Level Difficulty and Player Skill Prediction in Human Computation Games

Abstract

Human computation games (HCGs) often suffer from low player retention. This may be due to the constraints placed on level and game design from the real-world application of the game. Previous work has suggested using player rating systems (such as Elo, Glicko-2, or TrueSkill) as a basis for matchmaking between HCG levels and players, as a means to improve difficulty balancing and thus player retention. Such rating systems typically start incoming entities with a default rating. However, when applied to HCGs, incoming entities may have useful information associated with them, such as player behavior during tutorials and properties of the tasks underlying the levels. In this work, we examined using features derived from player behavior and level properties to predict their eventual Glicko-2 ratings in the HCG Paradox. We found that using regression produced rating estimates closer to the actual ratings than default or baseline average ratings. The use of rating systems allows a unified approach to predicting both player skill and level difficulty.