{"id":26201660,"url":"https://github.com/misaghmomenib/soccer-match-analysis","last_synced_at":"2026-04-20T08:07:08.932Z","repository":{"id":281883572,"uuid":"946754824","full_name":"MisaghMomeniB/Soccer-Match-Analysis","owner":"MisaghMomeniB","description":"This Project Predicts Football Match Outcomes (Home Win, Away Win, or Draw) Using Historical Match Data. 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It supports event processing, performance metrics, visualization, and automatic reporting on team and player performance.\n\n---\n\n## 📋 Table of Contents\n\n1. [Overview](#overview)  \n2. [Features](#features)  \n3. [Data \u0026 Approach](#data--approach)  \n4. [Getting Started](#getting-started)  \n5. [Usage Examples](#usage-examples)  \n6. [Visualization \u0026 Reporting](#visualization--reporting)  \n7. [Tech Stack](#tech-stack)  \n8. [Contributing](#contributing)  \n9. [License](#license)\n\n---\n\n## 💡 Overview\n\nThis repository is aimed at processing **soccer match event data** to generate performance metrics—pass accuracy, shots on target, possession rates, heatmaps—and visual reports. It can be used for analytics, coaching insights, or personal study.\n\n---\n\n## ✅ Features\n\n- 📥 Parse/clean soccer event data (passes, shots, fouls, etc.)  \n- 📉 Compute per-match and per-player statistics (accuracy, efficiency, involvement)  \n- 📊 Visual outputs: bar charts, line graphs, heatmaps  \n- 🔄 Comparative analysis between teams or players  \n- 🧠 Optional ML clustering of playing styles or event patterns\n\n---\n\n## 🗂️ Data \u0026 Approach\n\n- **Input data**: CSV or JSON with event-level details (timestamp, event type, position, player/team ID)  \n- **Cleaning**: Standardize missing values, convert coordinates to suitable ranges  \n- **Aggregation**: Compute:\n  - Pass success rate = passes completed / passes attempted\n  - Shots on target vs total shots\n  - Possession % measured through event duration  \n- **Output**:  \n  - Tables (CSV/JSON) with aggregated metrics  \n  - Visualizations (PNG/interactive HTML)\n\n---\n\n## ⚙️ Getting Started\n\n### Setup environment\n\n```bash\ngit clone https://github.com/MisaghMomeniB/Soccer-Match-Analysis.git\ncd Soccer-Match-Analysis\npython3 -m venv venv\nsource venv/bin/activate\npip install -r requirements.txt\n````\n\n### Prepare your data\n\n* Ensure `data/` folder contains `match_events.csv` (or `.json`)\n* File must include columns like: `match_id, team_id, player_id, event_type, x, y, timestamp`\n\n---\n\n## 🚀 Usage Examples\n\n### 🧮 Generate Metrics\n\n```bash\npython src/analyze_match.py --input data/match_events.csv --output reports/match_summary.json\n```\n\nThis produces match and player statistics in JSON or CSV format.\n\n### 📊 Create Visualizations\n\n```bash\npython src/plot_heatmap.py --input reports/match_summary.json --player 10\n```\n\nGenerates a positional heatmap for player ID 10.\n\n---\n\n## 📈 Visualization \u0026 Reporting\n\nAll plot scripts generate:\n\n* **Heatmaps** showing player movement and concentration\n* **Bar charts** for pass accuracy, shot efficiency, possession\n* **Line charts** for time-series trends (e.g., possession over match time)\n\nReports saved to `reports/` with filenames like `team_comparison.png`.\n\n---\n\n## 🛠️ Tech Stack\n\n* **Python 3.8+**\n* **pandas**, **NumPy** for data processing\n* **Matplotlib**, **Seaborn**, **Plotly** for plotting\n* **Jupyter Notebooks** for exploratory analysis\n\n---\n\n## 🤝 Contributing\n\nContributions are welcome! Suggestions include:\n\n* Add support for **multiple match files** and season-level aggregation\n* Support for **live data feeds** or event APIs (e.g., StatsBomb, Opta)\n* Include **interactive dashboards** (Streamlit, Dash)\n* Add **machine learning models** to predict outcomes or player roles\n\nTo Contribute:\n\n1. Fork the repository\n2. Create a branch (`feature/...`)\n3. Open a Pull Request with your changes\n\n---\n\n## 📄 License\n\nDistributed under the **MIT License**. See `LICENSE` for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmisaghmomenib%2Fsoccer-match-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmisaghmomenib%2Fsoccer-match-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmisaghmomenib%2Fsoccer-match-analysis/lists"}