zostaff

zostaff / football-analyze

Public

football gambling project

45
6
100% credibility
Found Apr 29, 2026 at 45 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

This repository provides a Python script to analyze soccer videos by detecting and tracking players, referees, and the ball, assigning teams by jersey colors, compensating for camera movement, transforming perspectives for accurate measurements, and visualizing speeds, distances, and ball possession.

How It Works

1
🔍 Discover the Tool

You find a handy tool online that analyzes soccer videos to track players and the ball like a coach would.

2
📥 Grab Samples

Download the example soccer video and the smart detection file from the provided links to get started.

3
📁 Add Your Video

Put your own soccer match video into the input folder so the tool can work on it.

4
⚡ Start Analysis

Run the main program, and it automatically detects players, referees, teams, and the ball while adjusting for camera shakes.

5
⏳ Watch It Process

Sit back as it calculates speeds, distances in real meters, and who controls the ball most.

🎥 Enjoy the Results

Open the new output video to see colorful tracks, speed labels, team possession percentages, and all the insights overlaid on your match.

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Star Growth

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AI-Generated Review

What is football-analyze?

This Python project lets you upload a football match video and get back an annotated output with player tracking, team assignments via jersey colors, ball possession stats, camera-adjusted positions, and real-world speeds/distances in km/h and meters. Built on YOLO for object detection, OpenCV for video processing, and supervision for tracking, it solves the hassle of manually analyzing football data from GitHub videos—perfect for a football analyzer AI that computes team ball acquisition percentages and helps analyze my football team without starting from scratch. You just need the trained model and sample input from Google Drive to run it.

Why is it gaining traction?

It stands out by transforming pixel movements into metric distances via perspective correction and optical flow, delivering user-ready visuals like speed overlays and possession bars—features rare in free football analyzers. Developers dig the end-to-end pipeline for football analyze and prediction, spitting out polished videos for football gambling apps or analyzed football predictions, without fiddling with raw detections. At 45 stars, it's hooking sports data tinkerers who want quick football data GitHub prototypes over clunky alternatives.

Who should use this?

Football analysts building prediction models or scouting apps, gambling site devs prototyping football gambling games with possession metrics, and hobby coaches reviewing matches to analyze my football team. Ideal for data scientists in sports betting needing a football analyzer app baseline, or fantasy football draft analyzers crunching player speeds from broadcast footage.

Verdict

Grab it for proofs-of-concept if you're into football gambling and me experiments—outputs are solid for demos—but the 1.0% credibility score, 45 stars, stub dependencies, and thin docs signal early-stage immaturity without tests or broad video support. Fork and harden it for production.

(198 words)

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