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

TL;DR

This study explores whether facial images analyzed through deep learning can predict NBA players' career success, offering a novel approach beyond traditional statistical methods.

Contribution

It introduces a deep learning framework using facial images and CNNs to predict NBA prospects' career trajectories, a new perspective in talent evaluation.

Findings

Potential correlation between facial features and athletic talent

Deep learning models show promise in predicting career success

Facial analysis could complement existing evaluation methods

Abstract

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.