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

Author

Listed:
  • Vittorio Maniezzo

    (University of Bologna)

  • Fabian Andres Aspee Encina

    (University of Bologna)

Abstract

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.

Suggested Citation

  • Vittorio Maniezzo & Fabian Andres Aspee Encina, 2022. "Predictive Analytics for Real-time Auction Bidding Support: a Case on Fantasy Football," SN Operations Research Forum, Springer, vol. 3(3), pages 1-23, September.
    • Handle: RePEc:spr:snopef:v:3:y:2022:i:3:d:10.1007_s43069-022-00160-w
    DOI: 10.1007/s43069-022-00160-w
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    References listed on IDEAS

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    1. Boulier, Bryan L. & Stekler, H. O., 2003. "Predicting the outcomes of National Football League games," International Journal of Forecasting, Elsevier, vol. 19(2), pages 257-270.
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      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    3. Müller, Oliver & Simons, Alexander & Weinmann, Markus, 2017. "Beyond crowd judgments: Data-driven estimation of market value in association football," European Journal of Operational Research, Elsevier, vol. 263(2), pages 611-624.
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