⚽ Football Stats

A data engineering project that processes football match data from a raw CSV file, cleans and validates it, loads it into a PostgreSQL database, and prepares views for analysis. The processed data can be further visualized in Power BI.

This project was created to gain experience with data handling (240k+ rows), Docker, PostgreSQL, and data engineering workflows.

Data source: Gábor, A. (2025). Club Football Match Data. Retrieved from https://github.com/xgabora/Club-Football-Match-Data-2000-2025/.

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🚀 Tech Stack

• Python (ETL scripts with pandas)
• PostgreSQL (data storage, views)
• Docker (containerized setup)
• Power BI (data visualization)

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📂 Project Structure

Football-Stats/
│-- Data/ # Raw and processed CSV files
│-- PostgreSQL/ # SQL scripts, schema definitions
│-- Power-BI/ # Power BI dashboards and reports
│-- src/ # Helper modules (cleaning, ETL functions)
│-- .env # Environment variables (DB credentials etc.) You have to create it
│-- docker-compose.yml # Docker setup for PostgreSQL
│-- Dockerfile # Docker image build for Python environment
│-- main.py # Main pipeline script
│-- requirements.txt # Python dependencies

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⚡ Features

• Load football statistics from CSV (240,000+ rows).
• Clean and validate raw data using pandas.
• Insert teams and matches into PostgreSQL tables.
• Automatically replace team names with IDs for consistency.
• Create SQL views for easier data analysis.
• Connect to Power BI for visualization.

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🔧 Installation & Setup

1. Clone the repository

git clone https://github.com/szabilukacs/football-stats.git
cd football-stats

Create your .env file

DB_USER = ""
DB_PASS = ""
DB_HOST = ""
DB_PORT = ""
DB_NAME = ""

The script will:

1. Load raw CSV data.
2. Clean & validate it.
3. Insert data into PostgreSQL tables.
4. Create analysis-friendly views.

After running, open Power BI (or another BI tool) to the PostgreSQL database and start building dashboards.