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Joint modelling of goals and bookings in association football

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  • A. C. Titman
  • D. A. Costain
  • P. G. Ridall
  • K. Gregory

Abstract

type="main" xml:id="rssa12075-abs-0001"> A multivariate counting process formulation is developed for the quantification of association football event interdependences which permits dynamic prediction as events unfold. We model data from English Premier League and Championship games from the 2009–2010 and 2010–2011 football seasons and assess predictive capacity by using a model-based betting strategy, applied prospectively to available live spread betting prices. Both the scoreline and the bookings status were predictive of match outcome. In particular, the award of a red card led to increased goal rates for the non-penalized team and the home team scoring rate decreased once they were ahead. Overall the betting strategy profited with gains made in the bookings markets.

Suggested Citation

  • A. C. Titman & D. A. Costain & P. G. Ridall & K. Gregory, 2015. "Joint modelling of goals and bookings in association football," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 659-683, June.
    • Handle: RePEc:bla:jorssa:v:178:y:2015:i:3:p:659-683
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    File URL: http://hdl.handle.net/10.1111/rssa.2015.178.issue-3
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    References listed on IDEAS

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    1. Peter Dawson & Stephen Dobson & John Goddard & John Wilson, 2007. "Are football referees really biased and inconsistent?: evidence on the incidence of disciplinary sanction in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 231-250, January.
    2. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
    3. Vecer Jan & Kopriva Frantisek & Ichiba Tomoyuki, 2009. "Estimating the Effect of the Red Card in Soccer: When to Commit an Offense in Exchange for Preventing a Goal Opportunity," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-20, January.
    4. Babatunde Buraimo & David Forrest & Robert Simmons, 2010. "The 12th man?: refereeing bias in English and German soccer," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 431-449, April.
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    Cited by:

    1. da Costa, Igor Barbosa & Marinho, Leandro Balby & Pires, Carlos Eduardo Santos, 2022. "Forecasting football results and exploiting betting markets: The case of “both teams to score”," International Journal of Forecasting, Elsevier, vol. 38(3), pages 895-909.
    2. Jakub Červený & Jan C. Ours & Martin A. Tuijl, 2018. "Effects of a red card on goal-scoring in World Cup football matches," Empirical Economics, Springer, vol. 55(2), pages 883-903, September.
    3. Llorenç Badiella & Pedro Puig & Carlos Lago-Peñas & Martí Casals, 2023. "Influence of Red and Yellow cards on team performance in elite soccer," Annals of Operations Research, Springer, vol. 325(1), pages 149-165, June.
    4. Dmitry Dagaev & Sofia Paklina & J. James Reade & Carl Singleton, 2024. "The Iron Curtain and Referee Bias in International Football," Journal of Sports Economics, , vol. 25(1), pages 126-151, January.
    5. Camilo Abbate & Jeffrey Cross & Richard Uhrig, 2025. "Video assistant referee and home field advantage: Implications for referee bias," Southern Economic Journal, John Wiley & Sons, vol. 91(3), pages 1176-1196, January.
    6. Babatunde Buraimo & David Forrest & Ian G. McHale & J.D. Tena, 2020. "Unscripted Drama: Soccer Audience Response To Suspense, Surprise, And Shock," Economic Inquiry, Western Economic Association International, vol. 58(2), pages 881-896, April.
    7. Marius Ötting & Christian Deutscher & Carl Singleton & Luca De Angelis, 2023. "Gambling on Momentum in Contests," Economics Discussion Papers em-dp2023-08, Department of Economics, University of Reading.

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