Abstract: Evaluating shooting ability is a critical component of player comparison and player development. However, players are often evaluated on a limited number of shots, exposing assessment to high variation and inaccurate, anecdotal conclusions. The aim of this paper is to explore the potential of high-resolution shot data to improve shooter evaluation. Using over 22 million shots captured in high-resolution by Noahlytics, we reveal previously hidden systematic biases in entry left-right and entry depth from all positions on the court. Then, we focus on the high-resolution shot data from 509 NBA, college and high school players to train a machine-learning algorithm that predicts shooting ability from 25-shot sessions. The algorithm outperforms conventional methods and better ranks players by skill-level. We conclude by encouraging coaches and players to re-evaluate their largely anecdotal assessment methods and implement more effective, data-driven methods to enhance shooter development and shooter ranking.