Predictive Analytics for Real-time Auction Bidding Support: a Case on Fantasy Football

Autor:	Vittorio Maniezzo, Fabian Andres Aspee Encina
Přispěvatelé:	Vittorio Maniezzo, Fabian Andres Aspee Encina
Rok vydání:	2022
Předmět:	Control and Optimization Predictive analytics · Prescriptive analytics · Lagrangian relaxation · Online decision support Applied Mathematics Economics Econometrics and Finance (miscellaneous) Computer Science Applications
Zdroj:	Operations Research Forum. 3
ISSN:	2662-2556
DOI:	10.1007/s43069-022-00160-w
Popis:	This 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d406be0d107a3d0aa3be5dadcf4d74e1 https://doi.org/10.1007/s43069-022-00160-w Zobrazit plný text záznamu Full text from SpringerLink