The use of expected goals (xg), a statistical measure used in football analysis, has grown exponentially. The term is usually abbreviated to xg; therefore, when you read about a team’s performance not just by the final score of a game but also by an xg figure – e.g., “1.8 xg” or “0.6 xg” – you’ve probably been exposed to some sort of football analysis either in terms of building up towards a match, analyzing Predictions using platforms related to football analysis, or after a game analyzing statistics.
This article authored by the bettingvoice analytical team provides insight into what xg actually represents and demonstrates how analysts and prediction platforms utilize it to make pre-kick off, more informed Predictions on future matches.
What does xg represent in football?
Xg quantifies the quality of a potential scoring opportunity, and not simply if the potential scoring opportunity resulted in a goal. Each shot is assigned a probability value between 0 and 1 that signifies the probability that an average player would achieve a successful shot from that location/situation; utilizing historical data from thousands of other similar shots as reference.
For example, a shot taken very close to the net could have an xg of .85 whereas a long range attempt under duress could be worth only .03. Therefore, if you add each shot’s value together across a given match, you will obtain the total xg of a particular team which indicates the quality of opportunities created by said team, independently of the degree of fortune of finishing.
How are xg values determined?
Typically there are several variables that factor into an xg model:
- Distance from goal: shots near the goal area are scored on more frequently than those farther away from the goal.
- Angle of the shot: a shot taken directly at the center of the net affords a greater likelihood of success than a tight-angle shot.
- Type of assist: through balls/cut backs generally present a better quality scoring chance compared to long passes.
- Body part utilized: generally speaking, header attempts are less successful than attempts made with the foot.
- Defensive pressure: was the attempted shot un-marked or were they pressed?
Different data providers such as Opta build their respective models with slightly different weights on these factors. This is why you may observe slight variations in the reported xg values for the same match depending upon which provider you choose to rely on.
Why should I care about xg rather than simply focusing on the scoreline?
Teams can certainly go down 1-0 yet play far superior soccer and/or conversely teams can win 2-1 yet dominate the creation of scoring opportunities. Using xg helps differentiate between performance and result. Therefore, xg can help expose patterns within a team’s performance that are otherwise obscured by the simple scoreline.
If a team continually exceeds their xg values, it is possible that the team relies too heavily upon their ability to finish opportunities; something that is difficult for them to maintain going forward. Conversely, if a team continually falls short of their xg values, it is possible that they are experiencing bad fortune in front of the net and should expect an eventual reversal in fortunes regardless of how poorly they appear on paper.
How is xg incorporated into match previews & predictive models?
Analysts of predictive models incorporate xg in several concrete ways:
- assessing form beyond results: over time, the trends exhibited in a team’s xg metrics provide a clearer indication of that team’s true level of performance relative to their mere win-loss record.
- goal market assessment: the cumulative xg values for both participating teams contribute to assessing over/under bets on totals regarding goals. Teams with high xg values are more frequently associated with higher-scoring contests.
- identifying discrepancies between performance & result: if a team’s xg values suggest that they are playing better than their recent results indicate, then that discrepancy is where additional insights come from.
- head-to-head context: when comparing xg for vs. xGA against for multiple matchups, provides a richer context for understanding a matchup than merely examining previous match histories between the two sides; particularly when changes occur in squad or management.
Using xg to inform the selection process for 7’s banker tips
When teams’ xg for and against exhibit similarities across multiple contests, and further reinforce consistency via team news, form and head-to-head context, then this presents an opportunity to identify a contest that stands apart from others throughout the remainder of the card. That process illustrates how 7’s banker tips work; i.e., identifying selections that represent a stronger combination of evidence than others.
In gambling terminology, a “banker” refers to the selection(s) in which an analyst has the greatest amount of faith. A “banker” is not necessarily indicative of certainty. An analyst cannot guarantee a favorable outcome. Regardless of how many positive indicators exist around strong xg numbers for and against, numerous external events can negate those positive indicators including red cards, injuries or deflected goals. Thus, always consider any “banker” recommendation as nothing more than an educated guess.
Limitations of xg
Xg is not a perfect forecast tool. As such, xg does not take into consideration:
- game state: a defensive-minded approach by a leading side may include fewer high-quality opportunities.
- specific finishers: a talented finisher can exceed all model-based expectations.
- Set piece specialists/penalty taker conversion rates: players who specialize in Set pieces or penalty kicks possess significantly better conversion rates than players who do not specialize in these areas.
- one-time factors: weather conditions, referee decision making, red cards, etc…
Due to these limitations, xg works best as one component amongst others in developing predictive models. Additional components include team news/tactical setups/head-to-head history.
Want to see how these numbers feed into daily selections? Browse today’s picks on 7’s Banker Tips. Predictions are for information only, so please bet responsibly and only with what you can afford to lose.
Frequently asked questions
what is xg (expected goals)
xg is a statistical measure of the probability of a shot on target resulting in a goal. It accounts for all factors related to shots including distance, angle and type of shot.
What is a good xG score in football?
there isn’t a specific number, however; teams that produce .15 or more xg per game are generally developing many scoring opportunities. teams with less than .10 xg per game are generally having few scoring opportunities.
Is xG the same as expected goals against (xGA)?
No. xg measures the quality of the chances being generated by your team while xGA measures the quality of the chances you’re giving up. Both are important statistics when trying to determine whether a team has offensive/defensive balance.
Can xg be used to predict what the outcome of a match will be?
No. xg is simply a measure of the quality of chances created over long periods of time. xg should be viewed as just one tool for assessing a team’s performance and cannot be relied upon as the sole means of predicting match results.
How does xg fit into 7s bankertips.com?
xg is just one input we use to assess how good our picks are. along with other forms of data such as injury reports, team trends and head-to-head history. bankers are selections made on extremely high confidence levels; however, they do not provide guarantees for match outcomes.
