Improve your Football Prediction Skills with XG Stats

Football betting has never been the same again, especially now when there are advanced ways of predicting which team wins by calculating its expected goals.

One such way of determining the match winner is the Expected Goals (xG). The model started a while ago as a minor tool by individuals. But at the moment, it has gained immense popularity such that even the Premier League managers are now using the tool to predict their performance.

What exactly is the xG model, and how does it work? Let’s find out in this article.

What is XG Stats?

XG Statistics is a method of football match results prediction that tries to mathematically calculate the number of expected goals in a particular game (Expected Goals) that each team scores. In the model, each goal is valued between 0.01 and 1. When the chances are accumulated, it gives the total expected goals, becoming a crucial comparison tool to determine the likely winner.

To get the xG value for each team, the method tries to quantify the chance of each shot becoming a goal. Consequently, a shot near the goal front catches a high xG value than a shot from a longer distance.

In addition to its application in predicting the number of goals scored by a team, the XG Stats model can also be used to determine the football stars. This is possible as the model paves the way to analyse players’ efficiency, effectiveness, and scoring ability.

When using the method, we can tell a player’s overall contribution to the overall goals scored by their team. For this reason, the model becomes a perfect way to determine football stars compared to goals.

How the xG Stats Model Works

Using the xG Stats model in predicting football outcomes is easy and difficult in an equal measure. To ensure that the model works perfectly, it is essential to start gauging and evaluating a team’s performance in previous matches. This way, you will tell xG for different matches a team has played, which could mirror a general trend for your current prediction.

To effectively use the model, it would be good to consider a tournament as it gives you various performances of a team in that tournament. In addition to qualifying the value of every shot by a team, there are other metrics that help determine its expected goals. These include the following:

• xG- Conceded goals: Refers to the goals scored against a team
• xPts- The total estimated points that can help determine the final ranking of a team in the tournament

Despite the above being essential ingredients in determining the expected goals, there is more that should be considered to determine the value of a targeted goal. Some of the factors include:

• Forward’s position
• Distance to the goal
• The shooting body part
• The position of the goalkeeper
• The number of defenders in front of the ball
• The height, angle, and the shot direction
• Whether it is a pass, rebound, or cross assist
• The number of touches before that shot

In a more advanced evaluation analysis, other factors include field and weather conditions, the tournament stage, and a player’s time on the field.

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Implementation of xG in Predictions

The football data experts are well set in utilising the xG Stats model. Most of them have hundreds of thousands of shots in their database to help them calculate the possibility of an attempt being scored from a particular position in the match.

This way, they can establish the most accurate value of an attempt, resulting in an accurate expected goal.

Poisson Distribution Method and xG Stats Model

The xG method of goals or player performance prediction is one of football’s most reliable prediction techniques. But, these might not always give the exact outcome.

To try and come up with an accurate prediction, the Poisson distribution method can be used to complement the xG Stats Model.

The Poisson distribution model is an arithmetic concept that makes it possible to convert numbers to goal-scoring probabilities. In this context of predicting the exact goals scored by a team, the model is applied to establish the number of times the scoring event is likely to occur at a given time, mostly in 45 or 90 minutes of a match. The model derives its name from French mathematician Simeon Denis Poisson.

Being a discrete probability-reliant method of prediction, the Poisson distribution tries to predict accurate scores by considering a team’s past performance in terms of goals in a season alongside any historical data.

The Poisson distribution model converts the total goals for and against to the real chances of the actual goals each team is scoring.  For instance, If Liverpool’s average for goals is 1.7 per game, the Poisson Distribution allocates the percentage of the goal as below:

• The chance of Liverpool scoring nil goals in their next match is 18.3%
• The chance of Liverpool scoring 1 goal is 31%
• Chance of Liverpool scoring two goals is 26.4%
• The chance of scoring three goals is 15%.

So far, it has been proved that combining the xG Stats model with the Poisson Distribution gives more accurate predictions than using common goals.

Conclusion

When betting, it is always a wise consideration to find working strategies, and xG, in combination with the Poisson Distribution model, is one of the best to consider. Therefore, it pays to understand how the strategy works and how you could apply it in your football betting escapades.