From Economics to Python-Powered Sports Analytics: Nate Braun's Game-Changing Journey

On this week’s episode of the show, I talk with Nate Braun, author of several Python books, all having to do with sports. Nate shares his journey from having a background in economics to writing books on sports data analysis and visualization using Python. Despite not initially being skilled in coding, Braun was inspired by his work in environmental issues and modeling, leading him to develop fantasy football models and later educational books on coding and data analysis with a focus on various sports. We cover Nate’s data scraping and writing process, as well as the ins and outs of why he likes to work with Python and the various libraries he uses in his work.

Topics Discussed

• Background and Transition: Nate shares his unconventional journey from working on environmental issues to developing a niche in sports data analytics. His inspiration took root during his work on modeling the impact of the BP oil spill.
• Fantasy Football and Education: The pivot to sports began with fantasy football models. The success of these models led Nate to author books designed to educate enthusiasts on coding and data analysis, specifically tailored for those outside the computer science field.
• Challenges and Opportunities: Nate talks about the difficulties he faced entering the competitive fantasy football advice market. With the rise in betting and fantasy sports advertising, he recognizes the potential for educating people on data analysis.
• Sport-by-Sport Learning Curve: Despite not being an expert in all sports, Braun has written instructional books on a range of sports by dedicating time to write and develop new models, leveraging the success and experience gained from his initial football book.
• Data Gathering and Visualization: Our conversation delves into the varying difficulty levels of acquiring and visualizing data across sports and we highlight Nate’s use of the Python Seaborn library.
• Python Over R: Nate expresses his preference for Python due to its versatility in machine learning, data visualization, web scraping, and content creation, favoring it over R.
• Technical Deep-Dive into Web Scraping: We talk about using Python for web scraping, including dealing with JavaScript-heavy websites, and the other tools Nate uses like Beautiful Soup and Selenium.
• Future Plans: A teaser for a potential Python book on Formula One as Braun’s love for sports continues to drive his writing endeavors.

➡️ Check out more links, notes, transcript, and more at the PolicyViz website.

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