Take Real Madrid’s 1-0 loss at La Cartuja to Betis. Real Madrid had 20 shots, yet failed to score. The score hardly reflected the chances Madrid had created. Expected goals (xG) can help explain that difference, but they can’t predict who will win the next match on their own.
Matches like this explain why expected goals (xG) have become so widely used in modern soccer analysis. xG is a statistic that gives an indication of the quality of a team’s chances irrespective of the final tally. Yet the question remains: is it any good at telling us how a LaLiga matchday will pan out?
What are expected goals (xG)?
The likelihood of a goal varies with the shot. Put a forward one-on-one with the keeper and he has a far better prospect than a midfielder who puts one in from 25 yards. xG is the means to put a number on that disparity.
Statistical models will run the numbers on thousands of shots from the past and take into account the angle, distance and the situation of the chance to arrive at an xG figure. An xG of 0.40, for instance, means there is a 40% estimated probability of it ending in a goal. Sum them up and you have the expected goals for the side; a measure of the quality of its play, though not a foregone conclusion that so many should have been put away.
Analyzing LaLiga results with expected goals
Barcelona present a case in point, quite unlike Real Madrid. Under Hansi Flick the club has made a strong start to the 2026–27 campaign with two Champions League victories and nine straight in LaLiga. In those eleven matches, Barcelona have scored at least twice in every game.
The advanced stats back up their form. As of 19 October 2026, xgEdge had them top of the league after nine matchdays with 33.49 xG, or 3.72 per game. They have also put 39 past opponents in LaLiga, more than was expected. But Barcelona are doing more than just finding the net. They’re creating clear chances regularly. So far their results are in keeping with the figures.
In that respect xG is of greater utility when you are looking at several matches as opposed to one.
Is there other data to consider in LaLiga?
For all their worth, expected goals do not tell the whole tale. One has to factor in the opposition, home and away form and absences from the squad. Then there is xGA (expected goals against) to measure the sort of chances a team lets in.
Put the two metrics side by side and you see if a side is more dangerous than it is vulnerable. But then again, a tactical shift or a key man missing through injury can change a team’s output from one week to the next. Statistics are useful in a match analysis but they must be viewed in conjunction with what is happening on the pitch.
The need for reliable statistics
There is no shortage of statistics for any given match these days, but the models differ from source to source and the outcome can be different. Specialized websites and media help make sense of these figures, particularly for readers exploring online sports betting, where understanding team performance and statistical trends can provide useful context.
It is one thing to know the xG a side has put up, another to appreciate how it went about it and if it has been consistent over a number of matchdays. Well sourced and explained information prevents hasty judgements being made on the strength of one number.
Can Expected Goals Predict Who Will Win?
Not on their own. Real Madrid created chances worth 3.46 xG against Betis and still lost. Barcelona, meanwhile, have been creating good opportunities almost every week, and their results reflect that.
Both examples show why expected goals (xG) are useful when analysing LaLiga. They can reveal things that aren’t always obvious from the league table, particularly when we look at several matches rather than just one.
But there’s more to football than statistics. That’s why reliable data matters, along with knowing how to interpret it. The numbers can tell us plenty about how a team is playing, even if they can’t tell us who’s going to win next weekend.
