Exploring the Impact of Various Factors on the Salaries of Players in the National Basketball Association
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Keywords
NBA player salary, random forest model, multiple linear regression model, performance on the field, team winning percentage
Abstract
This study focuses on the factors affecting NBA player salaries. On the basis of multidimensional data from 288 players in the 2022--2023 season, a random forest model and multiple linear regression model are used to explore the impacts of individual competitive level, implicit traits, commercial endorsement value, and institutional restrictions on salaries. Research has shown that a player’s performance in the field, especially their offensive contribution, is the core determinant of salary. Multiple linear regression validation reveals that scores and team win rates have a significant positive effect on salary, whereas the number of games has a negative effect. Defensive indicators and injury absenteeism do not significantly affect player salaries. In addition, the team’s luxury tax and off-field commercial value have weak explanatory power for salaries, revealing that the current salary system is still centered around competitive performance. This research provides a quantitative basis for player salary negotiations, team salary structure optimization, and alliance policy adjustments. In the future, real-time game data and social media indicators can be further integrated to improve the prediction accuracy.
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