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.

翻译：日常梦幻体育（DFS）是每周或每日进行的在线竞赛，其中真实比赛中的球员个人表现被转换为梦幻积分（FPTS）。用户在设定球员薪资上限的条件下选择阵容，以最大化其FPTS。本文聚焦于：（1）开发一种在不确定性下预测NFL球员表现的方法，以及（2）在给定薪资限制下确定最优阵容以最大化FPTS。本研究构建了一个监督学习神经网络，基于过去球员表现（本文以2018年NFL常规赛数据为例）对下一周比赛进行FPTS预测。随后将这些预测的FPTS输入混合整数线性规划模型，以求解最优阵容。将所得阵容的表现与随机生成的阵容进行比较，结果显示最优阵容平均表现优于随机阵容。进一步将生成的阵容与DraftKings平台用户的真实阵容进行对比，发现生成阵容的排名大致位于第31百分位（中位数附近）。本文提出的FPTS方法与预测可作为基线参照，通过后续研究进一步优化改进。