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
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- Current Faculties > Faculty of Engineering and Physical Sciences > School of Electronics and Computer Science > Agents, Interactions and Complexity
School of Electronics and Computer Science > Agents, Interactions and Complexity - Current Faculties > Faculty of Engineering and Physical Sciences > School of Electronics and Computer Science
School of Electronics and Computer Science
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