Capitalizing on market inefficiencies with neural networks: a case study in football betting

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dc.contributor.MentorGómez Seeber, Matías José
dc.creator.AutorArana, Joaquín Matías
dc.date.accessioned2025-05-22T15:31:13Z
dc.date.available2025-05-22T15:31:13Z
dc.date.issued2024-12
dc.descriptionFil: Arana, Joaquín Matías. Universidad de San Andrés. Departamento de Economía; Argentina.
dc.description.abstractThis paper introduces a machine learning approach for profiting from sports betting markets, which are non-arbitraged inefficient markets that present opportunities for exploiting discrepancies between bookmaker odds and actual probabilities of outcomes. I will develop a Neural Network Model that accurately forecasts the events of two different football competitions: The English Premier League and The Argentine’s First Division. A betting strategy based on Markow’s portfolio theory is developed to exploit inconsistencies and maximize yield over time. The findings suggest that machine learning algorithms, like the one presented in this study, could potentially enable consistent profitability in sports betting by identifying inefficiencies in bookmaker odds.
dc.formatapplication/pdf
dc.identifier.urihttps://repositorio.udesa.edu.ar/handle/10908/25214
dc.languageeng
dc.publisherUniversidad de San Andrés. Departamento de Economía
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleCapitalizing on market inefficiencies with neural networks: a case study in football betting
dc.typeTesis
dc.typeinfo:eu-repo/semantics/bachelorThesis
dc.typeinfo:ar-repo/semantics/tesis de grado
dc.typeinfo:eu-repo/semantics/updatedVersion
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