Method and Validation for Optimal Lineup Creation for Daily Fantasy Football Using Machine Learning and Linear Programming

TL;DR

This paper develops a machine learning and linear programming approach to optimize daily fantasy football lineups, improving performance over random lineups and providing a baseline for future enhancements.

Contribution

It introduces a neural network-based performance forecast combined with linear programming to create optimal fantasy football lineups under salary constraints.

Findings

Optimal lineups outperform random lineups

Generated lineups are around the 31st percentile of real-world lineups

Method provides a baseline for future improvements

Abstract

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.