<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Optimising Daily Fantasy Sports Teams with Artificial Intelligence

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"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...

Optimising Daily Fantasy Sports Teams with Artificial Intelligence

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 21 - 35

DOI: https://doi.org/10.2478/ijcss-2020-0008

Schlüsselwörter
OPTIMISATION, MACHINE LEARNING, FANTASY FOOTBALL, NFL

© 2020 Ryan Beal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Optimising Daily Fantasy Sports Teams with Artificial Intelligence

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 21 - 35

DOI: https://doi.org/10.2478/ijcss-2020-0008

SchlüsselwörterOPTIMISATION, MACHINE LEARNING, FANTASY FOOTBALL, NFL

© 2020 Ryan Beal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
OPTIMISATION, MACHINE LEARNING, FANTASY FOOTBALL, NFL