Soccer Analytics with Machine Learning

Chapter 1. The Soccer Analytics Landscape

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It is Saturday evening. You are at home watching Liverpool play. Before kickoff, you placed a small, casual bet on them to win—not as a professional strategy, but simply to make the match more engaging. As the game unfolds, your phone rests nearby with a live betting app open. The win probability updates continuously.

Liverpool strings together several passes in the opponent’s half. The probability shifts from 54% to 57%. A sudden counterattack follows. A dangerous shot. The probability drops to 49%.

The score has not changed. Yet your confidence moves with the numbers.

This experience captures a defining feature of modern soccer: the game is no longer interpreted solely through narrative and intuition. Increasingly, it is accompanied—and often understood—through probabilities.

But when a model assigns a 62% chance of victory, several natural questions immediately arise. What determines those probabilities? What assumptions underlie that number? How ...