https://github.com/stefagnone/moneyball_project

Data-driven analysis inspired by the Moneyball approach, identifying affordable replacements for key Oakland A's players using R and sabermetrics to support cost-effective recruitment.
https://github.com/stefagnone/moneyball_project

baseball-statistics data-analysis data-driven-decision-making player-replacement-strategy r-programming sabermetrics sports-analytics

Last synced: 15 days ago
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Data-driven analysis inspired by the Moneyball approach, identifying affordable replacements for key Oakland A's players using R and sabermetrics to support cost-effective recruitment.

• Host: GitHub
• URL: https://github.com/stefagnone/moneyball_project
• Owner: stefagnone
• Created: 2024-11-14T12:32:39.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-12T16:48:14.000Z (22 days ago)
• Last Synced: 2024-12-12T17:43:04.976Z (22 days ago)
• Topics: baseball-statistics, data-analysis, data-driven-decision-making, player-replacement-strategy, r-programming, sabermetrics, sports-analytics
• Language: R
• Homepage:
• Size: 2.36 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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](https://www.linkedin.com/in/stefano-compagnone98/) to discuss this project or view my other work in data analysis and sports analytics.