# 12-099/III (2012-09-27)

Author(s)
Siem Jan Koopman, VU University Amsterdam; Rutger Lit, VU University Amsterdam
Keywords:
Betting, Importance sampling, Kalman filter smoother, Non-Gaussian multivariate time series models, Sport statistics
JEL codes:
C32, C35

This discussion paper led to a publication in Journal of the Royal Statistical Society Series A, 2015, 178(1), 167-186.

Attack and defense strengths of football teams vary over time due to changes in the teams of players or their managers. We develop a statistical model for the analysis and forecasting of football match results which are assumed to come from a bivariate Poisson distribution with intensity coefficients that change stochastically over time. This development presents a novelty in the statistical time series analysis of match results from football or other 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 2010/11 and 2011/12 seasons of the English Premier League. We show that our statistical modeling framework can produce a significant positive return over the bookmaker's odds.