This project sought to develop analytical methods of analyzing the performance of wide receivers by using the NFL’s granular player tracking data. Instead of relying on performance statistics like yards, catches, and touchdowns, player tracking data can trace the movements of a player throughout the play. This allows us to identify metrics like separation from the nearest defender throughout the route and at the time of the catch. This method emphasizes the process of good wide receiver play, while de-emphasizing the results, adding an extra dimension to assessing performance at the position. Other parts of the project used unsupervised learning methods to classify defensive formations and coverages based on player tracking data, as well as which route was run by the receiver. This allows for dimensional analysis on the performance metrics, which provide even greater insight into a receiver’s strengths. Finally, an optimization model was constructed to maximize the value of a 3-receiver corps by maximizing their performance and minimizing the financial impact of their salary. Overall, this project sought to provide new ways of assessing wide receiver performance based on technological advancements in data collection.