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Forecasting National Football League Game Outcomes Relative to Betting Spreads

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  • William Mallios

    (California State University, Fresno)

Abstract

Cointegrated time processes measuring NFL playoff game performances relative to the betting spreads are graphed in terms of candlestick charts and forecast in terms of autoregressive systems with time varying coefficients. Coefficients are modeled in terms of linear regressions on lagged shocks. Estimation is non Bayesian. Forecasts provide measures of market efficiency/inefficiency and outcome volatility. Risk assessment utilizes GARCH-type modeling in estimating volatility. Applications are presented for the New York Giants 2012 playoff games based on a data backlog of three years.

Suggested Citation

  • William Mallios, 2012. "Forecasting National Football League Game Outcomes Relative to Betting Spreads," Journal of Gambling Business and Economics, University of Buckingham Press, vol. 6(3), pages 1-16, December.
    • Handle: RePEc:buc:jgbeco:v:6:y:2012:i:3:p:1-16
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    References listed on IDEAS

    as
    1. William Mallios, 2012. "Dynamic Modeling Forecasts Of Equity Price Movements In Cases Of Insider Trading," Journal of Prediction Markets, University of Buckingham Press, vol. 6(1), pages 1-30.
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    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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