NBA Player Evaluation Using a Value-Performance Salary Index | VSPI

Abstract

This study aims to develop a new metric to analyze the value of NBA players by analyzing various statistical categories. The study incorporates data from the last three NBA seasons, including regular stats, advanced metrics, and salary information, to create a predictive assessment of player value. The methodology involves building datasets, generating visualizations, and applying machine learning techniques to uncover patterns and relationships within the dataset.

This study examines how these factors contribute to a player's overall impact on their team's success and financial worth to their organization. The results indicate that combining these data points allows for a more nuanced understanding of player value, going beyond traditional evaluation methods that often rely on singular statistics.

The study's findings reveal that the newly developed metric offers a robust tool for NBA teams to make more informed decisions regarding player signings and contracts. This metric can influence roster management and financial strategies in professional basketball by providing a monetary value for assessing the effectiveness of contracts and return on investment. Overall, this research highlights the importance of a data-driven approach in optimizing team performance and salary-cap allocation.