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dc.contributor.advisorChase, Christina
dc.contributor.authorCalvetti Jr., Paul G.
dc.date.accessioned2023-07-31T19:56:51Z
dc.date.available2023-07-31T19:56:51Z
dc.date.issued2023-06
dc.date.submitted2023-06-06T16:34:59.081Z
dc.identifier.urihttps://hdl.handle.net/1721.1/151656
dc.description.abstractBuilding a successful National Football League (NFL) team is a challenging task, requiring front offices to balance player selection and compensation while operating under a salary cap constraint. The salary cap represents the maximum amount a team can spend on player salaries in a given season. Effective team construction entails strategic allocation of resources across different positions to maximize performance within this budget. This paper focuses on the critical aspect of allocating salary cap resources among offensive positions to maximize team success. We introduce a novel model that considers the interplay between players at different offensive positions, as well as the variations in salaries and performance levels observed between players under rookie and veteran contracts. By framing the allocation challenge as a constrained optimization problem, we aim to help teams maximize their points per game while staying within the salary cap limit. Our model’s predictions enable us to identify the optimal distribution of resources across offensive positions, providing valuable insights for NFL front offices as they seek to allocate their salary cap to achieve maximum offensive performance and increase their chances of success on the field.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright retained by author(s)
dc.rights.urihttps://rightsstatements.org/page/InC-EDU/1.0/
dc.titleOptimizing the Allocation of Capital Among Offensive Positions in the NFL
dc.typeThesis
dc.description.degreeM.Eng.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Engineering in Electrical Engineering and Computer Science


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