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Optimising daily fantasy sports teams with artificial intelligence

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
1684-4769
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). (doi:10.2478/ijcss-2020-0008).

Record type: Article

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

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DFS_IJCSS - Accepted Manuscript
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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
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 18 Jan 2021 17:32
Last modified: 12 Mar 2021 02:46

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