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Expected Goals in Football: A Concise Guide /

Expected Goals in Football: A Concise Guide

Analysis of sports data is now a part and parcel of virtually all games being played today. You'll notice that when yu watch a game on TV, there's an endless parade of facts and figures, some of which appear to be very obscure and yet are important to the match or game play. An so it is with Advanced Metrics. These are used to measure any number of factors, with the latest of these being in football, with Expected Goals.

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Introduction: What are Expected Goals?

It’s a mathematical measure of whether a given shot will result in a goal. The factor is written as “xG” and is between 0 -1. The metrics will take into account a number of related details such as historical data. The idea is to allow the punter to predict how many goals the team “Should of” scored judging by the quality of chances they had during match play. Betting on expected goals is a great way to win the jackpot online.

Expected Goals: Glossary

There are many variations of xG’s, these include:

Expected Goals (xG) – The volume of goals that either a player or team will be expected to score based on the factors that a model takes into consideration.
Non-Penalty Expected Goals (npxG) – The total expected goals minus any expected goals from penalty attempts.
Expected Goals For (xGf) – The amount of goals a team is expected to have scored based on the expected goals data.
The Expected Goals Against (xGa) – The number of goals a team should have conceded based on the expected goals data.
Expected Goals Assisted (xA) – The total number of assists a player should have produced based on expected goals taken directly from their passes.
The Expected Points (xPts) – The number of points a team is expected to have won in relation to the expected goals data.

Expected Goals: More Detail

When a player has a 100% chance of scoring, that is show as an xG of”1″, the highest value. So the higher the value, the more likely the player will convert the goal scoring opportunity. For even greater analysis, then we can turn to the npxG numbers. Penalties carry an xG of 0.76, and they can significantly distort a player and a team’s expected goals. Another point to keep in mind, is that most penalties are not earned or deserved, thereby further distorting the relevant data.

Before the use of Expected goals, bettors used to rely on data such as “total shots” and more specifically, “shots on target”. These factors were used when calculating a final score line. But they can prove deceptive if you consider that a shot with an xG of, say, 0.13 is classed the same as a shot of 0.83. As an example, imagine that Team A made a total of 17 shots during a match, whilst it’s opponents, Team B, took only 8. With this example, we would expect that Team A deserved the win. But xG data shows that Team A had an xG of 1.34 from the game, whereas Team B had an xG of 2.18.

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One of the problems and therefore criticisms of xG data, is that is doesn’t take the player’s skill level into account. These two factors can be summarized as: the skill of the player shooting vs the skill of the goalkeeper he’s facing. It goes without saying that both of these factors will heavily influence the final xG score. This should be kept in mind when using these figures.

How Is xG Calculated

There are a number of different models in use to calculate these goal values. The most well known is from Opta, who use over 300,000 shots in their analysis. There are any number of variables to take into account. These can include, among others:

Distance from goal – Generally, the closer you are the higher the xG.
Angle of the shot – Generally, the more acute the angle, the lower the xG.
Shooting part – Was it with the strong foot, weak foot or a header?
Passage of play – Was it from open-play or from a set piece?
Chance creation – Did the opportunity come from a cross, a through ball etc?
The shot – Was it from a rebound, did it come after beating an opponent etc?

xG: Attack And Defense

Though the data can give a pretty good indicator of goal analysis, there are more complex models out there. These will look into such factors as defensive play. How strong a defense is matters in the ability to find openings and chances to score. The same goes for attacking data. So by also including this data, it will create an even more accurate picture. To learn more check out our guides to win the jackpot.

So there you have it. Expected goals are simply a means of assigning a “quality” value to each goal scoring opportunity. It’s a growing trend and the data-base is being constantly updated. Of course, the more data we have at our disposal, the more accurate our predictions may become. But do keep in mind that the analysis is not 100% reliable or necessarily representative of all goal scoring situations. There will always be outliers.