PARX model for football matches predictions
Giovanni Angelini () and
Luca De Angelis ()
No 2, Quaderni di Dipartimento from Department of Statistics, University of Bologna
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.
Keywords: Sports forecasting; Density forecasts; Count data; Poisson autoregression; Bet- ting market. Previsioni sportive; Previsioni di densità; Dati di conteggio; Autoregressione di Poisson; Mercato delle scommesse. (search for similar items in EconPapers)
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Journal Article: PARX model for football match predictions (2017)
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