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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
Restricted to Repository staff only until 18 February 2021.
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 Jan 2021 17:32
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Contributors
Author:
Ryan James Beal
Sarvapali Ramchurn
University divisions
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