This paper offers an extension to TrueSkill, a Bayesian method for ranking players and predicting outcomes of multiplayer games, for cases where a game is high-dimensional. TrueSkill was originally developed by Microsoft Research to rank and match XBox Live players, but offers a general method for inferring player skill based almost exclusively on the win-loss outcome of a match. Although such a method works well for relatively simple games like Halo, the framework is limited in its ability to incorporate information-rich features - often called boxscores - commonly used to describe high dimensional games, such as basketball. Our work extends TrueSkill for these types of games by reformulating its underlying graphical model as the internal dynamics of a recurrent neural network cell in addition to using neural networks as expressive function approximators to map between high-dimesional boxscores and a player's weight when conducting TrueSkill updates. Experimental results on NBA data shows that our method improves upon the original TrueSkill algorithm for predicting the outcome of basketball games.