Artificial Intelligence

Optimising daily fantasy sports teams with artificial intelligence

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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

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.

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Text

DFS_IJCSS
– Accepted Manuscript

Restricted to Repository staff only until 18 February 2021.

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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

Author:

Sarvapali Ramchurn

ORCID iD

University divisions

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