Título: |
RECOMMENDING PLAYER SIGNINGS TO FOOTBALL TEAMS: A DATA-DRIVEN OPTIMIZATION APPROACH
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Instituição: |
PONTIFÃCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO - PUC-RIO
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Autor(es): |
PEDRO MEDEIROS HAMACHER
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Colaborador(es): |
SILVIO HAMACHER - Orientador
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Data da catalogação: |
11 11:10:20.000000/01/2024 |
Tipo: |
SENIOR PROJECT
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Idioma(s): |
ENGLISH - UNITED STATES |
Referência [pt]: |
https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/DEI/serieConsulta.php?strSecao=resultado&nrSeq=65859@1 |
Referência [en]: |
https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/DEI/serieConsulta.php?strSecao=resultado&nrSeq=65859@2 |
Referência DOI: |
https://doi.org/10.17771/PUCRio.acad.65859
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Resumo:
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Football teams spend billions of dollars yearly signing new players to improve their squad and
to fill identified areas of need. Despite having available complete statistics from players all
around the globe, teams often do not use this data at its total capacity to identify potential
signings. Looking to fill this void, this work presents models intended to suggest to teams
which players should they buy and sell to fulfill their specific needs, adequately assemble the
roster and gain a competitive edge. A stochastic two-stage Mixed Integer Linear
Programming model is presented to optimize a team’s roster choices considering their budget,
desired attributes and absences throughout the season due to injury or suspensions. A data-science framework is also proposed for data collection and treatment to input it into a data-driven model. The framework is applied to real-world data from top leagues and some case
studies are presented in order to showcase its results and roster suggestions.
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