Soccer Analytics with Machine Learning

Chapter 3. Exploratory Data Analysis in Soccer

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 3rd chapter of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

In the previous chapter, you built a toolkit of essential Python skills. Now, it’s time to put those skills to work on real soccer data.

This chapter introduces exploratory data analysis (EDA), the initial, open-ended process of inspecting a dataset to understand its structure, find patterns, and identify anomalies. Just as a coach studies match footage before deciding on tactics, EDA is a structured investigation of the data. It’s how analysts gain their first understanding of what has been recorded, how often, and where the gaps or oddities lie. Instead of jumping straight to predictive models, we start by asking simple questions to build intuition. This crucial first step prevents us from drawing conclusions on shaky ground and lays the foundation for all subsequent analysis.

As we perform EDA, we’ll see how to adapt general-purpose data science tools into soccer-specific analyses. Rather than just counting rows or calculating averages, we’ll ...