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A Primer on a Flexible Bivariate Time Series Model for Analyzing First and Second Half Football Goal Scores: The Case of the Big 3 London Rivals in the EPL

Author

Listed:
  • Yuvraj Sunecher

    (University of Technology Mauritius
    University of Mauritius)

  • Naushad Mamode Khan

    (University of Mauritius)

  • Vandna Jowaheer

    (University of Mauritius)

  • Marcelo Bourguignon

    (Universidade Federal do Rio Grande do Norte)

  • Mohammad Arashi

    (Shahrood University of Technology)

Abstract

The ranking of some English Premier League (EPL) clubs during football season is of keen interest to many stakeholders with special attention to the London rivals: Arsenal, Chelsea and Tottenham. In particular, the first (GF) and second half (GS) scores, besides being inter-related, is perceived as a convenient measure of the clubs potential. This paper studies the contributory effects of the possible factors that commonly influence the club scoring capacity in the halves along with forecasted measures diagnostics via a novel flexible bivariate time series model with COM-Poisson innovations using data from August 2014 to December 2017.

Suggested Citation

  • Yuvraj Sunecher & Naushad Mamode Khan & Vandna Jowaheer & Marcelo Bourguignon & Mohammad Arashi, 2019. "A Primer on a Flexible Bivariate Time Series Model for Analyzing First and Second Half Football Goal Scores: The Case of the Big 3 London Rivals in the EPL," Annals of Data Science, Springer, vol. 6(3), pages 531-548, September.
    • Handle: RePEc:spr:aodasc:v:6:y:2019:i:3:d:10.1007_s40745-018-0180-1
    DOI: 10.1007/s40745-018-0180-1
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    References listed on IDEAS

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    1. Sellers, Kimberly F. & Morris, Darcy Steeg & Balakrishnan, Narayanaswamy, 2016. "Bivariate Conway–Maxwell–Poisson distribution: Formulation, properties, and inference," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 152-168.
    2. Christian Weiß, 2008. "Thinning operations for modeling time series of counts—a survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(3), pages 319-341, August.
    3. Dominique Lord & Srinivas Reddy Geedipally & Seth D. Guikema, 2010. "Extension of the Application of Conway‐Maxwell‐Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1268-1276, August.
    4. Ian McHale & Phil Scarf, 2007. "Modelling soccer matches using bivariate discrete distributions with general dependence structure," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 432-445, November.
    5. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    6. Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
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    Cited by:

    1. Lucio Palazzo & Riccardo Ievoli, 2022. "A Semiparametric Approach to Test for the Presence of INAR: Simulations and Empirical Applications," Mathematics, MDPI, vol. 10(14), pages 1-18, July.

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