The Prediction Market for the Australian Football League
AbstractThe purpose of this paper is to make a novel contribution to the literature on the prediction market for the Australian Football League, the major sports league in which Australian Rules Football is played. Taking advantage of a novel micro-level data set which includes detailed per-game player statistics, predictions are presented and tested out-of-sample for the simplest kind of bet: fixed odds win betting. It is shown that player-level statistics may be used to yield very modest profits net of transaction costs over a number of seasons, provided some more global variables are added to the model. A comparison of different specifications of the linear probability model (LPM) versus conditional logit (CLOGIT) regressions reveals that the LPM usually outperforms CLOGIT in terms of profitability. It is further shown that adding significant variables to a regression specification which is clearly superior in econometric terms may reduce the efficacy of the prediction and thus profits.
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Bibliographic InfoPaper provided by Department of Economics, Bar-Ilan University in its series Working Papers with number 2011-15.
Date of creation: Mar 2011
Date of revision:
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Postal: Faculty of Social Sciences, Bar Ilan University 52900 Ramat-Gan
Phone: Phone: +972-3-5318345
Web page: http://econ.biu.ac.il
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-06-11 (All new papers)
- NEP-DCM-2011-06-11 (Discrete Choice Models)
- NEP-FOR-2011-06-11 (Forecasting)
- NEP-SPO-2011-06-11 (Sports & Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Ryall, Richard & Bedford, Anthony, 2010. "An optimized ratings-based model for forecasting Australian Rules football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 511-517, July.
- Roger C. Vergin & Michael Scriabin, 1978. "Winning Strategies for Wagering on National Football League Games," Management Science, INFORMS, vol. 24(8), pages 809-818, April.
- Sargent, Jonathan & Bedford, Anthony, 2010. "Improving Australian Football League player performance forecasts using optimized nonlinear smoothing," International Journal of Forecasting, Elsevier, vol. 26(3), pages 489-497, July.
- James D. Dana & Michael M. Knetter, 1994. "Learning and Efficiency in a Gambling Market," Management Science, INFORMS, vol. 40(10), pages 1317-1328, October.
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