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Simulating a basketball match with a homogeneous Markov model and forecasting the outcome

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  • Štrumbelj, Erik
  • Vračar, Petar
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    Abstract

    We used a possession-based Markov model to model the progression of a basketball match. The model’s transition matrix was estimated directly from NBA play-by-play data and indirectly from the teams’ summary statistics. We evaluated both this approach and other commonly used forecasting approaches: logit regression of the outcome, a latent strength rating method, and bookmaker odds. We found that the Markov model approach is appropriate for modelling a basketball match and produces forecasts of a quality comparable to that of other statistical approaches, while giving more insight into basketball. Consistent with previous studies, bookmaker odds were the best probabilistic forecasts.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0169207011000458
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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 28 (2012)
    Issue (Month): 2 ()
    Pages: 532-542

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    Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:532-542

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Sports forecasting; Probability forecasting; Monte Carlo; Simulation; National Basketball Association;

    References

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    1. Kubatko Justin & Oliver Dean & Pelton Kevin & Rosenbaum Dan T, 2007. "A Starting Point for Analyzing Basketball Statistics," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(3), pages 1-24, July.
    2. Franck, Egon & Verbeek, Erwin & Nüesch, Stephan, 2010. "Prediction accuracy of different market structures -- bookmakers versus a betting exchange," International Journal of Forecasting, Elsevier, vol. 26(3), pages 448-459, July.
    3. Zak, Thomas A & Huang, Cliff J & Siegfried, John J, 1979. "Production Efficiency: The Case of Professional Basketball," The Journal of Business, University of Chicago Press, vol. 52(3), pages 379-92, July.
    4. Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    5. John M. Gandar & Richard A. Zuber & William H. Dare, 2000. "The Search for Informed Traders in the Totals Betting Market for National Basketball Association Games," Journal of Sports Economics, , vol. 1(2), pages 177-186, May.
    6. David J. Berri, 1999. "Who is 'most valuable'? Measuring the player's production of wins in the National Basketball Association," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 411-427.
    7. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    8. John M. Gandar & William H. Dare & Craig R. Brown & Richard A. Zuber, 1998. "Informed Traders and Price Variations in the Betting Market for Professional Basketball Games," Journal of Finance, American Finance Association, vol. 53(1), pages 385-401, 02.
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