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Forecasting exact scores in National Football League games

Author

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  • Baker, Rose D.
  • McHale, Ian G.

Abstract

The paper presents a point process model for predicting exact end-of-match scores in the premier league of American football, the National Football League. The hazards of scoring are allowed to vary with team statistics from previous games and/or the bookmaker point spread and over-under. The model is used to generate out-of-sample forecasts, which are evaluated using several criteria, including a Kelly betting strategy. In predicting the results of games, the model is marginally outperformed by the betting market. However, when it is used to forecast exact scores, the model proves to do at least as well as the market.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfor:v:29:y:2013:i:1:p:122-130
    DOI: 10.1016/j.ijforecast.2012.07.002
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    References listed on IDEAS

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    Cited by:

    1. Fentaw Abegaz & Ernst Wit, 2015. "Copula Gaussian graphical models with penalized ascent Monte Carlo EM algorithm," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(4), pages 419-441, November.

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    Keywords

    Sports; Predictions; Point processes;

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