This repository serves as a comprehensive, community-driven guide and resource hub for football analytics. It's designed for data scientists, analysts, researchers, and enthusiasts looking to delve into the quantitative aspects of the sport. The project aims to centralize learning materials, data sources, libraries, and research papers, fostering a collaborative environment for advancing football analytics.

How It Works

The project is structured as a curated collection of resources, organized into logical categories such as data sources, tutorials, libraries, and key concepts. It leverages GitHub for code sharing and collaboration, with a detailed README that acts as a central navigation point. The repository includes Jupyter notebooks demonstrating data scraping, parsing, engineering, and analysis, covering various advanced metrics and modeling techniques.

Quick Start & Requirements

• Installation: Primarily Python and R based. Ensure Python (3.6.1+) and R (4.0.4+) are installed. Specific Python and R libraries are listed in the README, including NumPy, pandas, matplotlib, seaborn, scikit-learn, SciPy, and various football-specific Python/R packages like kloppy, mplsoccer, worldfootballR, and StatsBombR.
• Prerequisites: Python and R environments, with their respective data science libraries.
• Resources: Extensive links to tutorials, blogs, papers, videos, and datasets are provided, making it a rich learning environment. Setup time is dependent on the user's familiarity with Python/R and the depth of exploration desired.

Highlighted Details

• Vast Resource Curation: An exceptionally thorough compilation of links to tutorials, libraries, papers, blogs, videos, podcasts, and notable figures in football analytics.
• Code Examples: Includes Jupyter notebooks for data scraping (FBref, Transfermarkt, Capology), parsing, engineering, and analysis (xG modeling, player similarity, tracking data).
• Data Sources: Lists and provides access methods for numerous event data, tracking data, aggregated performance data, and financial data providers.
• Key Concepts: Explains fundamental and advanced football analytics concepts like xG, xT, PV, VAEP, and more.

Maintenance & Community

The repository is actively maintained by Edd Webster (@eddwebster) with contributions from the football analytics community. It encourages pull requests and direct contact for suggestions. Links to Discord servers and Twitter accounts of key contributors are provided.

Licensing & Compatibility

The repository is licensed under the MIT License, permitting commercial use and linking with closed-source projects.

Limitations & Caveats

While comprehensive, the sheer volume of resources means users may need to filter for specific needs. Some data sources mentioned might have changed availability or access methods since the last update. The project is a curated list rather than a single executable tool.