Predicting movies' box office performance is a bit like gambling and predicting the stock market: there is a lot of luck involved but analyzing the system's input frequently leads a better result. There are many inputs to box office prediction but certainly among the most influential in predicting future success are the actors involved and their past performances. Grasping the variables needed in the large data sets to make a box office prediction is an overwhelming task where a visualization would be useful. In this paper we describe a means to visualize these large data sets and distill them down into a simple causal approach that makes understanding the factors that affect box office performance easier.