Can a face tell us anything about an NBA prospect? - A Deep Learning approach

Statistical analysis and modeling is becoming increasingly popular in professional sports organizations. Sophisticated methods and models of sports talent evaluation have been created for this purpose. In this research, we present a different perspective from the dominant tactic of statistical data analysis. We deploy 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 in every draft class since 1990. We then divided the players into five different quality classes based on their NBA career. Next, we trained popular image classification models in our data and conducted a series of tests in an attempt to create models that will provide 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.