Optimising Daily Fantasy Sports Teams with Artificial Intelligence
Abstract This paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real world players to form the optimal fantasy team using mixed integer programming. We test our solutions using real world data sets from across four seasons (2014 2017) We highlight the advantage that can be gained from using our machine based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game weeks over a season.
Int. J. Comput. Sci. Sport