IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v21y2025i1p51-71n1004.html
   My bibliography  Save this article

Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports

Author

Listed:
  • Florez Mauro

    (Department of Statistics, Rice University, Houston, TX, USA)

  • Guindani Michele

    (Department of Biostatistics, UCLA, Los Angeles, CA, USA)

  • Vannucci Marina

    (Department of Statistics, Rice University, Houston, TX, USA)

Abstract

Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data are essential for making reliable inferences. In this paper, we propose the use of the Conway–Maxwell–Poisson (CMP) model for analyzing count data in sports. The CMP model offers flexibility in modeling data with different levels of dispersion. Here we consider a bivariate CMP model that models the potential correlation between home and away scores by incorporating a random effect specification. We illustrate the advantages of the CMP model through simulations. We then analyze data from baseball and soccer games before, during, and after the COVID-19 pandemic. The performance of our proposed CMP model matches or outperforms standard Poisson and Negative Binomial models, providing a good fit and an accurate estimation of the observed effects in count data with any level of dispersion. The results highlight the robustness and flexibility of the CMP model in analyzing count data in sports, making it a suitable default choice for modeling a diverse range of count data types in sports, where the data dispersion may vary.

Suggested Citation

  • Florez Mauro & Guindani Michele & Vannucci Marina, 2025. "Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 21(1), pages 51-71.
    • Handle: RePEc:bpj:jqsprt:v:21:y:2025:i:1:p:51-71:n:1004
    DOI: 10.1515/jqas-2024-0072
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2024-0072
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jqas-2024-0072?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:bpj:jqsprt:v:21:y:2025:i:1:p:51-71:n:1004. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.