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Generalized model for scores in volleyball matches

Author

Listed:
  • Gonzalez-Cabrera Ivan

    (Konrad Lorenz Institute for Evolution and Cognition Research, Klosterneuburg, Austria)

  • Herrera Diego Dario

    (Federación Colombiana de Voleibol, Bogotá, Colombia)

  • González Diego Luis

    (Departamento de Física, Universidad del Valle, A.A. 25360, Cali, Colombia)

Abstract

We propose a Markovian model to calculate the winning probability of a set in a volleyball match. Traditional models take into account that the scoring probability in a rally (SP) depends on whether the team starts the rally serving or receiving. The proposed model takes into account that the different rotations of a team have different SPs. The model also takes into consideration that the SP of a given rotation complex 1 (K1) depends on the players directly involved in that complex. Our results help to design general game strategies and, potentially, more efficient training routines. In particular, we used the model to study several game properties, such as the importance of having serve receivers with homogeneous performance, the effect of the players’ initial positions on score evolution, etc. Finally, the proposed model is used to diagnose the performance of the female Colombian U23 team (U23 CT).

Suggested Citation

  • Gonzalez-Cabrera Ivan & Herrera Diego Dario & González Diego Luis, 2020. "Generalized model for scores in volleyball matches," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 41-55, March.
  • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:1:p:41-55:n:4
    DOI: 10.1515/jqas-2019-0060
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    References listed on IDEAS

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    1. Walker, Mark & Wooders, John & Amir, Rabah, 2011. "Equilibrium play in matches: Binary Markov games," Games and Economic Behavior, Elsevier, vol. 71(2), pages 487-502, March.
    2. Newton Paul K & Aslam Kamran, 2009. "Monte Carlo Tennis: A Stochastic Markov Chain Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-44, July.
    3. Miskin Michelle A & Fellingham Gilbert W & Florence Lindsay W, 2010. "Skill Importance in Women's Volleyball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-14, April.
    4. Ferrante Marco & Fonseca Giovanni, 2014. "On the winning probabilities and mean durations of volleyball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-8, June.
    5. Kovacs Balazs, 2009. "The Effect of the Scoring System Changes in Volleyball: A Model and an Empirical Test," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-14, July.
    6. John Simmons, 1989. "A Probabilistic Model of Squash: Strategies and Applications," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(1), pages 95-110, March.
    7. David Strauss & Barry C. Arnold, 1987. "The Rating of Players in Racquetball Tournaments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 163-173, June.
    8. Zetou Eleni & Moustakidis Athanasios & Tsigilis Nikolaos & Komninakidou Andromahi, 2007. "Does Effectiveness of Skill in Complex I Predict Win in Men's Olympic Volleyball Games?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(4), pages 1-11, October.
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