https://github.com/misaghmomenib/soccer-match-analysis
This Project Predicts Football Match Outcomes (Home Win, Away Win, or Draw) Using Historical Match Data. It Involves Data Preprocessing, Exploratory Analysis, and Training a Random Forest Model to Predict Results Based on Features Like Shots, Possession, and Passes.
https://github.com/misaghmomenib/soccer-match-analysis
data-analysis git open-source python
Last synced: 2 months ago
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This Project Predicts Football Match Outcomes (Home Win, Away Win, or Draw) Using Historical Match Data. It Involves Data Preprocessing, Exploratory Analysis, and Training a Random Forest Model to Predict Results Based on Features Like Shots, Possession, and Passes.
- Host: GitHub
- URL: https://github.com/misaghmomenib/soccer-match-analysis
- Owner: MisaghMomeniB
- License: gpl-3.0
- Created: 2025-03-11T16:14:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-13T19:33:16.000Z (about 1 year ago)
- Last Synced: 2025-06-13T20:33:35.096Z (about 1 year ago)
- Topics: data-analysis, git, open-source, python
- Language: Python
- Homepage:
- Size: 36.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ⚽ Soccer Match Analysis
A Python-based project designed to **analyze soccer match data** and extract meaningful insights. It supports event processing, performance metrics, visualization, and automatic reporting on team and player performance.
---
## 📋 Table of Contents
1. [Overview](#overview)
2. [Features](#features)
3. [Data & Approach](#data--approach)
4. [Getting Started](#getting-started)
5. [Usage Examples](#usage-examples)
6. [Visualization & Reporting](#visualization--reporting)
7. [Tech Stack](#tech-stack)
8. [Contributing](#contributing)
9. [License](#license)
---
## 💡 Overview
This 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.
---
## ✅ Features
- 📥 Parse/clean soccer event data (passes, shots, fouls, etc.)
- 📉 Compute per-match and per-player statistics (accuracy, efficiency, involvement)
- 📊 Visual outputs: bar charts, line graphs, heatmaps
- 🔄 Comparative analysis between teams or players
- 🧠 Optional ML clustering of playing styles or event patterns
---
## 🗂️ Data & Approach
- **Input data**: CSV or JSON with event-level details (timestamp, event type, position, player/team ID)
- **Cleaning**: Standardize missing values, convert coordinates to suitable ranges
- **Aggregation**: Compute:
- Pass success rate = passes completed / passes attempted
- Shots on target vs total shots
- Possession % measured through event duration
- **Output**:
- Tables (CSV/JSON) with aggregated metrics
- Visualizations (PNG/interactive HTML)
---
## ⚙️ Getting Started
### Setup environment
```bash
git clone https://github.com/MisaghMomeniB/Soccer-Match-Analysis.git
cd Soccer-Match-Analysis
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
````
### Prepare your data
* Ensure `data/` folder contains `match_events.csv` (or `.json`)
* File must include columns like: `match_id, team_id, player_id, event_type, x, y, timestamp`
---
## 🚀 Usage Examples
### 🧮 Generate Metrics
```bash
python src/analyze_match.py --input data/match_events.csv --output reports/match_summary.json
```
This produces match and player statistics in JSON or CSV format.
### 📊 Create Visualizations
```bash
python src/plot_heatmap.py --input reports/match_summary.json --player 10
```
Generates a positional heatmap for player ID 10.
---
## 📈 Visualization & Reporting
All plot scripts generate:
* **Heatmaps** showing player movement and concentration
* **Bar charts** for pass accuracy, shot efficiency, possession
* **Line charts** for time-series trends (e.g., possession over match time)
Reports saved to `reports/` with filenames like `team_comparison.png`.
---
## 🛠️ Tech Stack
* **Python 3.8+**
* **pandas**, **NumPy** for data processing
* **Matplotlib**, **Seaborn**, **Plotly** for plotting
* **Jupyter Notebooks** for exploratory analysis
---
## 🤝 Contributing
Contributions are welcome! Suggestions include:
* Add support for **multiple match files** and season-level aggregation
* Support for **live data feeds** or event APIs (e.g., StatsBomb, Opta)
* Include **interactive dashboards** (Streamlit, Dash)
* Add **machine learning models** to predict outcomes or player roles
To Contribute:
1. Fork the repository
2. Create a branch (`feature/...`)
3. Open a Pull Request with your changes
---
## 📄 License
Distributed under the **MIT License**. See `LICENSE` for details.