XG stats in sports betting

XG stats in sports betting

In recent years, in football, more and more attention is paid to extended statistics, and one of its most important indicators is xG (expected goals). What it is, how to calculate it and use it in making a prediction for a match, we will tell you in our article.

XG stats in sports betting

What is xG

Every football fan knows that the final score on the scoreboard is most important, but it does not always reflect what is happening on the field. How many matches were there, where one team attacked all 90 minutes, struck more than a dozen shots on someone else’s goal, and in the end lost because of the only successful counterattack of the opponent. If you do not watch the match, you may never know the true reason for such a result. But the xG statistics help to solve this riddle and understand what exactly led to this result.

xG (from English expected goals) Is a model that calculates the number of goals to be expected based on the hazard of the strikes. Each shot on goal has its own coefficient, and the sum of the values ​​determines the level of danger created at the opponent’s goal. Comparison of xG indicators helps to more accurately predict the final score of the fight. Of course, the number of goals expected will not necessarily help predict the outcome accurately. There are a lot of examples in football when these indicators did not coincide with the score on the scoreboard.

There can be many reasons: the level of motivation of the players, luck, skill of the attackers and goalkeepers, refereeing, and so on. Leadership in terms of xG unconditionally shows only one thing: which of the rivals put more effort into winning 3 points in the match. The importance of xG has been proven time and again and is used by many football coaches in their analysis of matches.

How to calculate xG

If earlier only the main minds of the coaching department could independently calculate these statistics, today any person who understands football at least at an average level can do it. There are many calculation models, but none of them is ideal. They take into account a different number of indicators, but the following are usually taken as a basis:

  • Distance to the gate;
  • The place from which the player hits;
  • How many times the player touched the ball before hitting;
  • Which part of the body the player hit the ball.

When calculating, all these criteria are evaluated and summed up, and as a result, the specific weight of the moment is determined, which shows the degree of danger of an impact. The xG model evolves along with football. Every year new parameters for analysis are added to it, which make it possible to carry out it more and more accurately.

The main disadvantages of the model

While this statistic is advanced, it has certain drawbacks. The main one is this: xG cannot reliably determine who will win the match, because football is primarily a game, and not just dry numbers. It is impossible to guess in advance what attitude the players will have for the match, how the game will develop, which side will be lucky, whether the referees will act correctly, how a potential quick goal will affect the course of the match. This all applies to a specific match, but the sample for the season is sure to be more accurate? – Yes, but not completely.

On average, teams deliver more than 500 hits per season, and this is not such a large indicator that reduces the margin of error to zero. In addition, the team may undergo serious changes during the season: the coach will change, the main scorer or the main goalkeeper will be injured, the tactics for the match will change depending on the standings. And the xG model takes into account only the strikes delivered, but it cannot calculate the unapplied strikes. For example, a footballer entered an excellent striking position, but kiksanul and did not hit the ball.

That is why it is important to take into account not only xG, but also other important statistics: hits and inaccurate shots, ball possession percentage, number of corners taken, fouls, yellow cards, and so on.

How to use xG in betting

The xG model is primarily useful at a distance or when the player has not watched the match. Then he can determine how logical the outcome was, who owned the advantage and how he disposed of the chances. If you watch matches, you will already have a clear idea of ​​how they played out and who deserved to win.

Using xG, the player can calculate how many goals each team has scored and how many they have scored in the end. Observing this indicator at a distance, you can determine which teams consistently score more than they create, and which, on the contrary, less. In this case, you can track a series of successful or unsuccessful matches and find values. For example, on a team that has been scoring more than it creates for a long time, you can bet on the total less, and on a team that has long been scoring less than it creates, you can bet on the total more. However, not all so simple.

This method of playing has one big drawback: it is impossible to predict exactly when this or that series will end. This may happen in the next match, or maybe in the next season, and there are plenty of such examples. Here, as in the case of the catch-up, according to the theory of probability, sooner or later the player will nevertheless win and make a profit, but it is impossible to predict exactly when this will happen. A series of failures can drag on so long that the entire pot will be dumped even before the bet is passed, which compensates for all previous failures and makes a profit.

Speaking about the use of xG statistics in betting, I would like to emphasize that it is indeed a useful tool in making a prediction for a match, but in no case should it be taken as a basis. Think of xG only as an addition to the rest of the criteria for making a prediction and compensation for a match that has not been watched (after all, absolutely all games cannot be watched). In this case, your prediction for the match will be more accurate and the chances of winning will increase. But do not try to look for values ​​only on the basis of xG – this will inevitably lead to the loss of the pot.

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