This project focuses on analyzing football (soccer) data using data science and machine learning techniques. It extracts meaningful insights such as player performance, team statistics, and match patterns through data processing and visualization.
Football analytics helps transform raw match data into actionable insights for performance analysis, scouting, and decision-making.
- Data preprocessing and cleaning
- Exploratory Data Analysis (EDA)
- Visualization of player and team performance
- Match statistics analysis
- (Optional) Machine learning models for prediction/insights
- Structured dataset handling
- Python
- Pandas
- NumPy
- Matplotlib / Seaborn
- Jupyter Notebook
football-analytics/
│
├── data/ # Dataset files
├── notebooks/ # Jupyter notebooks
├── src/ # Source code (if any)
├── outputs/ # Graphs & results
├── requirements.txt # Dependencies
└── README.md
- Clone the repository:
git clone https://github.com/Samearth17/football-analytics.git
cd football-analytics- Install dependencies:
pip install -r requirements.txt- Run Jupyter Notebook:
jupyter notebook- Open the notebooks in Jupyter
- Run cells step-by-step
- Explore visualizations and insights
This project may include:
- Player performance comparison
- Team statistics trends
- Goal/assist distribution
- Match outcome patterns
- Add real-time data APIs
- Build predictive models (match outcome, player performance)
- Create a web dashboard for visualization
- Integrate deep learning / tracking data
Contributions are welcome! Feel free to fork the repo and submit a pull request.
This project is licensed under the MIT License.
Samarth Mukhija
- GitHub: https://github.com/Samearth17
- Open football datasets
- Data science and sports analytics community