any game with discrete state spaces. Unfortunately, due to its low discriminative power, Playtracer's usefulness is significantly decreased for games of even medium complexity, and is unusable on games with continuous state spaces. Here we show how the use of state features can remove both of these weaknesses. These state features collapse larger state spaces without losing salient information, resulting in visualizations that are significantly easier to interpret. We evaluate our work by analyzing player data gathered from three complex games in order to understand player behavior in the presence of optional rewards, identify key moments when players figure out the solution to the puzzle, and analyze why players give up and quit. Based on our experiences with these games, we suggest general principles for designers to identify useful features of game states that lead to effective play analyses.