/CBBModel2023

College Basketball Model

Primary LanguagePython

College Basketball Betting Model - 2023

This repository contains a Python script for extracting and analyzing sports betting data for college basketball games. The main calculations script is CBBModel2023.py, and the Streamlit app for visualizing the projections is NCAAStreamlit.py. The README file provides an overview of the code and how to use it.

Table of Contents

Requirements

Before using the script, make sure you have the following prerequisites installed:

  • Python 3.x
  • Required Python libraries: BeautifulSoup, requests, pandas, json, numpy, streamlit

Installation

  1. Clone this repository to your local machine or download the script.

    git clone https://github.com/ryanbiancavilla/CBBModel2023.git

  2. Navigate to the project directory.

    cd CBBModel2023

  3. Install the required Python libraries using pip.

    pip install beautifulsoup4 requests pandas numpy streamlit

Usage

To use this script, follow these steps:

  1. Run the main calculations script to fetch and update the projections.

    python CBBModel2023.py

  2. Run the Streamlit app to visualize the projections.

    streamlit run NCAAStreamlit.py

  3. The main calculations script (CBBModel2023.py) will perform the following tasks:

    • Scrape data from KenPom to retrieve team statistics.
    • Fetch sports betting data from Oddsshark using the OddsShark API.
    • Calculate adjusted statistics and projections for each game.
    • Simulate games to generate win percentages, spread edges, and over/under percentages.
    • Update the proj_scores DataFrame with the latest projections.

  4. The Streamlit app (NCAAStreamlit.py) will display the projections in an interactive web interface.

Script Overview

The main calculations script (CBBModel2023.py) consists of several key components:

  1. Data Scraping: It uses the BeautifulSoup library to scrape team statistics from KenPom and sports betting data from Oddsshark.

  2. find_team Function: This function allows you to find team information by providing the team name as input. It retrieves data from the KenPom dataset.

  3. Data Analysis: The script calculates adjusted statistics, projected scores, spreads, and over/under values for each game.

  4. Simulation: It simulates games using Monte Carlo simulations to estimate win percentages, spread edges, and over/under percentages.

  5. proj_scores DataFrame: The updated projections are stored in the proj_scores DataFrame, which is used by the Streamlit app.

Customization

You can customize the main calculations script and the Streamlit app as follows:

  • Adjust Monte Carlo simulation parameters by changing num_simulations in CBBModel2023.py.
  • Modify the team name mapping file (CBBTeamsDatabase.xlsx) to include the teams you are interested in.
  • Customize the Streamlit app interface and styling in NCAAStreamlit.py.

For any questions or issues, please create a GitHub issue in this repository.

Enjoy analyzing college basketball games with the 2023 College Basketball Betting Model!