🎮🎬 Metacritic Backend Scraper

Scrape high-quality data on games, movies, and TV shows straight from Metacritic’s backend. No frontend scraping. No fluff. Just clean, structured data.

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✨ What is this?

The Metacritic Backend Scraper is a Python tool that taps into Metacritic’s unofficial public backend API to pull detailed, structured data on:

• 🕹️ Games
• 🎥 Movies
• 📺 TV Shows

It uses clean requests logic with retry mechanisms, structured Pydantic models for data validation, and saves everything to organized CSV files. Ideal for data science, machine learning, or just geeking out on media insights.

🚀 Features

• 🎯 Full metadata extraction (release date, ratings, genres, platforms, cast, director, etc.)
• 📝 Fetch critic and user reviews, complete with scores and quotes
• 📊 Supports single title, top-ranked titles, by year, or custom product lists
• 📋 Logs every action to both console and file
• 💾 Saves outputs to easy-to-use CSVs
• 🔐 Unicode-safe, URL-safe slugification
• 🧩 Built with modularity, extensibility, and readability in mind

Installation

1. Clone the repository:

    git clone https://github.com/davutbayik/metacritic-backend-scraper.git
    cd metacritic-backend-scraper

2. Create a virtual environment (Optional - Recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

🧠 How It Works

The magic happens in MetacriticScraper, a flexible, object-oriented class with built-in support for:

• Session retries
• Clean URL slug generation
• Type-specific logic for parsing responses (games vs. movies vs. shows)
• Dynamic URL construction for reviews and top content

You can instantiate the scraper with just one line:

from metacritic_scraper import MetacriticScraper

scraper = MetacriticScraper(product_type="games")
games, reviews = scraper.fetch_top_products(limit=10)

🛠 Usage Examples

1. Scrape top 20 movies from the last decade:

scraper = MetacriticScraper(product_type="movies")
movies, reviews = scraper.fetch_top_products(limit=20, year_min=2015)

2. Scrape reviews for specific shows:

scraper = MetacriticScraper(product_type="shows")
shows, reviews = scraper.fetch_product_list(["Breaking Bad", "Better Call Saul"])

3. Scrape games released in 2023:

scraper = MetacriticScraper(product_type="games")
games, reviews = scraper.fetch_products_by_year(year=2023)

4. Scrape all movies (metacritic fetches all products which have at least 7 reviews)

scraper = MetacriticScraper(product_type="movies")
movies, reviews = scraper.fetch_all_products()

Data is saved automatically to the data/ directory as:

• games.csv
• games_reviews.csv

(Change games to movies or shows depending on type.)

📊 Datasets on Kaggle

Want to explore the scraped data directly without running the scraper?

Check out the published datasets on Kaggle using directly the MetacriticScraper class:

🔗 Metacritic Games Dataset
🔗 Metacritic Movies Dataset
🔗 Metacritic TV Shows Dataset

Each dataset includes both the metadata and reviews, formatted and ready for analysis in CSV format.

Don't forget to upvote if you find them useful! 👍

🧾 Example Outputs

🎥 Example Demo

metacritic_scraper_demo.mp4

⚙️ Dependencies

• requests
• pandas
• pydantic
• rich
• urllib3

All installable via pip install -r requirements.txt

⚠️ Disclaimer

This project uses an unofficial Metacritic API intended for educational purposes and internal use. Please use responsibly and be mindful of rate limits and Terms of Service.

📄 License

This project is licensed under the terms of the MIT License.
You are free to use, modify, and distribute this software as long as you include the original license.

📬 Contact

Made with ❤️ by Davut Bayık — feel free to reach out via GitHub for questions, feedback, or collaboration ideas.

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