A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League
type="main" xml:id="rssa12042-abs-0001"> <title type="main">Summary</title> <p>We develop a statistical model for the analysis and forecasting of football match results which assumes a bivariate Poisson distribution with intensity coefficients that change stochastically over time. The dynamic model is a novelty in the statistical time series analysis of match results in team sports. Our treatment is based on state space and importance sampling methods which are computationally efficient. The out-of-sample performance of our methodology is verified in a betting strategy that is applied to the match outcomes from the 2010–2011 and 2011–2012 seasons of the English football Premier League. We show that our statistical modelling framework can produce a significant positive return over the bookmaker's odds.
Year of publication: |
2015
|
---|---|
Authors: | Koopman, Siem Jan ; Lit, Rutger |
Published in: |
Journal of the Royal Statistical Society Series A. - Royal Statistical Society - RSS, ISSN 0964-1998. - Vol. 178.2015, 1, p. 167-186
|
Publisher: |
Royal Statistical Society - RSS |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Intraday Stock Price Dependence using Dynamic Discrete Copula Distributions
Koopman, Siem Jan, (2015)
-
Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model
Koopman, Siem Jan, (2015)
-
Koopman, Siem Jan, (2012)
- More ...