Fantasy sports, particularly the daily variety in which new lineups are selected each day, is a rapidly growing industry. The two largest companies in the daily fantasy business, DraftKings and Fanduel, are valued at more than \$1 billion. This research focuses on the development of a complete system for daily fantasy basketball, including both the prediction of player performance and the construction of a team. First, a Bayesian random effects model is used to predict an aggregate measure of daily NBA player performance. The predictions are then used to construct teams under the constraints of the game, typically related to a fictional salary cap and player positions. A permutation based and K-nearest neighbors approach are compared in terms of the identification of “successful” teams – those who would be competitive more often than not. We demonstrate the efficacy of our system by comparing our predictions to those from a well-known analytics website, and by simulating daily competitions over the course of the 2015-2016 season. Our results show an expected profit of approximately \$9,000 on an initial \$500 investment using the K-nearest neighbors approach, a 36% increase relative to using the permutation based approach alone.