NBA coaches and general managers are tasked with building lineups and rosters that maximize their chances of winning. Further, basketball is a team sport where interactions between the players in a lineup can be integral to the success or failure of that lineup; managing a team is not as simple as building a lineup of the best possible players without considering the way in which their play styles interact with one another. However, the most prominent statistical methods for evaluating players make the explicit assumption that there are no interaction effects between the players on the court in producing point outcomes. This thesis introduces a model to predict the expected point differential between two five-player lineups in the NBA based on the players’ play styles and the way in which these play styles interact, where play styles are represented by a collection of statistics collected from play-by-play data to describe each player’s tendencies. This model, which is shown to give a significant predictive improvement over today’s standard adjusted plus/minus models for player evaluation, is then used to evaluate players, lineups, and teams.