Forecasting football matches by predicting match statistics

Edward Wheatcroft

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper considers the use of observed and predicted match statistics as inputs to forecasts for the outcomes of football matches. It is shown that, were it possible to know the match statistics in advance, highly informative forecasts of the match outcome could be made. Whilst, in practice, match statistics are clearly never available prior to the match, this leads to a simple philosophy. If match statistics can be predicted pre-match, and if those predictions are accurate enough, it follows that informative match forecasts can be made. Two approaches to the prediction of match statistics are demonstrated: Generalised Attacking Performance (GAP) ratings and a set of ratings based on the Bivariate Poisson model which are named Bivariate Attacking (BA) ratings. It is shown that both approaches provide a suitable methodology for predicting match statistics in advance and that they are informative enough to provide information beyond that reflected in the odds. A long term and robust gambling profit is demonstrated when the forecasts are combined with two betting strategies.

Keywords: probability forecasting; sports forecasting; football forecasting; football predictions; soccer predictions (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2021-08-05
New Economics Papers: this item is included in nep-for, nep-isf, nep-ore and nep-spo
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Sports Analytics, 5, August, 2021, 7(2), pp. 77 - 97. ISSN: 2215-020X

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:111495

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