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A Bayesian quest for finding a unified model for predicting volleyball games

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  • Leonardo Egidi
  • Ioannis Ntzoufras

Abstract

Volleyball is a team sport with unique and specific characteristics. We introduce a new two‐level hierarchical Bayesian model which accounts for these volleyball‐specific characteristics. In the first level, we model the set outcome with a simple logistic regression model. Conditionally on the winner of the set, in the second level, we use a truncated negative binomial distribution for the points earned by the losing team. An additional Poisson‐distributed inflation component is introduced to model the extra points played in the case that the two teams have a point difference less than two points. The number of points of the winner within each set is deterministically specified by the winner of the set and the points of the inflation component. The team‐specific abilities and the home effect are used as covariates on all layers of the model (set, point and extra inflated points). The implementation of the proposed model on the Italian SuperLega 2017–2018 data shows exceptional reproducibility of the final league table and satisfactory predictive ability.

Suggested Citation

  • Leonardo Egidi & Ioannis Ntzoufras, 2020. "A Bayesian quest for finding a unified model for predicting volleyball games," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1307-1336, November.
    • Handle: RePEc:bla:jorssc:v:69:y:2020:i:5:p:1307-1336
    DOI: 10.1111/rssc.12436
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    References listed on IDEAS

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    1. K. Papadimitriou & E. Pashali & I. Sermaki & S. Mellas & M. Papas, 2004. "The effect of the opponents’ serve on the offensive actions of Greek setters in volleyball games," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 4(1), pages 23-33, August.
    2. M.‐H. Chen & J. G. Ibrahim & C. Yiannoutsos, 1999. "Prior elicitation, variable selection and Bayesian computation for logistic regression models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 223-242.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Fellingham Gilbert W. & Hinkle Lee J. & Hunter Iain, 2013. "Importance of attack speed in volleyball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 87-96, March.
    5. Siem Jan Koopman & Rutger Lit, 2015. "A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 167-186, January.
    6. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    7. Hendrik Sonnabend, 2020. "On discouraging environments in team contests: Evidence from top‐level beach volleyball," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(6), pages 986-997, September.
    8. K. T. Lee & S. T. Chin, 2004. "Strategies to serve or receive the service in volleyball," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 59(1), pages 53-67, February.
    9. Cohen-Zada, Danny & Krumer, Alex & Shapir, Offer Moshe, 2018. "Testing the effect of serve order in tennis tiebreak," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 106-115.
    10. Shonkwiler, J.S., 2016. "Variance of the truncated negative binomial distribution," Journal of Econometrics, Elsevier, vol. 195(2), pages 209-210.
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