Atlanta Metro Booming Update 3

Hello again.

 

Since my last update, I have collected all the demographic data that I will be using for this project. The IPUMS database from NHGIS proved invaluable in this task and I am indebted to that service. The data I collected are organized by census tracts, which are geographical divisions within counties that the census uses for greater geographic granularity. The data have attributes for race, gender, age, income, and a few more things that will allow me to analyze trends throughout the Atlanta region.

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Final Update for College Football Performance as Affected by Distance from Home

The moment of truth: is college football performance affected by distance from home? As far as I can tell, the answer is no.

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Update #2: College Football Performance and Proximity to Home

How do you measure football performance? How can you compare Ndamukong Suh, a defensive tackle that played for Nebraska from 2005 to 2009 with Cooper Rush, Central Michigan’s quarterback from 2013 to 2016? These questions are incredibly difficult to answer, especially given the nature of the sport. It is impossible, at this point, to accurately and fairly quantify every player’s contribution to the result of a play, much less a game. Advanced statistics in football is always improving and decoding the game, but it is still far behind simpler sports like baseball and basketball.

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First Update on College Football Recruiting Analysis

The necessary preliminary procedure for any data analysis project is data collection and compilation. So it is for my project, which is to determine the impact of distance from home on college football player performance. Fortunately, the website 247sports.com has a fairly complete database of recruits, as well as their rating, position, hometown, and college. Unfortunately, the website has this information in a very unfriendly format for data scraping. The major challenge of this first segment was implementing a program that took those attributes from the website and created a spreadsheet with this information in a workable format (Microsoft Excel). I successfully implemented this program and created the necessary databases by utilizing the BeautifulSoup module in Python to scrape the data from the website, as well as the xlwt package to write it out to Excel.

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