Journal Article

A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League

Lit, Rutger, Koopman, Siem Jan

2015

Overview

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.

• Publisher: Blackwell Publishing Ltd,John Wiley & Sons Ltd,Oxford University Press
• Subject: Betting / Computational efficiency / Datasets / Distribution / Dynamic models / Football / Forecasting / Forecasting models / Forecasting techniques / Importance sampling / Kalman filter smoother / Mathematical models / Maximum likelihood estimation / Non-Gaussian multivariate time series models / Poisson distribution / Poisson distributions / Professional soccer / Sampling / Sampling techniques / Soccer / Sport statistics / Sports / State vectors / Statistical analysis / Statistical discrepancies / Statistical forecasts / Statistical models / Statistics / Stochastic models / Team sports / Time series / Time series analysis / Time series forecasting / Time series models