🌱 Project: Touch Grass

Quantifying the "Misery Loop" of Mainstream Gaming through Data Engineering.

The Premise

Imagine a product.

It costs $70 a year. You don't just use it; you obsess over it. You spend nearly 15 hours—two full workdays—grinding away at it. It demands more of your time than any other form of entertainment you own.

And you hate almost every minute of it.

Engineered this repo to prove that Sports Gamers are trapped in a statistical anomaly I call the "Misery Index".

Ingested over 24,000 games from the RAWG API to settle the "Jock vs. Nerd" debate once and for all. Are shooters and sports games just "dumb fun" for casuals? Or is something darker happening?

Read the Full Analysis

I discovered that Mainstream gamers aren't casuals,they are "Grinders". We also found that Sports gamers have the highest retention but the lowest satisfaction in the industry.

👉 Read the full blog post and see the visualizations here

The Stack (Engineered for Speed)

Avoided the bloat of standard Data Science setups. This project uses uv, a Rust-based Python package manager that is significantly faster than pip or poetry.

• Ingestion: Python scripts hitting RAWG.io API (with rate-limit handling).
• Storage: Local CSV Data Lake (Idempotent updates).
• Analysis: Pandas & Seaborn (Jupyter Notebooks).
• Environment: Managed by uv.

The "Misery Index"

To quantify the relationship between addiction and satisfaction, engineered a custom feature:

$$ Misery\ Index = \begin{cases} \frac{Average\ Hours\ Played}{User\ Rating} & \text{if } Rating > 0 \\ \text{Excluded} & \text{if } Rating = 0 \end{cases} $$

• Low Score: Healthy engagement (High Fun / Moderate Time).
• High Score: Toxic engagement (Low Fun / Massive Time).

Project Structure

project-touch-grass/
├── data/
│   └── raw/
│       └── gun_vs_ball.csv    # The raw dataset (24k+ games)
├── notebooks/
│   ├── gun_and_ball.ipynb     # Part 1: Mainstream vs. The World
│   └── gun_vs_ball.ipynb      # Part 2: The Civil War (Shooters vs. Sports)
├── src/
│   ├── ingest_games.py        # The ETL script
│   ├── check_data.py          # RAWG API Data Checker
│   └── analyze_gun_ball.py    # Non-notebook version of the analysis
├── pyproject.toml             # uv configuration
└── README.md                  # You are here

How to Run

1. Prerequisite: Install uv

If you don't have it, get it. It changes lives.

Mac / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (PowerShell):

powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Sync Dependencies

This will create the virtual environment and install pandas, seaborn, ipykernel, etc.

uv sync

3. Run the Analysis

Open VS Code, select the gun_vs_ball.ipynb notebook, and select the Kernel named .venv.

code notebooks/gun_vs_ball.ipynb

Contributing

I am an engineer, not a statistician. If you find a flaw in the Misery Index or want to add a new dimension to the analysis (e.g., "Price per Hour of Fun"), feel free to open a PR!

1. Fork it.
2. Branch it (git checkout -b feature/new-metric).
3. Commit it (git commit -m 'Added the Microtransaction-Pain-Ratio').
4. Push it.