Learning (or Improving at) pandas

Data is central to any analytics project. In Python, by far the most commonly-used package to manage data is pandas. In this short post, I will offer a few suggestions for those of you who want to get up to speed using pandas or take your skills to the next level. There are a huge number of resources out there. Hopefully this will help you choose where to start.
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Web Scraping NBA Team Matchups and Box Scores

We are going to use machine learning and statistics to predict NBA matchups. To do this, we are going to need data on NBA games, and lots of it. So let’s get all the team matchups and box scores from stats.nba.com, and make them ready for use.

This post has two purposes. The first is to show you how to do the actual web scraping. The second purpose is to show you how to examine data before you us it. Data are almost always a bit messy and need to be handled with care. It’s important to take some time to look at data and to make sure it’s clean before use.
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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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