Profiting from arbitrage and odds biases of the European football gambling market

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Published: Aug 28, 2013
DOI: https://doi.org/10.5750/jgbe.v7i2.630
Keywords:
betting market favourite-longshot bias football betting profit margin soccer betting sports betting sports gambling

Main Article Content

Anthony Costa Constantinou
Queen Mary, University of London
Norman Elliott Fenton
Queen Mary, University of London

Abstract

A gambling market is usually described as being inefficient if there are one or more betting strategies that generate profit, at a consistent rate, as a consequence of exploiting market flaws. This paper examines the online European football gambling market based on 14 European football leagues over a period of seven years, from season 2005/06 to 2011/12 inclusive, and takes into consideration the odds provided by numerous bookmaking firms. Contrary to common misconceptions, we demonstrate that the accuracy of bookmakers' odds has not improved over this period. More importantly, our results question market efficiency by demonstrating high profitability on the basis of consistent odds biases and numerous arbitrage opportunities.

Article Details

Issue
Vol. 7 No. 2 (2013)
Section
Articles
Author Biographies

Anthony Costa Constantinou, Queen Mary, University of London

PostDoctoral Research Fellow, 1: Risk and Information Management (RIM) Research Group, Department of Electronic Engineering and Computer Science, Queen Mary, University of London2: Forensic Mental Health Research Group, Centre for Psychiatry, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary, University of LondonRisk and Information Management (RIM) Research Group,            Department of Electronic Engineering and Computer Science,            Queen Mary, University of London, UK, E1 4NS

Norman Elliott Fenton, Queen Mary, University of London

Professor of Risk Information Management, Director of Risk and Information Management (RIM) Research Group, Department of Electronic Engineering and Computer Science, Queen Mary, University of London, UK, E1 4NS

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