Abstract
AbstractThis work reports about an end-to-end business analytics experiment, applying predictive and prescriptive analytics to real-time bidding support for fantasy football draft auctions. Forecast methods are used to quantify the expected return of each investment alternative, while subgradient optimization is used to provide adaptive online recommendations on the allocation of scarce budget resources. A distributed front-end implementation of the prescriptive modules and the rankings of simulated leagues testify the viability of this architecture for actual support.
Funder
Alma Mater Studiorum - Università di Bologna
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Control and Optimization,Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)
Reference35 articles.
1. Ren K, Zhang W, Chang K, Rong Y, Yu Y, Wang J (2018) Bidding machine: Learning to bid for directly optimizing profits in display advertising. IEEE Trans Knowl Data Eng 30(4):645–659
2. Zhang CR, Zhang E (2014) Optimized bidding algorithm of real time bidding in online ads auction. pp 33–42
3. Lomax R (2006) Fantasy sports: History, game types, and research, Handbook of sports and media. 1(23}:383–392
4. GlobeNewswire (2021) Fantasy sports market estimate. https://www.prnewswire.com/news-releases/fantasy-sports-market-size-is-expected-to-reach-usd-48-6-billion-by-2027---valuates-reports-301150193.html. last Accessed 2 Mar 2022
5. Smith B, Hooper P (2006) Decision making in online fantasy sports communities. Interactive Technology and Smart Education 3:347–360