Analysis of NBA player salary based on multiple linear regression model

Abstract

Abstract. This study explores the use of machine learning techniques to predict NBA player salaries. Traditional salary evaluation methods often rely on subjective expert judgment, whereas data-driven approaches can provide more objective and accurate predictions. With the advancement of data analysis techniques and machine learning algorithms, it is now feasible to predict player salaries based on performance data. This study employs advanced statistical and machine learning techniques to analyze detailed player performance data, including points scored, rebounds, assists, steals, and blocks, to establish a data- and algorithm-based salary prediction model. This model can assist team management in making more scientific decisions during contract negotiations and player acquisitions, thereby avoiding the overvaluation or undervaluation of players and achieving a more balanced and fair salary distribution. Accurate salary predictions help teams allocate their limited salary cap more effectively, optimizing budget management and enhancing overall team competitiveness. This study not only demonstrates the practical value of data analysis and machine learning methods in the sports field but also promotes the further development of data science in sports management. Additionally, the results of the prediction model can provide valuable references for fans and media, enhancing their understanding of player salaries and team management strategies. This transparency enriches the overall fan experience and media coverage of the sport, facilitating more informed discussions and debates about player value and team decisions.