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A Bayesian approach to predict the number of soccer goals : Modeling with Bayesian Negative Binomial regression

This thesis focuses on a well-known topic in sports betting, predicting the number of goals in soccer games.The data set used comes from the top English soccer league: Premier League, and consists of games played in the seasons 2015/16 to 2017/18.This thesis approaches the prediction with the auxiliary support of the odds from the betting exchange Betfair. The purpose is to find a model that can create an accurate goal distribution. %The other purpose is to investigate whether Negative binomial distribution regressionThe methods used are Bayesian Negative Binomial regression and Bayesian Poisson regression. The results conclude that the Poisson regression is the better model because of the presence of underdispersion.We argue that the methods can be used to compare different sportsbooks accuracies, and may help creating better models.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-149028
Date January 2018
CreatorsBäcklund, JOakim, Nils, Johdet
PublisherLinköpings universitet, Statistik och maskininlärning, Linköpings universitet, Statistik och maskininlärning
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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