Moneyball Analysis: Player Replacement Strategy

Project Overview

This project is based on the data-driven approach of the Oakland Athletics baseball team, as featured in the book "Moneyball" by Michael Lewis. The analysis aims to identify cost-effective player replacements using sabermetrics, focusing on batting average, on-base percentage, and slugging percentage. The goal is to help the Athletics maintain a competitive edge with limited resources by finding undervalued players who can match or exceed the contributions of key players lost in the offseason.

Technologies Used

• R: For data manipulation, statistical analysis, and visualization
• Data Visualization: ggplot2 and other R libraries for clear insights

Repository Structure

• Data/: Contains Batting.csv and Salaries.csv, with player performance and salary information.
• Code/: Includes Moneyball.R with all the R code for data cleaning, analysis, and visualization.

Key Insights

• Data-Driven Strategy: Emphasis on sabermetrics allows for objective player evaluation, focusing on undervalued statistics like on-base percentage and slugging percentage.
• Budget Efficiency: Highlights players who can replace key contributors (e.g., Jason Giambi, Johnny Damon) within the constraints of a small-market team budget.
• Long-Term Impact: The analysis provides insights into maintaining competitiveness through efficient resource allocation, which has implications for sports management beyond baseball.

Instructions

1. Clone this repository.
2. Run the R script (Moneyball.R) in RStudio or any compatible R environment.
3. Ensure necessary R packages (like dplyr, ggplot2) are installed.
4. Review the analysis results, visualizations, and player recommendations.

Contact

Connect with me on LinkedIn to discuss this project or view my other work in data analysis and sports analytics.