Let's build a Football AI system to dig deeper into match stats! We'll use computer vision and machine learning to track players, determine which team is which, and even calculate stuff like ball possession and speed. This tutorial is perfect if you want to get hands-on with sports analytics and see how AI can take your football analysis to the next level.

Chapters: 

- 00:00:00 Football (Soccer) AI: The Next Level
- 00:00:58 Architectural Blueprint: Models & Tools for Football AI
- 00:03:14 YOLOv8 Fine-Tuning: Optimizing for Football Object Detection
- 00:12:22 Deploying YOLOv8 with Inference
- 00:27:37 ByteTrack: Robust Multi-Object Tracking
- 00:29:38 Embedding Analysis: Clustering Players with SigLIP & UMAP
- 00:51:46 Perspective Transformation: Homography Fundamentals
- 00:53:03 YOLOv8x-pose Training: Precise Pitch Landmark Detection
- 01:01:24 Keypoint Inference: Real-Time Pitch Understanding
- 01:06:27 Homography Application: Virtual Lines & Field Overlay
- 01:13:23 Top-Down Projection: Creating a Tactical Radar View
- 01:22:04 Spatial Analysis: Ball Territory
- 01:26:34 Implementation Challenges
- 01:28:29 Beyond the Basics: What Else is Possible?

Resources: 

- Roboflow: https://roboflow.com

 🔴 Community Session Aug 29th, 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET: https://www.youtube.com/watch?v=Xwou5qO--vY

- ⚽ Roboflow Sports repository: https://github.com/roboflow/sports 

- 🏞️ ball, players, and referees detection dataset: https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc 
- 🏞️ pitch keypoints detection dataset: https://universe.roboflow.com/roboflow-jvuqo/football-field-detection-f07vi 

- 📓 ball, players, and referees detection model training notebook: https://colab.research.google.com/github/roboflow/sports/blob/main/examples/soccer/notebooks/train_player_detector.ipynb 
- 📓 pitch keypoints detection model training notebook: https://colab.research.google.com/github/roboflow/sports/blob/main/examples/soccer/notebooks/train_pitch_keypoint_detector.ipynb
- 📓 football AI notebook: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/football-ai.ipynb

- ⚽ Ball Tracking in Sports with Computer Vision blog post: https://blog.roboflow.com/tracking-ball-sports-computer-vision/ 
- ⚽ Camera Calibration in Sports with Keypoints blog post: https://blog.roboflow.com/camera-calibration-sports-computer-vision/ 

Stay updated with the projects I'm working on at https://github.com/roboflow and https://github.com/SkalskiP! ⭐

YouTube

This post provides a comprehensive tutorial on enhancing football AI analysis using Python. It covers detecting and tracking players, ball, and referees on the pitch, using Sly embeddings to divide players into teams, and employing keypoint detection and homography to create advanced visualizations like radar views and Voronoi diagrams. The guide is approachable for those with basic Python knowledge, with models and data publicly available for ease of replication. The tutorial uses tools like YOLO V8, Google Colab, and the Roboflow platform for model training and deployment.

Football AI Tutorial: From Basics to Advanced Stats with Python

<p>“Before we start…” Oh yeah!🤘</p>
