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, Play-
tracer's usefulness is signicantly 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 signicantly
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 gure 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 eective play analyses.