Why xG doesn't tell whole story in modern football analysis
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But as football analytics continues to evolve in late 2025, one thing is becoming increasingly clear: xG is a baseline, not a verdict.
Whilst it remains the most widely used tool for measuring chance quality, it has notable blind spots that can distort how a 90-minute match is understood.
xG Is Built on Averages - Football Is Not
The most fundamental flaw in traditional xG models is that they are built around the average player.A shot rated at 0.10 xG means an average professional scores it 10% of the time. But football is not played by averages as it is defined by elite outliers.
When a chance falls to an in-form finisher like Erling Haaland, the real probability of scoring is significantly higher than the model suggests.
Conversely, the same chance falling to a centre-back is far less likely to end in a goal.
By treating every shooter equally, xG ignores the very finishing skill clubs spend hundreds of millions to recruit.
What xG Can't See
xG only measures shots that actually occur. It is completely blind to several crucial elements of match danger.The "Almost” Chance
A striker sliding inches away from a tap-in across goal generates 0.00 xG, despite being a heart-in-mouth moment for defenders and fans alike.Tactical Sacrifice
A team protecting a 1–0 lead late on may deliberately stop attacking. A low xG total doesn't mean they were outplayed as it reflects a strategic choice.Red Cards
Teams reduced to ten men will inevitably concede more shots, but xG does not automatically adjust for the difficulty of defending with fewer players.In isolation, xG often fails to explain why a game unfolded the way it did.
When Volume Becomes Misleading
One of the most common misinterpretations of xG is simple accumulation.If a team takes 20 shots from 30 yards, each worth 0.03 xG, the total rises to 0.60 xG. Fans may claim they were unlucky not to score.
But 20 low-quality efforts are not the same as one clear chance.
The same issue appears in shot sequences.
A rebound scenario - a shot off the post (0.50 xG) followed by a blocked rebound (0.30 xG) - produces 0.80 xG for a passage of play that could only ever result in one goal.
xG counts volume well, but it often struggles with context.
Why Post-Shot xG Matters
Traditional xG measures the probability of scoring at the moment of the shot - not where the ball actually ends up.This is why analysts increasingly rely on Post-Shot Expected Goals (PSxG), which evaluates shot placement within the goal frame.
A player may take a high-xG chance but hit it straight at the goalkeeper. Traditional xG suggests bad luck; PSxG reveals poor execution.
Likewise, elite goalkeepers consistently outperform xG models by saving shots that theory suggests should result in goals.
What xG Measures - And What It Misses
| Aspect | What xG Measures | What It Misses |
|---|---|---|
| Player ability | Average finishing | Elite or poor individual skill |
| Defence | Distance, angle, pressure | Goalkeeper quality |
| Match flow | Discrete shots | Dangerous crosses and near misses |
| Context | Ball location | Psychology and momentum |
So, How Should xG Be Used?
xG remains a powerful long-term tool.Over a full season, teams with strong xG numbers usually finish roughly where they deserve.
But in individual matches, it should be treated as a starting point for discussion - not the final judgement.
xG explains what should happen in a world of averages, but football, however, is decided by moments of brilliance, poor decisions, elite finishing and human pressure - the very elements no model can fully quantify.
Used wisely, xG informs. Used carelessly, it misleads.
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