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The Prediction Market for the Australian Football League

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  • Adi Schnytzer

    (Department of Economics, Bar Ilan University)

Abstract

The 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.

Suggested Citation

  • Adi Schnytzer, 2011. "The Prediction Market for the Australian Football League," Working Papers 2011-15, Bar-Ilan University, Department of Economics.
    • Handle: RePEc:biu:wpaper:2011-15
    as

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    File URL: https://www2.biu.ac.il/soc/ec/wp/2011-15.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Zuber, Richard A & Gandar, John M & Bowers, Benny D, 1985. "Beating the Spread: Testing the Efficiency of the Gambling Market for National Football League Games," Journal of Political Economy, University of Chicago Press, vol. 93(4), pages 800-806, August.
    4. 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.
    5. Gandar, John, et al, 1988. " Testing Rationality in the Point Spread Betting Market," Journal of Finance, American Finance Association, vol. 43(4), pages 995-1008, September.
    6. James D. Dana & Michael M. Knetter, 1994. "Learning and Efficiency in a Gambling Market," Management Science, INFORMS, vol. 40(10), pages 1317-1328, October.
    7. Adi Schnytzer & Guy Weinberg, 2008. "Testing for Home Team and Favorite Biases in the Australian Rules Football Fixed-Odds and Point Spread Betting Markets," Journal of Sports Economics, , vol. 9(2), pages 173-190, April.
    8. Grant, Andrew & Johnstone, David, 2010. "Finding profitable forecast combinations using probability scoring rules," International Journal of Forecasting, Elsevier, vol. 26(3), pages 498-510, July.
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