Positional salaries and performance: A linear regression analysis of investment efficiency in the NBA

Abstract

In many previous dissertation studies, the relationship between NBA player performance and team wins and losses has been demonstrated. At the same time, many papers discussed the relationship between players performance at different NBL positions, game wins and losses, and their salaries. This will help team managers visualize the players that will better enhance their team based on their salary level through data. There needs to be more literature on this specific task in the NBA. While the prominence of star players often drives a significant portion of a teams payroll, the distribution of salaries across various positions can play a critical role in a teams overall performance. This study delves into the intricate relationship between positional spending and associated on-field metrics. This paper utilized a linear regression model to investigate the correlation between a teams investment in individual positions and the resultant win shares. Preliminary findings suggest that while star players undeniably uplift a teams performance and revenue through various streams, a balanced allocation strategy catering to all positions may yield optimal results. This research aims to offer teams a data-driven perspective to optimize their investment and achieve maximum returns under the constraints of the salary cap, fostering a competitive edge in the league.