A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League
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 s...
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| Veröffentlicht in: | Journal of the Royal Statistical Society. Series A, Statistics in society Statistics in society, 2015-01, Vol.178 (1), p.167-186 |
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| container_end_page | 186 |
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| container_issue | 1 |
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| container_title | Journal of the Royal Statistical Society. Series A, Statistics in society |
| container_volume | 178 |
| creator | Koopman, Siem Jan Lit, Rutger |
| description | 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. |
| doi_str_mv | 10.1111/rssa.12042 |
| format | Article |
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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.</description><identifier>ISSN: 0964-1998</identifier><identifier>EISSN: 1467-985X</identifier><identifier>DOI: 10.1111/rssa.12042</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><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 ; State vectors ; Statistical analysis ; Statistical discrepancies ; Statistical forecasts ; Statistical models ; Statistics ; Stochastic models ; Time series ; Time series analysis ; Time series forecasting ; Time series models</subject><ispartof>Journal of the Royal Statistical Society. 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| fulltext | fulltext |
| identifier | ISSN: 0964-1998 |
| ispartof | Journal of the Royal Statistical Society. Series A, Statistics in society, 2015-01, Vol.178 (1), p.167-186 |
| issn | 0964-1998 1467-985X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1651411754 |
| source | Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current); Wiley Online Library Journals Frontfile Complete; EBSCOhost Business Source Complete |
| subjects | 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 State vectors Statistical analysis Statistical discrepancies Statistical forecasts Statistical models Statistics Stochastic models Time series Time series analysis Time series forecasting Time series models |
| title | A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League |
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