There are a number of possible machine learning approaches to modeling the win probability of an NBA game. Previous publically available research suggests that a mixture density network performs best in modeling win probability. In this paper, I explain and reproduce a number of previously shared approaches to modeling win probability. Unlike previous research, I fit each model with an identical set of inputs to fairly evaluate and compare the performance of each model. Furthermore, I create a recurrent mixture density network approach based off the recommendation of previous research. I find that the recurrent mixture density network has the highest measured accuracy in comparison to all other tested models.