This project is an exploration into the world of baseball analytics, focusing on predicting Wins Above Replacement (WAR) for MLB players. It leverages historical player performance data to project future contributions using machine learning techniques.
In this project, we delve into the application of data science within the realm of baseball, one of America's most data-rich sports. By analyzing player statistics, we attempt to forecast their future impact on the team's success, measured in terms of WAR. The goal is to assist teams in making informed decisions regarding player acquisitions and game strategy, maximizing performance while managing resources efficiently.
Before running this project, you should have the following setup:
- Python 3.8 or higher installed on your system.
- Ensure you have access to Jupyter (https://jupyter.org/)
- Basic knowledge of Python programming.
- Familiarity with baseball statistics would be beneficial.
To set up the project environment and install the necessary packages, run the following command:
pip install -r requirements.txtThis project is the work of a solo enthusiast venturing into the intersection of data science and sports. Suggestions and feedback are highly appreciated.
I want to thank Dataquest YouTube channel for providing a lot of the underlying code and basic idea for the project. Also, I want to thank James LeDoux, the creator of pybaseball, for producing such a useful library for baseball analytics in Python.