In recent years, data analysis has revolutionized the way basketball is understood and managed, with the NBA leading this transformation. The development of advanced metrics and machine learning tools has enabled more accurate evaluations and predictions in the sport. Despite the success of current metrics like the Player Efficiency Rating (PER) and Pythagorean Wins (PW), they often fail to provide a complete picture due to their reliance on production data, which can lead to data leakage. This study aims to introduce new efficiency-based metrics that mitigate the risk of data leakage and improve the accuracy of predicting NBA regular season wins. The new metrics adjust traditional statistics by incorporating the team’s pace and comparing them to opponents, ensuring a more reliable prediction framework. Using machine learning models such as K-nearest neighbors, random forest, gradient boosting regression, and support vector regression, the study evaluates the predictive accuracy of the new metrics against traditional data. Results indicate that the new metrics significantly enhance prediction accuracy, particularly in random forest and gradient boosting models. This research provides a more robust methodology for predicting team performance, aiding coaches, scouts, and management in their decision-making processes.