Soccer Analytics with Machine Learning

Chapter 4. Regression Techniques for Soccer Analytics

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In Chapters 2 and 3, we built the foundations of the analytical workflow used throughout this book. We set up the environment, learned the core tools, and used exploratory analysis to understand what the data contains. The next step is to move from description to prediction.

This chapter introduces regression, the first predictive framework we will study in depth. Regression is used when the outcome we want to estimate is numerical rather than categorical. In soccer, that leads to questions such as:

• What is a player’s market value likely to be next season?

• How many goals can we expect a team to score in their next match?

• How many kilometers will a midfielder cover in a game?

These are all questions that regression models can help us answer. Regression does not remove uncertainty, but it gives us a structured way to estimate expected values, compare influences, and understand how outcomes change as inputs change. That makes it useful ...