IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v3y2022i3d10.1007_s43069-022-00160-w.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-022-00160-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43069-022-00160-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baker, Rose D. & McHale, Ian G., 2013. "Forecasting exact scores in National Football League games," International Journal of Forecasting, Elsevier, vol. 29(1), pages 122-130.
    2. Song, Kai & Shi, Jian, 2020. "A gamma process based in-play prediction model for National Basketball Association games," European Journal of Operational Research, Elsevier, vol. 283(2), pages 706-713.
    3. Vaughan Williams, Leighton & Stekler, Herman O., 2010. "Sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 445-447, July.
      • Herman O. Stekler, 2007. "Sports Forecasting," Working Papers 2007-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Jan 2007.
    4. Pedro Garcia‐del‐Barrio & Pablo Agnese, 2023. "To comply or not to comply? How a UEFA wage‐to‐revenue requirement might affect the sport and managerial performance of soccer clubs," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 767-786, March.
    5. Bryan Boulier & H. O. Stekler & Sarah Amundson, 2006. "Testing the efficiency of the National Football League betting market," Applied Economics, Taylor & Francis Journals, vol. 38(3), pages 279-284.
    6. Craig, J. Dean & Winchester, Niven, 2021. "Predicting the national football league potential of college quarterbacks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 733-743.
    7. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • 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.
    8. Singleton, Carl & Reade, J. James & Brown, Alasdair, 2020. "Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 89(C).
    9. Marco Di Domizio & Raul Caruso & Bernd Frick, 2020. "Intelligenza Collettiva E Valore Di Mercato Dei Calciatori: Il Caso Transfermarkt," Rivista di Diritto ed Economia dello Sport, Centro di diritto e business dello Sport, vol. 16(2), pages 155-172, novembre.
    10. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    11. Nancy Ammon Jianakoplos & Martin Shields, 2012. "Practice or Profits," Journal of Sports Economics, , vol. 13(4), pages 451-465, August.
    12. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
    13. Daniel Megía‐Cayuela, 2023. "Valuation of ticket prices for first‐division football matches in the Spanish league," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(1), pages 576-594, January.
    14. Justin Cox & Adam L. Schwartz & Bonnie F. Van Ness & Robert A. Van Ness, 2021. "The Predictive Power of College Football Spreads: Regular Season Versus Bowl Games," Journal of Sports Economics, , vol. 22(3), pages 251-273, April.
    15. Chunyang Huang & Shaoliang Zhang, 2023. "Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players," Papers 2311.04599, arXiv.org, revised Nov 2023.
    16. Maribel Serna Rodríguez & Andrés Ramírez Hassan & Alexander Coad, 2019. "Uncovering Value Drivers of High Performance Soccer Players," Journal of Sports Economics, , vol. 20(6), pages 819-849, August.
    17. Song, ChiUng & Boulier, Bryan L. & Stekler, Herman O., 2007. "The comparative accuracy of judgmental and model forecasts of American football games," International Journal of Forecasting, Elsevier, vol. 23(3), pages 405-413.
    18. Alexis Direr, 2013. "Are betting markets efficient? Evidence from European Football Championships," Applied Economics, Taylor & Francis Journals, vol. 45(3), pages 343-356, January.
    19. Carlos Sáenz-Royo, 2017. "A plausible Decision Heuristics Model: Fallibility of human judgment as an endogenous problem," Working Papers 2017/04, Economics Department, Universitat Jaume I, Castellón (Spain).
    20. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snopef:v:3:y:2022:i:3:d:10.1007_s43069-022-00160-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.