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dc.rights.licensehttps://creativecommons.org/licenses/by-sa/2.5/ar/es_AR
dc.contributor.advisorRoccatagliata, Pabloes_Ar
dc.contributor.authorCicognini, Agustínes_AR
dc.date.accessioned2023-01-06T19:41:20Z
dc.date.available2023-01-06T19:41:20Z
dc.date.issued2021
dc.identifier.urihttps://repositorio.utdt.edu/handle/20.500.13098/11567
dc.description.abstractThis paper proposes a methodology to predict work in non-traumatic injuries in professional soccer players. The task to be solved is a classification problem of the player's status with a window of 72 hours. The data set used corresponds to records of complete training by the players of Belgrano de Córdoba professional soccer team of the first division of Argentina. The chosen model is GBM with an AUC of 0.7. Interpretation exercises based on SHAP are performed on the chosen model to analyze the characteristics that determine the model's predictions. In addition, possible extensions are proposed such as the use of the results of the model at the time of contractual negotiation given the estimated proportion of time that the player will spend outside due to injury and the economic cost of those absences given, at least, by the direct salary cost of that player. Another approach to the injury forecasting problem based on survival time models is also discussed.es_AR
dc.format.extent57 p.es_AR
dc.format.mediumapplication/pdfes_AR
dc.languageenges_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.subjectFútboles_AR
dc.subjectFootballes_AR
dc.subjectSocceres_AR
dc.subjectAnálisis de datoses_AR
dc.subjectPredicción tecnológicaes_AR
dc.subjectData Analysises_AR
dc.titleMaking the cut: forecasting non impact injuries in professional socceres_AR
dc.typeinfo:eu-repo/semantics/masterThesises_AR
thesis.degree.nameMaster in Management + Analyticsen
thesis.degree.grantorUniversidad Torcuato Di Tellaes_Ar
thesis.degree.grantorEscuela de Negocioses_Ar
dc.subject.keywordNon-traumatic injuryes_AR
dc.subject.keywordMachine Learninges_AR
dc.subject.keywordSurvival analysises_AR
dc.subject.keywordSHAPes_AR
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_AR


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