The use of statistical and machine learning approaches, such as Markov chains, for procedural content generation (PCG) has been growing in recent years in the field of Game AI. However, there has been little work in learning to generate content, specifically levels, accounting for player movement within those levels. We are interested in extracting player models automatically from play traces and using those learned models, paired with a machine learning-based generator to create levels that allow the same types of movements observed in the play traces. We test our approach by generating levels for Super Mario Bros. We compare our results against the original levels, a previous constrained sampling approach, and a previous approach that learned a combined player and level model.