Diamond_Data_Scraper

Final Capstone project for Code The Dream Python 100 (Python Essentials)

⚾ MLB Strategy & Efficiency Dashboard

A data-driven deep dive into the "Moneyball" era of professional baseball.

This interactive dashboard analyzes the relationship between team spending, draft choices, and overall league parity. It helps answer the age-old question: Can you buy a championship, or is success built in the draft?

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🚀 Live Demo

[https://diamond-data-scraper.onrender.com/]

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📊 Core Features

💰 Spending Efficiency (ROI Analysis)

• What it shows: A dual-axis comparison of "Cost Per Win" vs. "Total Wins."
• Insight: Quickly identify "Value" teams (low cost, high wins) vs. "Luxury" teams (high cost, high wins).
• Interactive: Includes a dynamic "League Minimum Floor" to show teams operating on a shoe-string budget.

⚖️ League Parity (Inequality Trends)

• What it shows: Tracks the "Gap of Greatness" (Standard Deviation of WP) over time.
• Insight: Measures how the rise of the Minimum Salary affects competitive balance.
• Correlation Score: Automatically calculates if higher base pay makes the league more equal.

🎓 Draft Performance

• What it shows: Maps draft pick position against final season standing.
• Insight: Visualizes the "Rebuilding Cycle" using bubble sizes that reflect the weight of #1 overall picks.

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📸 Screenshots

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🛠️ Setup & Installation

1. Clone the repository

git clone [https://github.com/bunniebytes/Diamond_Data_Scraper](https://github.com/bunniebytes/Diamond_Data_Scraper)
cd Diamond_Data_Scraper

2. Set up a Virtual Environment

source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Run Main to collect data and create the database

python .\main.py

5. Run MyApp to view dashboard

python .\myapp.py

Visit http://127.0.0.1:8050/ in your browser.

📦 Tech Stack

• Language: Python 3.x
• Framework: Dash (Plotly)
• Data Storage: SQLite
• Processing: Pandas
• Deployment: Render / Gunicorn