⚽ TechTactics – Football Talent Classifier

TechTactics is a web application designed to predict football player talent and value based on video input and player attributes. This tool is ideal for scouts, coaches, and analysts to evaluate football players.

Demo

Watch the demo here

📁 Project Structure

FrontendApp/
├── Styles0.css
├── event-optimization.html
├── index.html
├── login.html
├── player-value.html
├── scout.html
├── script.js
└── talent.html

models/
├── detect_talent_football.ipynb
├── football_talent_model.pkl
├── finalized_model.sav
└── player_value.ipynb

flask_backend.py
player_value.py
server.js
test.py
.gitignore
README.md
desktop.ini

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🧪 Backend Setup (Python)

The project uses Flask to serve the backend for both the talent prediction and player value prediction functionalities.

1. 🔧 Create a Virtual Environment (Optional but Recommended)

If you want to set up a virtual environment for Python dependencies, run the following commands:

python -m venv venv
# On Mac/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

2. 📦 Install Python Dependencies

Install the necessary Python packages using pip:

pip install flask flask-cors joblib numpy pandas opencv-python mediapipe

3. 🧠 Prepare the Models

Make sure the following model files exist in the models/ directory:

• models/football_talent_model.pkl
• models/football_talent_model.sav

These files contain the pre-trained machine learning models used for talent classification and player value prediction.

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🚀 Running the Backend

Flask Backend

To start the backend for both football talent prediction and player value prediction, run the following:

python flask_backend.py

This will run the Flask server at http://localhost:5001.

Player Value Prediction (Python)

To start the backend for player value prediction, run:

python player_value.py

This server will run at http://localhost:5002.

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🌐 Frontend Usage

The frontend is built using HTML, CSS, and JavaScript. It includes the following pages:

• Talent Prediction (talent.html): Allows users to upload a football video, which will be analyzed to determine if the player is "Talented" or "Not Talented".
• Player Value Prediction (player-value.html): Allows users to input player attributes like age, height, weight, etc., to predict the player's market value.

Opening the Frontend

To view the frontend:

1. Open the FrontendApp/index.html file directly in a browser.
2. Use an extension like Live Server to serve the pages if you prefer dynamic reloading during development.

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🌍 Optional Node.js Server (If you're using server.js)

1. 📦 Install Node.js Dependencies

If you're using Node.js for additional server features, install the dependencies:

npm install dotenv express multer mongoose

2. ⚙️ Create a .env File

Create a .env file in the root directory and add the following:

PORT=5000
MONGO_URI=your_mongodb_connection_string

3. 🚀 Run the Node Server

node server.js

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🎯 Features

• Football Talent Prediction: Upload and analyze football videos to classify a player's talent based on AI-powered classification.
• Player Value Prediction: Input player attributes (e.g., age, height, weight) and get a predicted market value.
• Pose Estimation: The system uses MediaPipe Pose to extract the football player's movements from video for talent prediction.
• Scalable: This system can be extended to support more functionalities such as user accounts, scouting dashboards, and detailed player history.

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💡 Future Improvements

• User Accounts & Scouting Dashboards: Allow scouts and coaches to create accounts and track player evaluation history.
• Player Statistics History: Add a feature to store and display player statistics over time.
• Pose Visualization: Improve the visual feedback of the pose estimation process for better understanding.
• Multilingual Support: Extend the UI to support multiple languages (Arabic/English).

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🧑‍💻 Made by Ghadeer, Atheer, Renad, Zaher

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🧑‍💻 Development Notes

• Ensure that all backend servers are running before using the frontend for full functionality.
• If you encounter issues with the Flask or Node.js servers, check if the necessary model files (.pkl or .sav) are present in the models/ folder and that the correct paths are referenced in the backend scripts.

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📦 Testing

To test the talent prediction system via CURL, you can use the following command to send a video file directly to the backend:

curl -X POST -F "video=@/path/to/your/football_video.mp4" http://localhost:5001/predict

For the player value prediction:

curl -X POST -H "Content-Type: application/json" -d '{"age": 24, "height_cm": 180, "weight_kgs": 75, "overall_rating": 85, "potential": 90}' http://localhost:5002/predict-value

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✅ Requirements

• Python 3.6 or higher
• Node.js (optional for advanced features)
• Dependencies: Flask, MediaPipe, OpenCV, etc.