How to Use XG and XA Analysis for Player Comparisons: A Liverpool FC Fan’s Guide

Understanding player performance in modern football requires more than just watching the game or checking goal tallies. Expected Goals (xG) and Expected Assists (xA) have become essential metrics for evaluating how effectively a player creates and converts chances. For Liverpool FC supporters, these numbers offer a clearer picture of why a midfielder’s influence might not show up on the scoresheet, or why a forward’s form is better than the headlines suggest. This guide walks you through the practical steps of using xG and xA to compare players, whether you’re assessing potential transfer targets or evaluating the current squad.

Why XG and XA Matter for Liverpool’s System

Liverpool’s tactical system under the current head coach relies on high pressing, quick transitions, and creating high-quality chances rather than simply taking many shots. A winger like Mohamed Salah might register fewer total attempts than a counterpart at another club, but his xG per shot is often higher because the chances come from central areas after quick combinations. Similarly, a full-back’s xA can tell you whether overlapping runs are generating genuine danger or just recycling possession.

The core idea is simple: xG measures the quality of a shot based on location, angle, type of assist, and defensive pressure. A chance from six yards out has a higher xG than a speculative effort from 25 yards. xA measures the quality of the pass that leads to a shot, accounting for the same factors. Over a season, these metrics stabilise and become reliable indicators of a player’s underlying performance.

Step 1: Choose Your Comparison Context

Before diving into numbers, decide what you want to compare. Are you evaluating two Liverpool forwards competing for a starting spot? Comparing a Reds player to a potential transfer target? Or assessing how the squad measures up against a rival like Manchester City?

For example, if you’re looking at Liverpool’s attacking options, you might compare Diogo Jota and Darwin Núñez. Jota typically has a higher xG per 90 minutes because he operates closer to goal, while Núñez’s xG might be spread across a wider range of positions due to his drifting movement. Context matters: a player’s role within the system affects their expected numbers.

Step 2: Gather Reliable Data Sources

Use platforms like FBref, Understat, or Opta-powered sites that provide xG and xA data for multiple seasons. For Liverpool-focused analysis, you can cross-reference with our own breakdowns at The Anfield Perspective. Avoid single-match samples—look at rolling averages over 10-15 games or full-season totals.

When comparing players, ensure you’re using the same data provider because xG models vary slightly between companies. For instance, one model might rate a header from a corner at 0.08 xG, while another gives it 0.11. Consistency is key.

Step 3: Normalise for Playing Time

Raw totals can mislead. A player who has started every match will have higher xG and xA than a substitute who plays fewer minutes. Always use per-90-minute figures for fair comparisons. This is especially important for Liverpool’s squad, where rotation is common due to the demands of the Premier League and Champions League.

Here’s a simplified example of how two Liverpool forwards might compare over a 20-game period:

Forward A has higher raw numbers, but per 90 minutes, both players are performing similarly. The difference in goals might be due to finishing variance or chance quality.

Step 4: Compare XG to Actual Goals (and XA to Assists)

The gap between xG and actual goals reveals finishing efficiency. A player who scores significantly more than their xG is either a elite finisher or benefiting from a hot streak. For Liverpool, a player like Mohamed Salah has consistently outperformed his xG over multiple seasons, suggesting genuine finishing skill rather than luck.

Conversely, a player underperforming their xG might be in a slump or taking low-quality chances. Darwin Núñez’s early season at Anfield saw him underperform his xG, but the volume of chances suggested the goals would come. Over a full season, regression to the mean is common, but some players do maintain consistent over- or underperformance.

Step 5: Factor in Shot Location and Assist Type

Not all xG is created equal. A player who takes many shots from outside the box will have a lower xG per shot than a poacher who only shoots from inside the six-yard box. When comparing, look at the breakdown: how many shots come from central areas? How many from set pieces?

For assists, xA can be inflated by a player who takes many corners or free kicks. Liverpool’s set-piece taker might have a higher xA than a midfielder who creates chances from open play, even if the open-play chances are more dangerous. Adjust for context by separating set-piece and open-play xA.

Step 6: Use XG and XA in Transfer Analysis

When evaluating a potential signing, xG and xA help you see beyond highlight reels. A winger with high xA but low actual assists might be creating chances that teammates are failing to convert—a problem that could be solved by Liverpool’s clinical finishers. A striker with a high xG per 90 but a low shot volume might struggle to adapt to a system that requires constant movement.

For example, if you’re comparing a target from a weaker league, normalise for league difficulty. A player with 0.50 xG per 90 in the Bundesliga might see that drop to 0.35 in the Premier League due to stronger defenders and goalkeepers.

Step 7: Combine with Other Metrics

XG and xA are powerful, but they don’t tell the whole story. Pair them with:

• Passing accuracy and progression (see our guide on passing accuracy progression)
• Possession-adjusted stats to account for team dominance (explained in possession-adjusted stats)
• Player ratings from FBref and WhoScored for a holistic view (player ratings)

A midfielder might have low xA but high progressive passes, indicating they create space for others. A defender might have low xG but high aerial duel win rate, which matters for set-piece defending.

Step 8: Watch the Games to Validate

Numbers never replace the eye test. Use xG and xA to identify patterns, then watch match footage to understand why a player’s numbers look the way they do. Is the winger making runs that aren’t being found? Is the striker dropping deep and leaving the box empty? The metrics guide your attention, but the video confirms the story.

For Liverpool fans, this means watching how the tactical system generates chances. A full-back’s high xA might come from cut-backs after overlapping runs, while a midfielder’s low xG might reflect a deeper role in build-up play.

Common Pitfalls to Avoid

• Overinterpreting small samples: 3-5 games of xG data is noise, not signal. Wait for at least 10 games.
• Ignoring team context: A player on a dominant team will have inflated numbers due to more possession and chances.
• Comparing across different data providers: Stick to one source for a given comparison.
• Forgetting about penalties: Penalties have a high xG (around 0.76) and can skew a player’s numbers. Separate penalty and open-play xG for fair comparisons.
• Confusing xG with finishing ability: A player’s xG per shot is a measure of chance quality, not finishing skill. The gap between xG and goals measures finishing.

Putting It All Together

Imagine you’re comparing two potential Liverpool transfer targets: a winger from the Bundesliga and one from La Liga. Both have similar xG per 90 (0.35) and xA per 90 (0.28). But the Bundesliga player takes more shots from outside the box (lower xG per shot), while the La Liga player gets into better positions but has a lower shot volume. The Bundesliga player also plays for a team that dominates possession, inflating their numbers.

Using possession-adjusted stats (see our guide), you can normalise for team dominance. The La Liga player’s numbers might hold up better under Liverpool’s system, while the Bundesliga player might regress. This doesn’t make one definitively better, but it gives you a framework for deeper scouting.

Summary

XG and xA analysis is a tool, not a verdict. For Liverpool fans, it offers a way to cut through noise and evaluate players on the quality of chances they create and convert. Start with per-90 figures, adjust for context, combine with other metrics, and always validate with video. Over time, you’ll develop an intuition for which numbers matter and which are mirages.

For further reading, explore our breakdown of expected goals xG explained and scouting metrics xG per 90 to deepen your understanding of the numbers behind the game at Anfield.