Expected Goals Betting Guide: What is xG and How it Works

What are Expected Goals (xG) in Football?

Expected Goals, popularly symbolised as xG, measure the probability of an individual shot becoming a goal. The metric is expressed in numbers between 0 and 1 and is mostly used to determine the quality of a shot.

xG is usually determined by crunching many historical data of different shots and evaluating them against a range of factors.

All expected goals calculated depend on factors such as quality and type of pass received, goal distance, shot angle, the number of players between the person taking the shot and the goal, and whether the chances created landed at the player’s weak or strong foot. These aspects help determine the XG of a shot and the expected goal scoreline of the match.

Origin of the xG Model

The xG term first appeared in 2012 at the MIT Sloan Sports Analytics Conference. This was on a paper by MIT student Brian Macdonald explaining ice hockey performance. Later, Brian's methods to calculate expected goals were published in a study that saw the xG model's birth. Later in April of the same year, Sam Green used the expected goals model to assess English Premier League goalscorers. xG models have since been used to determine the probability of shots becoming a final score.

How are Expected Goals Calculated?

Various models are applicable when it comes to calculating expected goals. However, every xG model considers special variables, most of which have been explained above. By watching a football match, you can determine the xG of a shot. For instance, when looking at the distance from the goal, proximity shots usually have a higher xG. It is mandatory to determine whether it was open play or a set piece, such as a free kick. Other essentials that determine the final result of the xG calculated include chance creation, the shot itself, the shooting part, and the shot's angle, which considers a particular position the player was taking the shot from.

Other complex models also account for the defensive play of the opponent. After all, this factor plays a major role in determining whether the attacker will make the score. All these xG data have an impact on the expected goal metric. To put every aspect into perspective, a short pass that lands on a player’s strong foot in open play has a higher XG than a corner kick from 30 yards at a much narrow-angle. xG value denoted as 0.35 means the shot is scored 35% of the time. Besides, the expected goal value can be described as 0.6xG, which means the shot should be scored 60% of the time. Players’ or teams’ xG rating combinations can help determine the number of goals they should have scored.

Variations of xG

Numerous variations come to light when calculating the expected goals of teams and players. Some of the top variations include:

Other xG variations not described include Player xG Stats and Team xG Stats. These are all useful expected goal variations that can help deduce important information about a team and its players.

How to Use Expected Goals Statistics in Football Betting

Besides using xG information to understand the football games’ and players’ statistics, football fans can use expected goals data in football betting. Since football is a low-scoring sport, it can be difficult to tell which team will score the most goals at the end of the match. However, using xG data, you can develop a winning prediction. The xG metric can also help determine if a team is likely to return to win a match, whether it is currently overperforming or underperforming. Lastly, can use xG data to create a correct table of the previous league and use the information to predict the future league or a particular league phase.

Conclusion

Expected goals are one of the most direct and useful metrics that you can use to measure the expected outcome of a shot becoming a goal. It has proven useful to teams, club management, sports analysts, and punters. Although not available at betting sites, you can place winning bets using the metric, increasing your profits. Besides, once you apply all the xG variables, you should easily calculate xG. After all, it does not require any analytical skills. If you have carefully reviewed this resource, you should understand the expected goals best.

Expected Goals FAQ