Can a face tell us anything about an NBA prospect? -- A Deep Learning approach
This work addresses talent evaluation for NBA teams by exploring a novel, non-statistical approach, though it is incremental as it applies existing deep learning methods to a new domain.
The study investigated whether facial features from images of NBA draftees could predict their career success, using a dataset of 1500 player images since 1990 and training pre-trained CNN models to classify players into five quality classes, with results indicating a potential correlation worth further exploration.
Statistical analysis and modeling is becoming increasingly popular for the world's leading organizations, especially for professional NBA teams. Sophisticated methods and models of sport talent evaluation have been created for this purpose. In this research, we present a different perspective from the dominant tactic of statistical data analysis. Based on a strategy that NBA teams have followed in the past, hiring human professionals, we deploy image analysis and Convolutional Neural Networks in an attempt to predict the career trajectory of newly drafted players from each draft class. We created a database consisting of about 1500 image data from players from every draft since 1990. We then divided the players into five different quality classes based on their expected NBA career. Next, we trained popular pre-trained image classification models in our data and conducted a series of tests in an attempt to create models that give reliable predictions of the rookie players' careers. The results of this study suggest that there is a potential correlation between facial characteristics and athletic talent, worth of further investigation.