The most useful xG site depends on the question behind the number.
A match ends 1–0, but two sites give the losing side different xG totals. That need not mean one is wrong: xG figures can vary with the shots counted and the model used to value them. A single total is most useful when its source stays consistent.
For a quick sense of how a match unfolded, a score app may be enough. Comparing a season calls for tables; examining a missed chance calls for a shot map. Keep comparisons within one source where possible, and treat forecasts as a separate question from what happened on the pitch.
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- - 01 Best for comparing player and team metrics.
FBref is a football reference site built around statistical tables. It suits readers checking how a player or team’s xG fits into a wider set of numbers, whether the question concerns recent matches or a season-long record. Advanced xG coverage depends on the competition.
Best for focused expected goals research.
Understat suits readers who want to look beyond a match’s xG total. Its shot map shows the individual chances behind that figure, making it easier to see whether the total came from one big opportunity or several smaller ones. Player and team views then help place the match in a broader scoring-chance pattern.
Best for checking xG alongside live scores.
FotMob keeps the score, lineups and player stats close to the match xG figure. For supported competitions, its shot map adds useful context: a narrow lead can look less convincing when the trailing side has created the better chances.
It is most convenient when a match is unfolding or has just ended. For a longer look at how a team’s chances develop across matches, Understat is a better next stop. The FotMob and Understat xG comparison sets out where their workflows differ.
FotMob makes xG easy to check without leaving the match page. Its strength is quick context around a result, not sustained historical research.
Best for pairing shot maps with model-based predictions.
Infogol pairs match shot maps and team analysis with model-based predictions. The shot maps help explain a match’s xG by showing where chances came from; the forecasts address a different question: what might happen next. A projected result should not be treated as another observed match stat.
That combination suits prediction-oriented exploration, but the forecasts deserve their own scrutiny. Readers comparing paid forecasting tools can also consider when premium xG data is worth buying.
Infogol is useful when shot-level context and possible future results matter together. Its predictions are best assessed separately from the underlying xG figures, not used as a neutral reference table.
For season-long player and team comparisons, FBref is the strongest starting point. Understat is better for tracing chances through shot maps, while FotMob suits quick matchday checks. Sofascore is useful for following football alongside other sports; Infogol fits analysis that also considers match predictions.
Before comparing figures, check that each site covers the competition and dates in question, then use the same matches and time periods. Small xG differences may reflect different models or update schedules rather than an error. For the underlying concept, see the practical introduction to xG.
Free match pages often show team and player xG alongside scores, shots and lineups. Some…
A shot’s probability is converted by the match into one of two outcomes: goal or…
