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Metrics glossary

Expected Goals explained

Get a quick breakdown of Expected Goals, plus related metrics and visualization tips. Explore charts, comparisons, and scouting insights with FBPlot.

Category: attackingMetric ID: expected_goalsUsage: Scouting, reporting, and benchmarking
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When you share Expected Goals, include the definition so non-technical audiences understand the impact.

Charts turn Expected Goals into an easy story. Start with a radar chart for a broad scan, then isolate the metric in a bar chart.

Category
attacking
Metric ID
expected_goals
Usage
Scouting, reporting, and benchmarking

Expected Goals definition

Expected Goals (xG) is a probability metric that estimates how likely a shot is to be scored based on historical outcomes of similar chances. Most xG models use features such as shot location, angle, body part, assist type, and sometimes game state to assign each shot a value between 0 and 1. Summed over time, xG estimates how many goals a player or team would be expected to score from the quality of chances created, making it a cornerstone of modern football analytics.

How analysts use Expected Goals

xG is widely used to separate sustainable performance from short-term variance. Comparing goals to xG can indicate overperformance (finishing hot streak) or underperformance (poor finishing or strong goalkeeping faced). For player scouting, xG per shot helps evaluate shot selection and chance quality, while xG per 90 helps evaluate how frequently a player gets into dangerous scoring positions. For tactical analysis, xG can validate whether a team's style is generating consistently high-quality chances or relying on low-probability shots. Because xG models vary across providers, the best comparisons use the same model for all players and seasons, and analysts often supplement xG evaluation with shot maps and video review to understand the tactical origins of chances.

How to interpret Expected Goals

Use Expected Goals alongside related metrics in the attacking category to understand role fit and tactical impact.

  • Compare within the same competition or position group
  • Use percentile ranks to normalize minutes played
  • Combine with at least one supporting metric

Best charts for Expected Goals

Radar charts surface it in context, while bar charts isolate the metric for direct comparisons.

  • Radar chart for full profile context
  • Bar chart for side-by-side comparisons
  • Exported visuals for reports and social sharing

Sources and definitions

FBref xG model explained

Modeling shot quality and xG methodology.

Open source
Hudl xG explained

Overview of expected goals and usage.

Open source
Opta definitions overview

Opta statistics definitions overview.

Open source

Start building charts

Turn Expected Goals into shareable charts for reports and scouting decks.

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Related metrics

Metric glossary

Goals

Goals is part of the attacking dataset used for player comparison and charting.

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Metric glossary

Assists

Assists is part of the attacking dataset used for player comparison and charting.

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Metric glossary

Goals + Assists

Goals + Assists is part of the attacking dataset used for player comparison and charting.

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Metric glossary

Shots

Shots is part of the attacking dataset used for player comparison and charting.

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Metric glossary

Shots on Target

Shots on Target is part of the attacking dataset used for player comparison and charting.

Explore metric
Metric glossary

Shots off Target

Shots off Target is part of the attacking dataset used for player comparison and charting.

Explore metric

Frequently asked questions

What does Expected Goals measure?

Expected Goals is part of the attacking dataset used for player comparison and charting.

When should I use Expected Goals?

Use Expected Goals when you need to evaluate attacking contributions and compare players in similar roles.

Which charts highlight Expected Goals?

Radar charts give context across metrics, while bar charts isolate the metric for direct comparisons.

Where can I learn related metrics?

Use the metrics glossary to explore complementary stats in the same category.

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