IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v600y2022ics0378437122003740.html
   My bibliography  Save this article

A physics-based algorithm to perform predictions in football leagues

Author

Listed:
  • Stock, Eduardo Velasco
  • da Silva, Roberto
  • Fernandes, Henrique A.

Abstract

In this work, we extended a stochastic model for football leagues based on the team’s potential (da Silva et al., 2013) for making predictions instead of only performing a successful characterization of the statistics on the punctuation of the real leagues. Our adaptation considers the advantage of playing at home when considering the potential of the home and away teams. The algorithm predicts the tournament’s outcome by using the market value or/and the ongoing team’s performance as initial conditions in the context of Monte Carlo simulations. We present and compare our results to the worldwide known SPI predictions performed by the “FiveThirtyEight” project. The results show that the algorithm can deliver good predictions even with a few ingredients and in more complicated seasons like the 2020 editions where the matches were played without fans in the stadiums.

Suggested Citation

  • Stock, Eduardo Velasco & da Silva, Roberto & Fernandes, Henrique A., 2022. "A physics-based algorithm to perform predictions in football leagues," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
  • Handle: RePEc:eee:phsmap:v:600:y:2022:i:c:s0378437122003740
    DOI: 10.1016/j.physa.2022.127532
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122003740
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2022.127532?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.

    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:eee:phsmap:v:600:y:2022:i:c:s0378437122003740. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.