Hands-On APIs for AI and Data Science

Chapter 9. Using APIs for Data Analytics

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 9th chapter of the final book. The GitHub repo for this book is https://github.com/handsonapibook/apibook-part-one.

If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the editor at [e-mail].

Your eyes see the game much better than the numbers. But the numbers see all the games.

Dean Oliver, sports statistician

The sports world loves all forms of data analytics - charts, graphs, and statistics that describe the results of events or predict what will happen next. When a sports fan views those data analytics, the probably never consider what data source was used to create them. In many cases, the data source is an API. In this chapter, you will learn best practices for consuming APIs and creating data analytics products using Jupyter Notebooks, a popular tool used by data scientists.

Custom Metrics for Sports Analytics

One of the most celebrated forms of analytics is the custom metric, a calculation that summarizes complicated behavior, ability, and outcomes as a number. Every sport has metrics that players, coaches, managers, and fans pay attention to. ...