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Issues in sports forecasting

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Author Info

  • Stekler, H.O.
  • Sendor, David
  • Verlander, Richard

Abstract

A large amount of effort is spent on forecasting the outcomes of sporting events, but few papers have focused exclusively on the characteristics of sports forecasts. Instead, many papers have been written about the efficiency of sports betting markets. As it turns out, it is possible to derive a considerable amount of information about the forecasts and the forecasting process from studies that have tested the markets for economic efficiency. Moreover, the huge number of observations provided by betting markets makes it possible to obtain robust tests of various forecasting hypotheses. This paper is concerned with a number of forecasting topics in horse racing and several team sports. The first topic involves the type of forecast that is made: picking a winner or predicting whether a particular team will beat the point spread. Different evaluation procedures will be examined and alternative forecasting methods (models, experts, and the market) compared. The paper also examines the evidence with regard to the existence of biases in the forecasts, and concludes by discussing the applicability of these results to forecasting in general.

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Bibliographic Info

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

Volume (Year): 26 (2010)
Issue (Month): 3 (July)
Pages: 606-621

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Handle: RePEc:eee:intfor:v:26:y::i:3:p:606-621

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

Related research

Keywords: Sports forecasting Betting markets Efficiency Bias Sports models;

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References

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Cited by:
  1. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
  2. Baker, Rose D. & McHale, Ian G., 2013. "Forecasting exact scores in National Football League games," International Journal of Forecasting, Elsevier, vol. 29(1), pages 122-130.
  3. Štrumbelj, Erik & Vračar, Petar, 2012. "Simulating a basketball match with a homogeneous Markov model and forecasting the outcome," International Journal of Forecasting, Elsevier, vol. 28(2), pages 532-542.
  4. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.

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