Optimising daily fantasy sports teams with artificial intelligence
Optimising daily fantasy sports teams with artificial intelligence
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.
Beal, Ryan James
d9874cb0-bd92-4a16-8576-78d769b41ff7
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Beal, Ryan James
d9874cb0-bd92-4a16-8576-78d769b41ff7
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Beal, Ryan James, Norman, Timothy and Ramchurn, Sarvapali
(2020)
Optimising daily fantasy sports teams with artificial intelligence.
International Journal of Computer Science in Sport, 19 (2).
(In Press)
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.
Text
DFS_IJCSS
- Accepted Manuscript
More information
Accepted/In Press date: 1 November 2020
Identifiers
Local EPrints ID: 445995
URI: http://eprints.soton.ac.uk/id/eprint/445995
ISSN: 1684-4769
PURE UUID: ec19f627-e963-49a1-9204-db6d7f03320d
Catalogue record
Date deposited: 18 Jan 2021 17:32
Last modified: 18 Feb 2021 17:26
Export record
Contributors
Author:
Ryan James Beal
Author:
Sarvapali Ramchurn
University divisions
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics