The National Basketball Association (NBA) is the highest form of professional basketball in the world in terms of competition and revenue. The financial investment into each player increases each season, not only in terms of salary but also into their care via support staff (Athletic Trainers, Strength and Conditioning Coaches), Nutrition, etc. When teams have deep playoffs runs, their parent organizations stand to make large amounts of revenues from television deals, ticket sales, and apparel. It would seem logical that keeping their players healthy would be a high priority and this aligns with the current trend into popular concepts like workload management. Workload management seeks to find the optimal window of work and rest for players both on and off the court. In accordance with these concepts, this paper used Machine Learning (ML) to explore whether two season of data (2021-2022, 2022-2022) injury and final rankings could accurately predict the final rankings of the 2023-2024 season. Using a Support Vector Regression (SVE), the relationships between games missed-rankings, and season-ranking were explored and found to be weak (R^2 score: .0004, MSE: 76.9). Using injuries and
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