ANALYZING AND PREDICTING THE SUCCESS OF NBA SECOND-ROUND PICKS USING ADVANCED COLLEGE PERFORMANCE METRICS
Open Access
Author:
Yang, Jay
Area of Honors:
Statistics
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Andrew John Wiesner, Thesis Supervisor Matthew D Beckman, Thesis Honors Advisor
Keywords:
statistics basketball regression NBA NBA Draft college basketball
Abstract:
The second-round is often an overlooked portion of the NBA draft, and second-round draft picks are often viewed as peripheral assets in trades. However, finding key contributors in the second-round can go a long way towards building a successful team. This thesis aims to identify key statistics from a player’s college career that may be able to help predict a player’s success in the NBA. To do so, various methods of regression will be used with a variety of advanced college basketball statistics as potential predictor variables to produce models for both longevity and productivity in the NBA. In addition, these models will be applied to previous second-round picks to see how well they predict the performance of well-known NBA second- rounders. In producing these models, win shares, and more specifically, defensive win shares, were found to be a significant predictor for multiple responses, providing evidence that defensive win shares in college may be a crucial advanced statistic to key in when searching for value in the second-round of the NBA draft. In addition, the success of forwards also seemed to differ from the success of non-forwards. When applied to past second-round picks, these models predicted favorable outcomes for notable NBA second-round picks, such as Hassan Whiteside and Draymond Green.
