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
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Norman, Timothy
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Ramchurn, Sarvapali
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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).
(doi:10.2478/ijcss-2020-0008).
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
e-pub ahead of print date: 31 December 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
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Date deposited: 18 Jan 2021 17:32
Last modified: 17 Mar 2024 03:41
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Author:
Ryan James Beal
Author:
Sarvapali Ramchurn
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