Soccer Analytics with Machine Learning

Chapter 6. Advanced Classification Methods

In Chapter 5, we used logistic regression and k-nearest neighbors (KNN) to build the core classification workflow. Those models are valuable starting points, but they also impose constraints. Logistic regression is linear in its decision boundary, and KNN can become unstable or hard to scale as feature spaces grow.

This chapter introduces tree-based models as a more flexible extension of that work. Decision trees, random forests, and gradient-boosting methods can represent nonlinear relationships, interactions, and threshold effects that arise naturally in soccer data.

That flexibility is especially useful in soccer analytics, where performance often depends on combinations of context: distance and angle together, pressure and location together, possession state and game situation together. Tree-based models can capture those structures without requiring us to write every interaction by hand.

At the same time, tree-based models preserve an important link to interpretation. A single tree can be read as a sequence of soccer-style rules, while feature importance and Shapley additive explanations (SHAP) analysis help explain more complex ensembles.

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

• Understand how decision trees learn hierarchical rules from soccer data.

• Build, visualize, and interpret a classification tree model.

• Construct a random forest ensemble to improve predictive accuracy and stability.

• Identify ...