Welcome to Biz_Ball: Analyzing the Financial Scorecard of Baseball Franchises.

This project started out as the demographic data analyzer project from the Data Analysis with Python certification from freecodecamp. I started with the main code that made the tests pass, but now that I have it in GitHub I'm turning it into a database app for analyzing the business info of baseball franchises. My inspiration is the Bloomberg Billionaires Index, which updates the projected net worth of the world's billionaires based on publicly available information. Variables for ownership include age, industry where they made enough of a fortune to purchase a baseball team (or inheritance if they either inherited a team or inherited the wealth it took to purchase a team), educational background (bachelors degree, masters degree, professional degree [MBA, JD, etc.], doctorate, or no degree), race (very broadly defined), sex (very narrowly defined), net worth, team, league, division, year bought, and value of the franchise.

The initial data points to be pulled include racial makeup of baseball owners, average age, percentage with at least a bachelors degree, top industry producing baseball owners, average net worth (I've realized this will be highly imprecise for a number of reasons to be detailed later), and average value of teams owned. Once I get sufficiently comfortable and proficient with Python and Pandas, I'll move on to more telling data points, outlining correlations and trends between the different variables. An example of this could be the question, "Which division has the richest owners or most highly valued franchises?" An obvious hunch would be larger market areas, like the East or West Coasts, but running correlation coefficients could really bring these questions to life.

Finally, once I've gotten more familiar with matplotlib, I'll visualize the data, and maybe even evolve it to the point where end users can choose their data points or questions and the application will spit out an accurate and meaningful answer.