Probability Modeling: Getting Started

This post introduces a framework to represent the mathematical concept of probability in Python. We’ll develop tools over a series of posts that we can use to analyze games of chance and some popular board games. We will also show how to apply these ideas to uncertainty in sports.
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Introduction to Web Scraping

Any analysis needs to start with data. To do serious sports analytics, we need to figure out how to capture information, assess its quality and put it into a useful format. Fortunately, there is a massive amount of quality sports data available on the internet, which can be your starting point for great analytics.
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Setting up Python

In this guide, I will show you how to set up a powerful Python environment to do sports analytics on your computer. If you follow these steps, you will have all the libraries you need to do web scraping, data analysis and visualization. Future posts on this site will assume that you have installed the necessary libraries.
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Welcome to Practically Predictable. If you want to learn sports analytics, this site is meant for you!

Sports analytics has come a long way from the publication of Moneyball in 2003 (and the film’s 2011 release) to the recent World Series. Although analytics first gained widespread attention in baseball, it has also become important in basketball, the NFL, English Premier League football, tennis and many other sports. The increasing use of analytics has changed sports and sports journalism.

This site will teach you how to:

  • access the huge amount of sports data available on the internet;
  • create charts and graphs to get insight and tell interesting stories about the data; and
  • make useful sports predictions supported by the data.

Whether your goal is to improve your fantasy roster, pick better NCAA March Madness brackets, or just learn about the exciting field of sports analytics, I hope you find this site helpful and informative.
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