The Evolution of Major League Baseball: Told Through Its Players

Project Highlights

• 1,000+ schools analyzed for MLB player production
• Over 18,000 player records examined across decades
• Financial spending trends traced across major franchises
• Career length, retention, and player characteristics revealed using descriptive statistics and advanced SQL queries
• Engaging and insightful visuals created in Jupyter Notebooks for storytelling

Tools & Libraries

• Python, Pandas, NumPy
• Matplotlib, Seaborn, Plotly
• Jupyter Notebooks for analysis and storytelling
• PostgreSQL (for original queries and joins)

Project Structure

├── Data/
|   └── Initial Data Load Script/
|       └── mlba_db_create_statements.sql
│   ├── players.csv
│   ├── salaries.csv
│   ├── schools.csv
│   └── school_details.csv
├── Notebook/
│   └── mlb_player_report.ipynb
├── Queries/
│   └── [All SQL files with queries]
├── Visuals/
├── requirements.txt 
├── README.md
└── mlb_player_report.pdf

Key Insights

1. School Origins

• California dominates with 1,247 MLB players, which is more than Texas and Florida combined.
• UT Austin, USC, and ASU are top talent pipelines with decades of player output.
• Shift over time: From Ivy League in early years to Southern and Western schools in modern decades.
• Post-1990s, fewer schools feed more players, suggesting recruitment has become more centralized.

2. Team Behavior & Spending

• San Francisco Giants, Yankees, and Angels top the lifetime spending chart.
• 2007 saw 6 teams exceed $1B in total spend, representing the peak of modern team investment strategies.
• Retention varies sharply: Some teams retain over 50% of players; others retain just 5%.
• Spending patterns reveal how each team builds competitive strategies differently.

3. Player Career Patterns and Retention Rates

• Average career lasts ~3 years with most players retiring by age 29.
• 75% of players play 8 years or less and nearly 7,000 last for only a single season.
• Only 23% of long-term players stay with the team they debuted with.

4. Physical Evolution and Attribute Comparison

• Player height has increased gradually (average rate: 0.35 in/decade).
• Player weight increased more rapidly, from 163 lbs in the 1870s to 207 lbs in the 2010s.
• Batting & throwing styles remain stable with ~79% of players showing a right-handed throw preference.

How to Run and View Project Locally

1. Clone the repository:
   git clone https://github.com/shree131/mlb-player-analytics.git

2. Navigate into the directory:
   cd mlb-player-analytics

3. Install dependencies:
   pip install -r requirements.txt

4. Launch Jupyter Notebook:
   jupyter notebook

Note: This project uses a .env file to store database credentials. Please create your own .env file based on the structure in the notebook.

Summary

Beneath the stats lies a dynamic story of how geography, economics, and strategy have shaped the modern MLB. It's a story told through data, trends, and generational shifts in the league's ecosystem.

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