Optimising Daily Fantasy Sports Teams with Artificial Intelligence

Beal, R., Norman, T. J., & Ramchurn, S. D. (2019). Artificial intelligence for team sports: a survey. The Knowledge Engineering Review.10.1017/S0269888919000225Search in Google Scholar

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.Search in Google Scholar

Breiman, L. (2001). Random Forests. Machine Learning., 5-32.10.1023/A:1010933404324Search in Google Scholar

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-ySearch in Google Scholar

Chu, P. C., & Beasley, J. E. (1998). A Genetic Algorithm for the Multidimensional Knapsack Problem. Journal of Heuristics, 63-86.10.1023/A:1009642405419Search in Google Scholar

Dang et Al., V. (2006). Overlapping coalition formation for efficient data fusion in multi-sensor networks. Proceedings of the 21st National Conference on Artificial Intelligence, (S. 635-640).Search in Google Scholar

Dixon, M., & Coles, S. (1997). Modelling association football scores and inefficiencies in the football betting market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 265-280.Search in Google Scholar

Easton, T., & Newell, S. (2019). Are daily fantasy sports gambling? Journal of Sports Analytics, 35-43.10.3233/JSA-180240Search in Google Scholar

Forbes. (2013). Forbes. Von https://www.forbes.com/sites/briangoff/2013/08/20/the-70-billion-fantasy-football-market/#1c340385755c. (accessed on: 20/06/2020).Search in Google Scholar

Gers, F., Schmidhuber, J., & Cummins, F. (1999). Learning to forget: Continual prediction with LSTM. IET.10.1049/cp:19991218Search in Google Scholar

Kapania, N. (2012). Predicting Fantasy Football Performance with Machine Learning Techniques. Von http://cs229.stanford.edu/proj2012/Kapania-FantasyFootballAndMachineLearning.pdf (accessed on: 20/06/2020).Search in Google Scholar

Landers, J. R., & Duperrouzel, B. (2018). Machine Learning Approaches to Competing in Fantasy Leagues for the NFL. IEEE Transactions on Games.Search in Google Scholar

Matthews, T., Ramchurn, S., & Chalkiadakis, G. (2012). Competing with Humans at Fantasy Football: Team Formation in Large Partially-observable Domains. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, (S. 1394-1400).Search in Google Scholar

Meehan, J. C. (2015). The Predominate Goliath: Why Pay-to-Play Daily Fantasy Sports are Games of Skill under the Dominant Factor Test. Marquette Sports Law Review, 5.Search in Google Scholar

Nesbit, T., & King, K. (2010). The impact of fantasy sports on television viewership. Journal of Media Economics, 24-41.10.1080/08997761003590721Search in Google Scholar

Park, J., & Sandberg, I. W. (1993). Approximation and Radial-Basis-Function Networks. Neural Computation, 305-316.10.1162/neco.1993.5.2.305Search in Google Scholar

Ramchurn et Al., S. (2010). Coalition formation with spatial and temporal constraints. Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, (S. 1181-1188).Search in Google Scholar

Seber, G. A., & Lee, A. J. (2012). Linear regression analysis. John Wiley & Sons.Search in Google Scholar

Sugar, G., & Swenson, T. (2015). Predicting Optimal Game Day Fantasy Football Teams. Von http://cs229.stanford.edu/proj2015/115_report.pdf (accessed on: 20/06/2020).Search in Google Scholar