How to Use Expected Goals to Forecast Football Games: A Beginner’s Guide

More than 500,000 people follow The xG Philosophy on Twitter, a popular account that debriefs post-match expected goals (xG) results.

With so much interest in this metric, why not capitalize on its popularity?

Instead of merely reporting on outcomes post-facto, take control of the metric and use it to predict the outcomes of games.

This guide aims to help you leverage pre-game xG statistics to forecast the future results of football matches, extracting valuable insights about each team’s performance potential.

You will learn how to create pre-game xG match ratings for upcoming fixtures and how to link those to historical data to gain a clearer understanding of the most likely future outcome.

Most importantly, you will learn how to generate automatic game week reports in a matter of seconds with our specially designed application (in case you purchased the paid version that will come out in August).

This guide is organized into five sections.

1. We start with a synthesis of what this guide aims to achieve. We will explore how a seemingly simple statistic like expected goals can convey a wealth of valuable pre-game information.
2. Then we follow up with a primer on expected goals. We will define what xG represents, discuss its biggest advantages, and explain why it is an effective metric to measure a team’s form.
3. In the third section, we will detail how to transform years of historical data into actionable match-day insights. In particular, we will demonstrate the logic behind creating an xG difference match rating system that quantifies the likelihood of a football game ending in a home win, draw, or away win.
4. In this fourth section, we will demonstrate how to combine data-driven xG insights with league-specific knowledge effectively. You will learn how to analyse the results from xG reports that you have created and refine the likelihood probabilities and trends that these reports generate. This process involves critically evaluating the data-driven insights to either challenge or confirm their validity, thereby providing a more nuanced understanding of what xG data can – and cannot – reveal about upcoming matches
5. In this fifth and final section, we will illustrate how to launch and use our application that will automatically generate game week xG reports for the football leagues available on FBREF: the English Premier league and Championship, the Italian Serie A and B, the German First and Second Bundesliga, the Spanish La Liga and Segunda Division, the French Ligue 1 and 2, the Dutch Eredivisie, the Portuguese Primeira Liga, the Belgian Pro League, the American Major League Soccer, the Mexican Liga MX, the Brazilian Serie A and the Argentinian Primera Division.

Note that the paid version coming out in August will cover all 17 leagues. The free version (available today) includes a working application covering the American Major League Soccer, the Brazilian Serie A, and the Argentinian Primera Division.