Predicting Chart Difficulty in Rhythm Games Through Classification Using Chart Pattern Derived Attributes

Abstract

Rhythm games are music-themed games that challenge players’ sense of rhythm and reaction skills. One such popular rhythm-based video game is Dance Dance Revolution, where players perform steps on a dance platform that is synchronized with music as directed by on-screen step charts. An issue that exists, not just in Dance Dance Revolution, but in rhythm games in general is the estimation of a chart’s difficulty level. While many methods and studies exist in generating and predicting chart attributes, there is no clear methodology existing in determining the optimal difficulty of a given chart. This paper aims to address the aforementioned issue in the game of Dance Dance Revolution by proposing a methodology that involves extracting patterns and common attributes in step charts that enable more accuracy in determining a chart’s difficulty level. The resulting methodology achieved an average True-Positive rating of 0.683 and an overall model accuracy of 74.82% for classifying charts according to levels in Dance Dance Revolution.