Expected Goals
Statistics have invaded everything. If you watch a football match on TV, you can know how much a player has run, the percentage of chances a shot had to end in a goal or how a team takes corners. If you hear it on the radio, you are bombarded with data and advanced statistics. When it comes to betting, we can leverage statistics in assessing odds. One of the parameters that we cannot lose sight of is Expected Goals (xG), the expected goals.
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What is Expected Goals
The Expected Goals concept is a statistical indicator that assigns the probability of a shot being a goal based on the characteristics of the play. Naturally, not all goal scoring opportunities result in the ball ending up in the back of the net, as there are multiple factors that influence this. With this indicator, we can estimate how many goals a team will score in a game, always from a theoretical perspective. The statistics can say one thing, but what happens on the field is another matter.
Typically, this indicator is measured on a scale of 0 to 1, with 1 meaning that a chance has a 100% probability of resulting in a goal and 0 meaning the probability is 0%. For example, if we say that a team has an xG of 0.25, it means that one out of every four chances will result in a goal. It's worth noting that this is an indicator that can be applied to teams, but also in the analysis of players or even defensive performance. Just as we analyze a team's expected goals, we can also analyze how many goals we expect a team to concede.
How are expected goals calculated?
The first step in calculating a team's xG is to choose which variables are influential or available. This is a relatively new statistic, and there is no standard way of calculating it. Therefore, you can compare xG in different statistical sources. If you decide to calculate it yourself, you will need to use the mathematical technique known as multivariate regression, calculating the factors to use and their weight in the final result. It's not easy, but it can be done in Excel. And what variables are involved? These are the most common ones:
- Distance to goal (the closer, the easier to score)
- Shot angle (the more centered, the easier the goal)
- Auction type (good leg, bad leg, head...)
- Rivals in front of the ball
- Players who accompany the play
- Occasion type (pass to foot, pass to space, set piece, counterattack)
- Home or away
Added to this are issues such as the score -it can generate more or less anxiety-, minute, one's own ranking and that of the opponent, distance to the nearest goalkeeper and/or defender, position of the goalkeeper with respect to the goal, how the ball is received, player's position -forwards have better shots than defenders-, speed and number of touches in the play... You can get the data from pages like Whoscored or Opta, which offer Philippines all kinds of statistics from the most outstanding leagues. The more data you have, the better.
What is Expected Goals used for?
Expected goals is a very interesting metric in the medium and long term. A single game is an insignificant sample, two or three will not tell Philippines much either, but as the season progresses, we can confirm the trend. xG can help Philippines understand why a team that creates a lot of danger has problems winning, and another that barely reaches the opposition area wins more easily. There are three aspects that we should take into account:
- Shot accuracy: if we have an xG of 0.3, three out of every 10 shots should end up as a goal. If this target is not reached, the team has a problem with accuracy.
- Quality of attack: when a team has a high xG, which is considered "high" with an index above 0.387, it means that the team has the ability to generate and convert goal-scoring opportunities. When the xG is low, it is synonymous with a team that attacks poorly and is unable to generate clear shots.
- Conversion rate of a scorer: Does a striker score more or less than expected? Great killers score more goals than their xG suggests.
When using Expected Goals in betting, we can apply it to markets like the match winner or the over/under bets, either for the match as a whole or for each team. For example, if two teams with a high xG face each other, it is easier for the over to be met than when two teams with a low xG play. Similarly, a team with a high xG will be favored if it plays against one with a low xG, especially if it plays at home.