Overview:

DataBall is a project that combines data science and sports by attempting to predict NBA winners against the spread. The project uses statistics pulled from the NBA stats website, point spreads, and over/under lines from covers.com. All predictions are made using Python and the scikit-learn machine learning library.

Features:

• Scraping data: The project includes a Scrapy project that can scrape point spreads and over/under lines from covers.com.
• Support functions: The project includes a Python module called “databall” that provides support functions for tasks such as collecting stats to a SQLite database, simulating seasons, and customizing plots.
• Documentation: The “docs” folder contains the necessary code to build the GitHub Pages site for this project.
• Notebooks: The “notebooks” folder contains Jupyter notebooks of all analyses.
• Report: The “report” folder contains LaTeX files for the report and slides.

Installation:

To install the theme, follow these steps:

1. Clone the repository: git clone [repository-url]
2. Navigate to the “databall” directory: cd databall
3. Install the required packages: pip install -r requirements.txt
4. Run the project: python main.py

Note: Make sure you have Python and scikit-learn installed on your system before proceeding with the installation.

Summary:

DataBall is a project that combines data science and sports to predict NBA winners against the spread. It uses data scraping, support functions, documentation, notebooks, and a report to achieve its goals. The project is implemented in Python and utilizes the scikit-learn machine learning library. With its comprehensive features and installation guide, DataBall provides a platform for NBA betting using data analysis.