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PARX model for football matches predictions

Author

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  • Giovanni Angelini

    (Università di Bologna)

  • Luca De Angelis

    (Università di Bologna)

Abstract

We propose an innovative approach to model and predict the outcome of football matches based on the Poisson Autoregression with eXogenous covariates (PARX) model recently proposed by Agosto, Cavaliere, Kristensen and Rahbek (2016). We show that this methodology is particularly suited to model the goals distribution of a football team and provides a good forecast performance that can be exploited to develop a profitable betting strategy. The betting strategy is based on the idea that the odds proposed by the market do not reflect the true probability of the match because they may incorporate also the betting volumes or strategic price settings in order to exploit bettors’ biases. The out-of-sample performance of the PARX model is better than the reference approach by Dixon and Coles (1997). We also evaluate our approach in a simple betting strategy which is applied to the English football Premier League data for the 2013/2014 and 2014/2015 seasons. The results show that the return from the betting strategy is larger than 35% in all the cases considered and may even exceed 100% if we consider an alternative strategy based on a predetermined threshold which allows to exploit the inefficiency of the betting market.

Suggested Citation

  • Giovanni Angelini & Luca De Angelis, 2016. "PARX model for football matches predictions," Quaderni di Dipartimento 2, Department of Statistics, University of Bologna.
    • Handle: RePEc:bot:quadip:wpaper:132
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    File URL: http://amsacta.unibo.it/id/eprint/5408
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    Cited by:

    1. da Costa, Igor Barbosa & Marinho, Leandro Balby & Pires, Carlos Eduardo Santos, 2022. "Forecasting football results and exploiting betting markets: The case of “both teams to score”," International Journal of Forecasting, Elsevier, vol. 38(3), pages 895-909.
    2. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    3. Koopman, Siem Jan & Lit, Rutger, 2019. "Forecasting football match results in national league competitions using score-driven time series models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 797-809.
    4. Andrei Shynkevich, 2022. "Informational efficiency of football transfer market," Economics Bulletin, AccessEcon, vol. 42(2), pages 1032-1039.
    5. Raffaele Mattera, 2023. "Forecasting binary outcomes in soccer," Annals of Operations Research, Springer, vol. 325(1), pages 115-134, June.
    6. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos & Verousis, Thanos, 2020. "A conditional fuzzy inference approach in forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 196-216.
    7. Lu, Ye & Suthaharan, Neyavan, 2023. "Electricity price spike clustering: A zero-inflated GARX approach," Energy Economics, Elsevier, vol. 124(C).
    8. David Winkelmann & Marius Ötting & Christian Deutscher & Tomasz Makarewicz, 2024. "Are Betting Markets Inefficient? Evidence From Simulations and Real Data," Journal of Sports Economics, , vol. 25(1), pages 54-97, January.
    9. Šárka Hudecová & Marie Hušková & Simos G. Meintanis, 2024. "Specifications tests for count time series models with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(4), pages 1014-1040, December.
    10. Angelini, Giovanni & Candila, Vincenzo & De Angelis, Luca, 2022. "Weighted Elo rating for tennis match predictions," European Journal of Operational Research, Elsevier, vol. 297(1), pages 120-132.
    11. Alberto Arcagni & Vincenzo Candila & Rosanna Grassi, 2023. "A new model for predicting the winner in tennis based on the eigenvector centrality," Annals of Operations Research, Springer, vol. 325(1), pages 615-632, June.
    12. Giovanni Angelini & Giuseppe Cavaliere & Enzo D'Innocenzo & Luca De Angelis, 2022. "Time-Varying Poisson Autoregression," Papers 2207.11003, arXiv.org.

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