Leveraging machine learning to predict factors that drive successful basketball team formation

El-Hajj, Mohamad; Kwon, Benjamin; Jethro Infante, Craeg; Steed, Jackson; Gore, Victor; Phan, Nhi; Elmorsy, Mohammed; Pang, Xiaodan

Leveraging machine learning to predict factors that drive successful basketball team formation

Author

El-Hajj, Mohamad

Kwon, Benjamin

Jethro Infante, Craeg

Date

2025

Keywords

NCAA basketball players, NBA, draft potential, basketball analytics

Abstract (summary)

This study delves deep into the key factors affecting the likelihood of NCAA basketball players getting drafted into the NBA. The study highlights the importance of offensive metrics such as points scored and offensive ratings in predicting an NCAA player’s chances of being drafted into the NBA by utilizing an unsupervised learning clustering model and a supervised decision tree model. This underscores the significance of offensive statistics in a player’s skill set and suggests that players and coaches should prioritize improving these metrics to enhance a player’s draft potential. The study found that defensive metrics like defensive ratings and blocks have less impact on overall draft potential than offensive metrics. A crucial point to note is that a team’s success often relies on having its top players actively participating on the court. This research enhances our understanding of the factors influencing the draft prospects of NCAA basketball players. It underscores the advancement of basketball analytics and paves the way for further research on player performance metrics and their influence on the scouting and selection of professional athletes.