  Daily fantasy sports (DFS) are weekly or daily online contests where
real-game performances of individual players are converted to fantasy points
(FPTS). Users select players for their lineup to maximize their FPTS within a
set player salary cap. This paper focuses on (1) the development of a method to
forecast NFL player performance under uncertainty and (2) determining an
optimal lineup to maximize FPTS under a set salary limit. A supervised learning
neural network was created and used to project FPTS based on past player
performance (2018 NFL regular season for this work) prior to the upcoming week.
These projected FPTS were used in a mixed integer linear program to find the
optimal lineup. The performance of resultant lineups was compared to
randomly-created lineups. On average, the optimal lineups outperformed the
random lineups. The generated lineups were then compared to real-world lineups
from users on DraftKings. The generated lineups generally fell in approximately
the 31st percentile (median). The FPTS methods and predictions presented here
can be further improved using this study as a baseline comparison.
