The ABC Sports Drone capstone team is an extension of the ABC Drone Project which is a group spearheaded by client Charles Kerr and in conjunction with the VT Club Ultimate team, Burn. The goal of the project as a whole is to provide high-quality footage and streaming of amateur sports to the masses. This capstone team is a subsection of the ABC Drone Project that has been tasked with creating software solutions and developing new techniques to help push this drone project to fruition. This report covers the progress of the capstone team in developing new routines for the drone, and the pivots that have been introduced as the team has received new data. The first goal that was tackled was identifying players on a field from an endzone-to-endzone view. This started with the analyzing of contours in addition to their position and attributes to determine if a contour was a player. Artifacts from off the field of play proved to be greatly troublesome, so a field bounding solution was created to eliminate as many artifacts as possible that were not on the field of play. Fairly good accuracy was achieved with this method (~75%), but the goal was set at 85%+ accuracy for identification. After experimenting with motion-detection and object persistence, the best course of action seemed to be identification via a convolutional neural network. No datasets were available that matched the application of this network, so an original dataset needed to be created. An application was developed that allowed for fairly quick extraction of data from sample videos. This data was fed to the neural network and constantly yields around 94% identification accuracy. Although the accuracy is high, it reduces frame rates to approximately 1 FPS. Some market interviews with actual coaches revealed a larger interest in post-processing capability than live-identification, so the client decided to pivot. A system that allows for speed-editing of footage has been developed, and a (proof of concept) companion application will allow coaches to easily track stats and pre-edit film via a GUI. The speed editing program takes in the footage and allows the coach to use a video game controller to create quick cuts to eliminate down time, as well as pan, tilt, and zoom on the footage to ensure the action is always framed. The edits are recorded in an edit-decision-list (EDL) file which is then sent in conjunction with the video file to Amazon Web Services. AWS takes the EDL file and original video and returns a fully-edited game film. With this method, a 90 minute game can be edited in 5 minutes or less. If coaches are recording stats during the game, the footage will also be annotated with important plays which are recorded on a similar EDL for gameplay statistics. Players will then have access to a program that will allow them to click their name to see the timestamps of all of their highlights.