Over the past decade, football has undergone a significant transformation—not only in the way the game is played but also in how it is analyzed. Traditional statistics like possession percentage, shots on target, and pass accuracy still hold value, but modern analysts and coaches now rely on more advanced metrics to evaluate performance. One of the most influential among them is Expected Goals (xG).
This article will explain what xG is, how it’s calculated, why it matters, and how it can be used to interpret both individual and team performances more accurately than conventional stats allow.
What Is Expected Goals (xG)?
Expected Goals (xG) is a statistical model used to assess the quality of goal-scoring chances in a football match. Rather than simply counting the number of shots a team takes, xG evaluates how likely each shot is to result in a goal based on historical data.
Each shot is assigned an xG value between 0 and 1, where:
- 0 means no chance of scoring
- 1 means a 100% chance of scoring
For example:
- A tap-in from 3 meters might have an xG of 0.90
- A long-range shot from 30 meters might have an xG of 0.02
The main goal of xG is to quantify chance quality. Over time, it allows us to answer deeper questions:
- Is a striker finishing better or worse than expected?
- Is a team creating high-quality chances or relying on low-percentage shots?
- Was a win deserved based on the quality of chances, not just the number of goals?
Why Is xG Important?
Football is a low-scoring sport where randomness and small margins often decide games. xG helps to cut through that randomness by showing what “should” have happened based on chance quality.
1. Performance Evaluation
xG allows coaches, analysts, and fans to evaluate how well a team or player is performing regardless of the scoreline. A team might lose 1–0 despite creating far better chances than the opposition. The xG numbers might show:
- Team A: xG 2.1
- Team B: xG 0.6
In this case, Team A was statistically unlucky or inefficient, while Team B was fortunate or highly clinical.
2. Measuring Finishing Ability
By comparing a player’s xG to their actual goals, we can measure finishing efficiency:
- A player outperforming xG is likely a clinical finisher
- A player underperforming xG might be struggling with finishing or decision-making—or just unlucky
3. Scouting and Recruitment
Scouts can identify players who consistently get into high-value shooting positions (high xG per 90), even if they’re not scoring. These players may become prolific goal scorers in a better system or with development.
4. Tactical Insights
xG helps teams and analysts evaluate tactical effectiveness—whether the team is creating good chances or relying on low-percentage efforts.
How Is xG Calculated?
xG models are built using historical shot data from thousands (or millions) of shots across multiple competitions. Each shot outcome (goal or no goal) is analyzed in the context of multiple variables. These variables differ slightly between data providers (Opta, StatsBomb, Wyscout, etc.), but typically include:
Shot Location
Shots closer to the goal generally have higher xG values. Central locations are more valuable than wide or tight-angle positions.
Shot Type
Headers generally have a lower chance of scoring than shots with the foot. Volleys, backheels, and bicycle kicks are also given lower xG due to difficulty.
Assist Type
Was the shot assisted by a through-ball, cross, cut-back, or set piece? Cut-backs and through-balls usually lead to higher xG chances due to clearer shooting opportunities.
Body Part
Shots taken with a player’s stronger foot may be more accurate, though some models don’t distinguish between left and right.
Defensive Pressure
Was the shooter under pressure from defenders? Was there a clear path to goal?
Goalkeeper Positioning
While basic xG models don’t include this, more advanced models (like post-shot xG) do factor in where the shot was placed and how likely the goalkeeper was to save it.
These variables are fed into a machine learning model to estimate the probability of a goal from each type of shot.
xG vs Post-Shot xG (PSxG)
It’s important to distinguish between xG and post-shot xG (PSxG):
- xG measures the quality of a shot before it’s taken, based on location and context.
- PSxG takes into account shot placement (top corner vs straight at the keeper) and is useful for evaluating goalkeeping and finishing skill.
For example:
- A 1v1 chance that is scuffed straight at the keeper might still have a high xG but a low PSxG.
- A tight-angle shot perfectly curled into the top corner may have a low xG but high PSxG.
Real Match Example: xG in Action
Let’s say Manchester City beats Brighton 2–0. Here’s a breakdown:
Interpretation:
- City created more and better chances, even though they didn’t convert all of them.
- Brighton had a few decent chances but didn’t take them.
- The xG numbers suggest a fair result, although City arguably could have scored more.
Now imagine the same game ends 1–1. xG still tells us City dominated in chance creation, even if the score didn’t reflect that.
Team vs Player xG
Team xG
This is the sum of all individual xG values for a team. It’s useful for evaluating overall attacking performance, comparing game-to-game consistency, and predicting long-term results.
Player xG
This measures the quality of chances a player receives. Over time, players who consistently accumulate high xG tend to be intelligent in their movement and positioning, regardless of whether they finish well.
For example:
- Erling Haaland: High xG per 90 due to excellent positioning, movement, and volume of chances.
- A deep-lying midfielder might have low xG but still contribute tactically or creatively in other ways.
Limitations of xG
xG is a valuable tool, but like all metrics, it has its limitations.
- Doesn’t Account for Individual Skill
A 0.10 xG chance might be far more likely to be converted by Lionel Messi than by an average player. xG models treat all players equally. - Doesn’t Measure Decision-Making
A player choosing to shoot instead of passing to a better-positioned teammate might inflate their xG without improving team output. - Doesn’t Include All Contextual Factors
xG doesn’t capture match context, momentum, psychological pressure, or unusual game states like red cards or late-game chaos.
How to Use xG in Football Analysis
Some practical uses of xG in performance analysis:
- Compare a team’s xG to actual results to identify under- or overperformance
- Track a player’s xG and goals over time to evaluate finishing trends
- Monitor rolling xG averages to study consistency and attacking development
- Use xG conceded to evaluate defensive structure and goalkeeper effectiveness
- Combine xG with video analysis to provide tactical and contextual insight
Final Thoughts
Expected Goals is more than just a buzzword in modern football—it’s a foundational tool for understanding the game at a deeper level. It helps explain performances, uncover trends, and evaluate players and teams beyond the surface level of goals scored.
When used correctly and in combination with qualitative analysis, xG adds a layer of objectivity to football that enhances tactical understanding and long-term decision-making. Whether you’re a coach, scout, analyst, or just an engaged fan, mastering xG is essential in the modern football era.