NBA Player Salary Projections Based on Gradient Boost in 2022-23 Season

Abstract

The National Basketball Association (NBA) game has a high international profile, and the level of NBA players and salary projections can help clubs make the right decisions. This study introduces the research topic of predicting NBA salaries for the 2022-23 season. The proposed method utilizes a gradient boosting algorithm to analyze player performance metrics and historical salary data. The specific process includes data preprocessing, feature selection, model selection and evaluation. The proposed method is evaluated through a large number of experiments, and the gradient boosting model outperforms other methods in terms of Mean-Square Error (MSE) and R-squared values. This study demonstrates the potential of machine learning in predicting the salary of NBA players. The results showed that the gradient boosting model achieved an R-squared value of 0.74, indicating that the model is proficient in capturing the relationship between player performance metrics and salary. Clubs can use the model to rationalize their overall layout by capturing the relationship between player performance metrics and salary.