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Journal of Sports Analytics
Article . 2021 . Peer-reviewed
License: CC BY NC
Data sources: Crossref
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Journal of Sports Analytics
Article
License: implied-oa
Data sources: UnpayWall
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Journal of Sports Analytics
Article . 2021
Data sources: mEDRA
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https://dx.doi.org/10.48550/ar...
Article . 2020
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Forecasting football matches by predicting match statistics

Authors: Wheatcroft, Edward;

Forecasting football matches by predicting match statistics

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.

Country
United Kingdom
Related Organizations
Keywords

FOS: Computer and information sciences, soccer predictions, football forecasting, sports forecasting, probability forecasting, football predictions, Applications (stat.AP), Statistics - Applications

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    11
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
Green
Published in a Diamond OA journal