Soccer Analytics with Machine Learning

Chapter 5. Predicting Soccer Outcomes with Classification

In Chapter 4, we modeled numerical outcomes such as market value and goals scored. Many soccer questions, however, are not about magnitude but about category: whether a shot becomes a goal, whether a pass is completed, or which result a match produces. That is the setting for classification.

Classification keeps the same overall workflow as regression: define a target, choose features, train a model, and evaluate it carefully. What changes is the task itself. Instead of estimating a continuous value, we assign an observation to one of several discrete classes. Think about questions such as these:

• Will this shot be a goal or not?

• Is this pass likely to be successful?

• Will the home team win, lose, or draw?

• What type of pass will a player attempt under pressure?

These are all classification problems. The answer is not a number on a sliding scale, but a label or a probability. That shift may sound small, but it changes the way models are interpreted as well as the way performance is measured. In soccer analytics, classification matters because many real decisions are ultimately categorical, even when uncertainty remains at the center.

By the end of this chapter, you will be able to do the following:

• Grasp the fundamental concepts of classification and its applications in soccer.

• Build, train, and interpret a logistic regression model to predict the likelihood of a shot becoming a goal (the famous expected goals, ...