Due to a steady increase in popularity, player demands for (online) video game content are growing to an extent in which consistency and novelty in challenges is hard to attain. Challenges in balance and error-coping accumulate. We introduce the concept of deep player behavior models by applying machine learning techniques to individual, atomic decision-making strategies. We discuss their potential application fields in personalized challenges, autonomous game testing, human agent substitution, and online crime detection. Results from a pilot study that was carried out with the massively multiplayer online role-playing game Lineage II depict a benchmark between hidden markov models, decision trees, and deep learning. Data analysis and individual reports indicate that deep learning can be employed to provide adequate models of individual player behavior with high accuracy for predicting skill-use and a high correlation in recreating strategies from previously recorded data.