In this post, we are going to take another look at scraping NBA team information from Wikipedia. We will also see how generate a map of NBA arena locations.
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Category: Technical Guides
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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Some Suggestions for Learning (or Improving your) Python
This post offers a few suggestions for those of you who might want to get up to speed using Python or to upgrade your skills. There are a huge number of resources out there. Hopefully this will help you choose where to start.
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Probability Distributions and Dice Rolls
This post builds on the previous post on probability modeling in Python. In this post, we will discuss the concept of probability distribution and how to represent it in Python.
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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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Some Suggestions for Running Python
In this guide, I will describe some of the ways you can run Python on your computer. This post is not intended to be a full tutorial. You can find plenty of those on the Internet if you need more details. Instead, I will make some suggestions and point out some helpful resources.
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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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