Football Analytics – Serie A 2017/18

Football Analytics is a comprehensive project designed to collect, analyze, and visualize performance data for football teams and players during the Serie A 2017/18 season. The project uses a sophisticated database structure and machine learning models to provide insights into match events, player actions, and team performance.

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🌐 Project Overview

The goal of this project is to build a system that allows for detailed analysis of football events, focusing on Serie A 2017/18 matches. By utilizing Kaggle's Soccer Match Event Dataset and additional data from FBref via web scraping, this project offers:

• Data Preprocessing: Cleaning and transforming raw football data.
• Database Design: Creating a robust database to store match events, player stats, and more.
• Analysis: Detailed analysis of events like passes, shots, and actions during the match.
• Visualization: Interactive visualizations of player movements, actions, and performance metrics.

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🛠️ Technologies & Tools

• Languages: Python
• Libraries: Pandas, NumPy, Scikit-learn, mplsoccer, Electron
• Database: MongoDB (NoSQL)
• Data Sources: Kaggle, FBref
• Machine Learning Models: Not specified in the current document
• Visualizations: mplsoccer for football-specific data visualizations

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📁 Repository Structure

Football_Analytics/
├── code/                     → Source code and implementation files
│
├── dataset.r                 → Compressed dataset (ZIP file, includes all relevant datasets)
│
├── docs/                     → Documentation
│   ├── Football_Analytics.pptx  → PowerPoint presentation about the project
│   ├── Football_Analytics_RAD.pdf  → Requirement Analysis Document
│   └── Project_Documentation_Football_Analytics.pdf  → Final project documentation
│
└── README.md                 → Project documentation (this file)

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🚀 How to Use

1. Clone the repository:

   git clone https://github.com/Marco210210/Football-Analytics.git

2. Download the dataset from Kaggle or via the provided link in the dataset/ directory. The dataset is not included in this repository due to size limitations.

3. Explore the code in the code/ directory for the data preprocessing and analysis scripts.

4. The Football_Analytics.pptx file provides an overview of the project, while the PDF documents in docs/ offer detailed documentation and requirement analysis.

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📄 Documentation

• Project Report (PDF)
• Requirement Analysis Document (PDF)

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👥 Contributors

• Arcangeli Giovanni
• Ciancio Vittorio
• Di Maio Marco

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📄 License

This project is licensed under the CC BY-NC-SA 4.0 License

You may share and adapt this work for non-commercial purposes only, as long as you give appropriate credit and distribute your contributions under the same license.
For commercial use, explicit permission from the authors is required.