“Team A is better than Team B in attack; Team A recorded 10 shots while Team B had only 5″—we hear this narrative quite often when reading or listening to match analysis. It isn’t wrong, but it lacks specificity. What if only three of Team A’s 10 shots were taken from inside the penalty area?

To score goals, a team must create chances that are not only numerous but also of high quality. Team B might have taken only five shots, but all of them originated from inside the penalty area. This means Team B created high-quality chances, rather than simply firing shots from outside the box like Team A.

The “expected goals” (xG) statistic represents the probability of a shot resulting in a goal. xG illustrates the quality of a chance. Each shot is assigned an xG value ranging from zero to one, where zero implies an impossible goal and one implies a guaranteed goal. But, since nothing in football is certain, xG values ​​never actually reach zero or one. How could this happen? You have to see our explanation because this is important to you before decide to access we88 and choose which is best.

Shots?

Opta has played a pivotal role in developing this statistic. xG figures are derived from the analysis and processing of over 300,000 shot data points held by Opta. Each data point carries specific attributes—such as distance and angle relative to the goal, whether the shot was a header or taken with the foot, and the type of pass. These attributes determine the magnitude of the xG value.

The further a shot is taken from the goal, the less likely it is to result in a goal. Similarly, the narrower the angle, the lower the probability of scoring. Headers are more difficult to convert than shots taken with the foot, and shots following a low pass are easier than those following a high, aerial pass. Each of these factors is assigned a value and then processed mathematically to calculate the final xG figure.

For instance, consider 20,000 shots sharing specific attributes: a particular distance and angle, a shot taken with the foot, and a low pass. If 2,000 of those shots resulted in goals, then any shot with those exact attributes would be assigned an xG value of 2,000 divided by 20,000. …with 0.2. In short, that is how the xG figure originated.

In practice, xG has numerous applications and is useful for both individual and team analysis. While providers like Instat and Wyscout offer xG data for a fee, it is available for free on Fbref; ultimately, however, these providers rely on Opta as the source of the raw data.

Several conclusions can be drawn from the data regarding the English Premier League’s top scorers and their total xG for the 2019/20 season. As the top scorer, Jamie Vardy showed a relatively small difference between his actual goals and his xG compared to the other top players. This indicates that Vardy frequently found himself in excellent scoring positions and converted those chances effectively.

For example, Harry Kane’s total xG was nearly half that of Vardy. Kane did not receive many high-quality chances—likely due to a lack of creativity at Tottenham following Christian Eriksen’s departure and Dele Alli’s dip in form. Nevertheless, Kane demonstrated his clinical finishing ability; the significant gap between his actual goals and xG suggests he was able to convert chances that, statistically, had a low probability of resulting in a goal.

xG is not limited to analyzing strikers; it can also be used to evaluate goalkeepers. There is a metric known as “post-shot expected goals” (PSxG), which measures the xG value of the shots a goalkeeper faces. The difference between goals conceded and PSxG can be used to assess a goalkeeper’s shot-stopping ability. Can you guess who had the worst differential last season? It was Kepa Arrizabalaga.

Learn from example

Regarding team analysis, Liverpool’s comeback in the 2018/19 Champions League semi-final offers an interesting case study. Barcelona won the first leg convincingly at Camp Nou with a 3–0 scoreline. The result suggested Barcelona’s dominance and implied that Liverpool was completely outclassed. However, the xG figures for both teams were actually quite close: Barcelona recorded 2.3, while Liverpool recorded 1.6.

These figures indicate that Liverpool played reasonably well but failed to capitalize on their chances effectively. This implies that Liverpool’s level was not vastly inferior to Barcelona’s; rather, that specific match was simply an off day for the Reds. It is hardly surprising, then, that they managed to turn the tie around with a 4–0 victory in the second leg. In that match, Liverpool recorded an xG of 1.6 compared to Barcelona’s 1.0.

Another interesting xG visualization is the “xG story” or “xG timeline.” This data visualization illustrates a team’s cumulative xG over specific time intervals. The xG story is a new feature in the *Football Manager 2021* game, while real-world xG stories can be accessed via Understat.

We can observe the specific time intervals during which a team creates numerous chances. A stagnant graph indicates that the team did not take any shots during that period. Several factors can influence this, such as the coach’s tactics, the players on the pitch, or levels of stamina and concentration.

One example is the match between Barcelona and Real Betis, which ended 5–2. Ronald Koeman benched Lionel Messi, a decision that contributed to Barcelona being held to a 1–1 draw in the first half with an xG of 1.44.

Messi came on at halftime and performed impressively; he frequently threatened the opposition goal and ultimately scored twice. This is reflected in the second-half xG graph, which rose steadily until Barcelona finished the match with a total xG of 4.44. The second-half xG figure was clearly higher than the first, indicating that Messi’s introduction made a significant difference to the quality of Barcelona’s attack.

Despite its various uses, xG also has limitations. Opta’s xG figures do not factor in the positioning of opponents who might be blocking the shooting lane. Furthermore, xG does not account for individual player ability; for instance, a shot with an xG of 0.1 for Messi is vastly different from one with an xG of 0.1 for Jesse Lingard.

While xG offers deep insights, visual observation remains crucial in football. Statistics serve to complement analysis derived from watching the game; football cannot be analyzed solely through statistics without visual observation. However, unaided observation—lacking statistical backing—results in an analysis of unverified validity. Statistics can be a valuable asset when employed correctly.