Affiliation:
1. University of Southampton , University Rd, Southampton, SO17 1BJ
Abstract
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.
Subject
Biomedical Engineering,General Computer Science
Reference19 articles.
1. Beal, R., Norman, T. J., & Ramchurn, S. D. (2019). Artificial intelligence for team sports: a survey. The Knowledge Engineering Review.10.1017/S0269888919000225
2. Boswell, J. (2008). Fantasy Sports: A Game of Skill That Is Implicitly Legal under State Law, and Now Explicitly Legal under Federal Law. Cardozo Arts & Entertainment Law Journal, 1257-1278.
3. Breiman, L. (2001). Random Forests. Machine Learning., 5-32.10.1023/A:1010933404324
4. Chalkiadakis, G., & Boutilier, C. (2012). Sequentially optimal repeated coalition formation under uncertainty. Autonomous Agents and Multi-Agent Systems, 441-484.10.1007/s10458-010-9157-y
5. Chu, P. C., & Beasley, J. E. (1998). A Genetic Algorithm for the Multidimensional Knapsack Problem. Journal of Heuristics, 63-86.10.1023/A:1009642405419
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