  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.
