Feature-based Projections for Effective Playtrace Analysis
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 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 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.
Project Members
Yun-En Liu
Erik Andersen
Richard Snider
Seth Cooper
Zoran Popović
Feature-based Projections for Effective Playtrace Analysis
Yun-En Liu, Erik Andersen, Richard Snider, Seth Cooper, Zoran Popović
International Conference on the Foundations of Digital Games (FDG 2011)
[Paper (5.21 MB)]
[Project Website]
[Play Refraction (Requires Adobe Flash Player 10)]
[Project Website]
Hello Worlds
[Hello Worlds on Kongregate (Requires Adobe Flash Player 10)]
University of Washington Center for Game Science
National Science Foundation