Do Games Missed Predict Rankings in the NBA: A Machine Learning Approach

¹Chicago Bulls Basketball Club, USA

²Western Governors University, USA

*Corresponding author: Dr. Brian Serrano, DC, Ph.D., ATC, CSCS, RSCC, CPSS, Chicago Bulls Basketball Club & Western Governors University, USA, E-mail: [email protected]

Received Date: July 09, 2025

Published Date: September 03, 2025

Citation: Serrano B. (2025). Do Games Missed Predict Rankings in the NBA: A Machine Learning Approach. Mathews J Sports Med. 4(1):10.

Copyrights: Serrano B. © (2025).

ABSTRACT

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, 2022-2023) 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 rankings as the sole proxies for success, caution is warranted as human success is not binary. However, recent clinical evidence from high performance teams and the evolving style of the NBA may be pointing towards the merit of availability being a key controllable contributor to winning.

Keywords: Basketball, NBA, Machine Learning, Sports Medicine.