How to Read Liverpool’s xG and Advanced Metrics Like a Scout

You are watching a Liverpool match, and Mohamed Salah cuts inside from the right, shoots, and the ball curls just wide of the far post. The commentator says, “He should have scored there,” but the expected goals (xG) model might tell a different story. Understanding advanced metrics is no longer optional for a modern fan—it is the difference between saying “we were unlucky” and knowing “we created high-quality chances but lacked finishing precision.” This checklist will teach you how to interpret xG, xA, progressive passes, and other data points to evaluate Liverpool’s performances under Arne Slot without falling into the trap of overconfidence or false narratives.

Step 1: Understand the Core Metric—xG (Expected Goals)

xG measures the quality of a shot based on factors like distance to goal, angle, body part used, and whether it came from a cross or a through ball. A shot from six yards out with the goalkeeper off their line might have an xG of 0.75, while a 30-yard volley might have 0.02. The sum of all shots’ xG for a team approximates how many goals they “should” have scored.

For Liverpool, xG is particularly useful because Arne Slot’s system generates high-quality chances in central areas—cutbacks from the byline, through balls to runners, and first-time finishes from the edge of the box. If Liverpool’s xG is 2.5 but they score only one goal, the issue is finishing, not chance creation. Conversely, if they score three from an xG of 0.8, they were clinical but unsustainable.

Practical tip: Compare Liverpool’s xG per match with their actual goals over a five-game rolling average. A persistent gap of more than 0.5 goals per game indicates either a finishing slump or a goalkeeper in exceptional form.

Step 2: Move to xA (Expected Assists) to Evaluate Creativity

Expected assists (xA) measure the quality of a pass that leads to a shot. A cross that finds a teammate unmarked at the penalty spot might have an xA of 0.4, while a sideways pass that leads to a speculative long shot might have 0.05. For Liverpool, Trent Alexander-Arnold’s diagonal switches and Mohamed Salah’s cutbacks are high-xA actions.

Use xA to separate passers from creators. A midfielder with high pass completion but low xA is likely recycling possession—valuable for control but not for breaking down a low block. For Slot’s system, full-backs and wingers are expected to contribute to chance creation, and their xA per 90 minutes can be compared to squad averages over time to assess effectiveness.

Table: Illustrative xA Ranges for Liverpool’s Creative Players (Based on Typical Premier League Ranges)

Data is illustrative and based on typical Premier League ranges, not actual 2024/25 Liverpool season data.

Step 3: Incorporate Progressive Passes and Carries

Progressive passes are passes that move the ball at least 10 yards forward or into the penalty area. Progressive carries are dribbles that move the ball significantly toward the opponent’s goal. These metrics reveal how Liverpool build through the thirds.

Under Slot, Liverpool’s center-backs—Virgil van Dijk and Ibrahima Konaté—are expected to play progressive passes into midfield, bypassing the first line of pressure. If Van Dijk’s progressive pass count drops noticeably per 90, it may indicate the opposition is pressing him effectively or he is being forced to play safe sideways passes.

Checklist for evaluating build-up play:

• Center-backs maintain a solid rate of progressive passes per 90, relative to team and league averages.
• Midfielders contribute a healthy number of progressive carries per 90.
• Full-backs have a high number of touches in the final third.

Step 4: Use Non-Penalty xG (npxG) to Remove Distortions

Penalties have an xG of approximately 0.76, meaning they inflate a player’s or team’s total xG significantly. For accurate analysis of open-play performance, always use non-penalty xG (npxG). If Liverpool’s npxG is 1.8 but their total xG is 2.6, they relied on a penalty—useful for the result but not reflective of open-play dominance.

This is especially relevant for evaluating strikers. If a forward’s goal tally includes multiple penalties, their npxG per 90 gives a truer picture of their finishing ability. For Liverpool, if a new signing like Alexander Isak (hypothetical) scores 15 goals but 5 are penalties, his npxG per 90 might be lower than a teammate who scores 10 from open play.

Step 5: Compare xG Against and Post-Shot xG (PSxG) for Defensive Assessment xG Against measures the quality of chances conceded. Liverpool’s defensive solidity under Slot can be evaluated by tracking xG Against per match. A value below 1.0 is excellent; above 1.5 indicates vulnerability.

Post-Shot xG (PSxG) measures the probability of a shot being saved based on its placement after the shot is taken. Alisson Becker’s PSxG minus Goals Conceded (PSxG-GA) is a key metric: if PSxG-GA is positive, he is saving shots that the model expected to go in. For Liverpool, a consistently positive PSxG-GA over a season indicates world-class goalkeeping.

Table: Defensive Metrics to Track Over a Five-Match Window

Note: Targets are illustrative and based on typical Premier League benchmarks; actual performance should be evaluated in context.

Step 6: Contextualize with Shot Maps and Heat Maps

Raw numbers like xG total lose nuance without spatial context. Shot maps show where Liverpool take their shots—central and close to goal is optimal, while shots from wide angles or long range indicate a lack of penetration. Heat maps for players like Luis Díaz or Cody Gakpo reveal whether they are receiving the ball in dangerous areas or being forced wide.

For tactical analysis, compare Liverpool’s shot map in a win versus a draw. If the shot locations are similar but conversion differs, variance explains the result. If shot locations shift to lower-xG areas, the opposition’s defensive structure is working.

How to read a shot map:

• Red markers = goals; blue = saved; white = missed.
• Clusters in the six-yard box = high-quality chances.
• Clusters outside the box = low-percentage attempts.

Step 7: Apply Metrics to Transfer Evaluation

When evaluating a potential signing—say, Jeremie Frimpong as a right-back—use advanced metrics to compare him to Liverpool’s system needs. Frimpong’s progressive carries per 90 and xA from wide areas should be compared to Trent Alexander-Arnold’s current output. If Frimpong’s xA per 90 is relatively low and his defensive duels won percentage is below average, he may not fit Slot’s requirement for full-backs to both create and defend.

Checklist for transfer metric analysis:

• Compare npxG per 90 to current Liverpool players in the same position.
• Evaluate progressive passes per 90 against Slot’s system average.
• Check defensive metrics: tackles won, interceptions, and aerial duels.
• Review xG Against per 90 when the player is on the pitch (team defensive impact).

Advanced metrics like xG, xA, and progressive passes are tools, not verdicts. They tell you what happened, not why it happened, and they cannot account for the emotional weight of a last-minute winner at Anfield. Use this checklist to separate noise from signal, but always pair data with your own eyes. The next time you see a stat about Liverpool’s xG, ask: Is this about chance creation, finishing, or a penalty? That question alone will make you a more informed fan.

For deeper dives, explore our season statistics page for rolling data, player profiles for individual metrics, and tactical analysis for game-specific breakdowns.