Build an AI/ML NBA Basketball Analysis system with YOLO, OpenCV, and Python

Hello. In this video, I'll teach you how to build this basketball analysis project from scratch. This project is able to extract a lot of good insights from a basketball game using multiple AI models like YOLO for object detection. This includes the number of passes and interceptions each team made, the ball acquisition percentage of each team, the

distance and speed of each player, and even translate the camera view into a tactical top-down view, allowing the system to fully understand what's happening on the court. We'll start by detecting players and balls using YOLO. YOLO is one of the best object detection models right now. We'll also be training it or fine-tuning it to improve the

performance of out- of-the-box readymade models. Then we'll assign players to teams based on their jersey color using a zeroot image classifier that identifies the jersey color from the cropped player image and the class labels we provide. Zerootshot classification is a great way to classify images on a specific use case without needing to train a whole model.

With just these two models, we can track which player gains possession of the ball, allowing us to calculate each team's ball acquisition percentage. We can also detect passes and interceptions by analyzing changes in the ball possession between players. We'll take it a step further by training our own keyoint detector to identify landmarks on the court in each

frame. Then using those cord key points, we'll work on perspective transformation that takes in a camera's distorted viewpoint of the 3D world and accurately represent the scene's depth and perspective into a clean top-down tactical map. This mapping translates pixel positions into real world court coordinates, so we know a player speed and distance covered in