Player Comparison Using xG and xA Metrics

So you’ve been scrolling through transfer rumours, watching highlight reels, and trying to figure out whether that young forward from abroad is actually worth the hype—or if Liverpool should just stick with what they’ve got. It’s a question that haunts every fan during the summer window: how do you compare players who’ve never shared a pitch in the same league, let alone the same system? The answer, more often than not, starts with two little acronyms that have taken over modern scouting: xG and xA.

Expected Goals (xG) and Expected Assists (xA) aren’t perfect, but they’re the closest thing we’ve got to a level playing field when comparing attackers from different competitions, different tactical setups, or even different eras of the Premier League. For a club like Liverpool, where the transfer committee is famously data-driven, these metrics have become part of the everyday language at Anfield. Whether you’re looking at a potential signing or evaluating whether the current first-team squad needs an upgrade, xG and xA give you a starting point that raw goals and assists just can’t match.

Let’s break down how these numbers work, what they mean for player comparison, and how you can use them to cut through the noise in the transfer market.

What Exactly Are xG and xA?

Before we dive into comparisons, let’s make sure we’re all on the same page. Expected Goals (xG) measures the quality of a shot based on a variety of factors—distance to goal, angle, type of assist, whether it’s a header or a foot, and even the pressure from defenders. A shot from six yards out with the goalkeeper out of position might carry an xG of 0.80, meaning that in similar situations, you’d expect it to go in about 80% of the time. A speculative effort from 30 yards might be 0.02.

Expected Assists (xA) does the same thing, but for the pass before the shot. It measures the likelihood that a given pass will become an assist, based on the location and type of delivery. A cutback from the byline to a player in the centre of the box carries a higher xA than a hopeful cross from deep.

The beauty of these numbers is that they strip away luck. A striker who’s scoring way more than his xG might be on a hot streak that won’t last. A winger whose xA is high but actual assists are low might be playing in a system where finishers are underperforming. That’s gold for a club like Liverpool, where the manager’s tactical system demands specific profiles in the final third.

Why Raw Goals and Assists Can Mislead You

Let’s be honest—most of us grew up judging players by their goal tally. It’s simple, it’s satisfying, and it works for pub arguments. But when you’re trying to decide whether a player from Ligue 1 can replicate his form in the Premier League, raw numbers can be a trap.

Consider two wingers: one plays for a dominant team that creates 20 chances a game, the other plays for a mid-table side that scraps for every opportunity. The first winger might have 15 goals and 10 assists, while the second has 8 and 5. On the surface, the first looks clearly better. But if you dig into the xG and xA, you might find that the second player is outperforming his xG by a massive margin while the first is actually underperforming his. That tells you the second player is creating and finishing chances that his teammates aren’t, while the first is a product of the system.

For Liverpool, this distinction matters more than ever. The tactical system under the current boss relies on wide players who can both create and finish at a high level. A player who looks good on paper but is actually being carried by a dominant team might struggle when asked to do the work in transition. On the flip side, a player whose xG and xA are elite despite playing for a weaker side might be a hidden gem.

How to Compare Players Using xG and xA

When you’re comparing two potential targets—or comparing a target to an existing Liverpool player—there’s a framework that works better than just glancing at totals. Here’s what I look at:

Per 90 minutes. This is the most important adjustment. A player who starts every game and plays 90 minutes will naturally have higher totals than a substitute or someone who’s been injured. Normalising to per-90 gives you a fair comparison of output when they’re actually on the pitch.

Non-penalty xG. Penalties inflate xG and goals, and not every player takes them. If you’re comparing a penalty taker to someone who isn’t, you need to strip those out. A player with high non-penalty xG is creating and finishing chances in open play, which is more transferable to a new system.

Shot volume vs. shot quality. Some players take a lot of low-quality shots, which pads their xG without actually being dangerous. Others take fewer but higher-quality chances. Liverpool’s system tends to favour the latter—players who get into dangerous positions rather than just letting fly from distance.

xA vs. actual assists. A player whose xA is consistently higher than his assists is creating chances that his teammates aren’t finishing. That’s a good thing—it means he’s doing his job. The finishing might improve with better teammates at Anfield.

A Practical Comparison: Attacking Midfielders

Let’s put this into practice with a hypothetical but realistic scenario. Liverpool are looking at two attacking midfielders who could slot into the number 8 or number 10 role. One is a current Premier League player, the other is from abroad. Both have similar raw goal and assist numbers over the last season.

On the surface, they look similar. But there’s a subtle difference: Player B has a slightly higher xA and more shot-creating actions per 90, which suggests he’s more involved in building attacks. Player A has a slightly bigger gap between his goals and his non-penalty xG, meaning he might have benefited from finishing variance or penalties. If you’re Liverpool, you might lean towards Player B because his numbers suggest a more consistent creative output that could translate to a new league.

The Risk of Over-Reliance on Metrics

Here’s where I have to pump the brakes a little. xG and xA are powerful, but they’re not the whole story. They don’t measure a player’s work rate off the ball, their pressing intensity, their ability to hold up play under pressure, or their decision-making in transition. A player with elite xG numbers might be a liability in Liverpool’s high-press system. A player with modest xA might be a brilliant dribbler who creates space for others even if the final pass doesn’t come.

The data is a tool, not a verdict. Liverpool’s recruitment team uses these metrics as a filter, not a final decision. They’ll still watch hours of footage, talk to agents, and assess character. The numbers tell you who to look at; the scouting tells you whether they fit.

How Liverpool Can Use These Metrics in the Transfer Window

The transfer market is a game of information asymmetry. Clubs with better data have an edge. Liverpool have been ahead of the curve for years, and xG/xA analysis is a big part of that. Here’s how they might apply it in the upcoming window:

• Identifying undervalued targets. A player whose xG and xA are strong but whose raw numbers are lower because of poor finishing around him might be available at a discount. Liverpool can buy low and hope the numbers catch up at Anfield.
• Avoiding overpriced streaks. A player who scored 20 goals but had an xG of 12 is likely due for regression. Paying top dollar for that player is a risk Liverpool usually avoids.
• Comparing across leagues. The data helps adjust for league quality. A player with high xG in a weaker league might still be a good prospect, but the gap in competition needs to be factored in. Liverpool’s analysts have models that attempt to do this.
• Evaluating contract extensions. When deciding whether to extend a current player, xG and xA trends can show whether a decline is real or just bad luck. For more on that, check out our piece on contract extension analysis.

The Bottom Line: Metrics Are a Starting Point, Not a Conclusion

If you’re a fan scrolling through transfer rumours this summer, xG and xA are your best friends. They give you a way to compare players that goes beyond the highlight reel and the hype. But they’re not a magic bullet. The best scouting departments use them as a filter, not a verdict.

For Liverpool, the challenge is always the same: finding players who can thrive in a specific tactical system while also representing value in a market that’s increasingly inflated. The numbers help narrow the field, but the final call still comes down to the manager, the coaching staff, and the human element that no metric can capture.

So next time you see a rumour linking Liverpool to a player from abroad, pull up their xG and xA per 90. Compare them to the current squad. Ask yourself whether the numbers suggest a genuine upgrade or just a different name. It won’t tell you everything, but it’ll tell you more than the raw goals column ever will.

For a deeper dive into how market values play into these comparisons, have a look at our guide to market value comparison in the Premier League. And if you want to understand the bigger picture of how Liverpool’s transfer strategy works, our transfer analytics hub has you covered.