Leveraging Machine Learning for Accurate Prediction of NBA Player Salaries

Basketball players in the NBA are renowned for their talent, athleticism, and commitment to the game. NBA players may make enormous sums of money; however, they vary greatly. Rookie agreements begin at a lower price and go up following performance. NBA players’ pays are influenced by several factors. Because they influence games and the success of the club, exceptional players fetch larger compensation. This study employs Machine Learning (ML) techniques, including Lasso Regression and Random Forest Regression (RFR) models to analyze wage trends, enhanced by the Slime Mould Algorithm (SMA) and Artificial Rabbit Optimization (ARO) for accuracy. The goal is to forecast NBA player salaries based on their performance. Upon analysis, the RFAR model emerged as the top-performing model across the train, test, and validation phases, showcasing impressive R2 values of 0.987 for both the train and test phases and 0.970 during validation. In contrast, the LASSO model was identified as the weakest performer, with R2 values of 0.906 during training, 0.887 in the validation phase, and 0.910 R2 values during the testing phase. In conclusion, RFAR is the most effective model for predicting NBA players’ salaries.