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What Is xP in Football: Expected Points Explained

05 Feb, 2026

It's common in modern football for analysts and coaches to look far beyond the headline figures and statistics when assessing their team's level of performance.

Despite pushback from some quarters of the football community, there's a growing understanding that looking at more than just final scores and total goals tallies can give us different insights about how a team is operating and how performances can be enhanced.

A number of terms and phrases have been popularised as stats gurus push for a more holistic approach to understanding game outcomes.

One of these is 'Expected Points', typically known simply as xP. xP has a close relationship with xG, but while the latter term has become widely used within the world of football, many people are still unaware of exactly what xP means.

That's why this article will focus on clearing up any confusion and explaining why football professionals are embracing the value of Expected Points. We'll work through xP's meaning in football, before detailing exactly how it's calculated.

What Is xP in Football?

Football is a results business. Ultimately, players, coaches, and teams as a whole are judged on whether or not they can win matches and climb league tables. But sometimes, the final score of a match doesn't paint the full picture.

Expected points is a metric designed to reflect the broader performances of a team, going beyond just the number of goals scored or conceded in a game.

Typically abbreviated to xP, this metric measures how many points a team should have earned based on the chances they've created and given away to the opposition during a match.

In recent years, this fairly new metric has become one of several key performances indicators (KPI) used by coaches, analysts and data experts to judge how a team is performing.

But the development of xP data usually relies heavily on Expected Goals (xG) data; so, before we move on, it's worth spending some time explaining what this metric means in more detail.

What Is Expected Goals?

Expected Goals (xG) is a football metric designed to measure the probability of a given shot ending in a goal. Put simply, this metric uses huge banks of historical data to rate how good a goalscoring opportunity is.

xG takes into account a variety of different factors including angle to goal, distance from goal, the positioning of attackers and defenders, the way the ball has connected with the offensive player, and the nature of the assist or prior action that led to the attacker gaining possession.

All of these elements combine to create a goalscoring opportunity, and xG is supposed to rate how dangerous that opportunity is.

In modern professional football, every shot taken on goal is given an xG score between 0 and 1; a chance with an xG rating of 0.1 has a very low probability of being scored, while a shot with an xG rating of 0.9 has an extremely high chance of ending up in the back of the net.

Having access to these ratings help us gain a greater understanding of whether a player is a good or bad finisher, whether a team is creating enough threatening opportunities, and whether a side's goal tally accurately reflects how well they've been performing.

xG doesn't always show the full picture; for example, elite-level strikers might consistently outperform their xG for several seasons, but that doesn't necessarily mean they've been lucky or are going to slow down any time soon, it just means their finishing ability is excellent.

In a broad sense, though, Expected Goals helps us better understand the attacking output and chance creation of a given team.

How Is xP Calculated?

These days, every shot taken in a top-level match receives an xG rating between 0 and 1. While there are different ways of calculating xP, for most xP models, using this xG data is absolutely crucial.

Expected Points is typically calculated by thousands of match simulations being run through computers, based on the xG of chances created and chances given away to the opposition in a game.

Each simulation uses this xG data to decide whether a chance should be scored, and consequently, a bank of likely results is created: win, loss, or draw.

There's a specific calculation that most models use to calculate Expected Points: xP = 3 x chance of win + 1 x chance of draw.

This might sound confusing, but it's essentially applying the standard points-scoring system that exists in football (3 points for a win, 1 point for a draw, 0 points for a loss) to a new metric.

Basically, if 50% of match simulations result in a win for a team, 30% result in a draw, and 20% result in a loss, they have a 50% chance of winning, a 30% chance of drawing and a 20% chance of losing.

xP converts those fractions into decimals for the 'chance of win' and 'chance of draw' section of the formula.

For example: if a team has a 50% chance of winning and a 30% chance of drawing, the calculation would be: 3 x 0.5 + 1 x 0.3 = 1.8. The Expected Points (xP) for that particular game would be 1.8.

According to Sportmonks, "If a team takes several shots during the same move, we don't count them all as separate chances. Instead, the model works out the combined chance of scoring in that possession.

For example, if there are 3 shots with xG scores of 0.2, 0.4, and 0.3, the chance of no goal is: (1-0.2) x (1-0.4) x (1-0.3). Then we subtract that from 1 to find the chance of a goal."

The Benefits of Expected Points

xP is designed to reflect whether a team is getting the results they deserve. While xG calculates the quality of chances and offers insight into whether forward players are being clinical enough, xG focuses on whether a team deserves to win a game overall, bringing in both chances created and chances conceded.

This can give us insights into whether a team is underperforming, and therefore whether their results (and subsequently league position) can be expected to drop off at some point.

By the same token, a higher points tally than Expected Points could just show that a team is being super efficient with the chances they are creating (reflecting strong coaching instructions).

During the 2024/25 season, Nottingham Forest picked up 65 points, which surpassed their xP by a substantial 15 points. Depending on how you look at it, this is either high efficiency or a touch of luck.

Meanwhile, Bournemouth amassed a total xP which exceeded their actual points by 9, showing us that the Cherries created a high number of dangerous chances but ultimately failed to convert many of these chances into goals.

Recruitment teams can draw conclusions from this data; for example, Bournemouth might find that they need a particular type of forward who can help the team be more clinical from these chances, while Forest staff might be keen to try and generate chances with higher percentage xG in order to give their strikers the best possible chance of consistently scoring goals.

Scouts and recruiters use xP to chart individual player performance, identifying areas they could improve on and working out how valuable certain people's contributions are.

Analysis of xP and xG can also lead coaches to tweak their tactical methods, or double down on certain principles if the data suggests that an uptick in results could be around the corner.

Predicting future results is a key reason for the rise of xP; in a world where marginal gains are everything, analysts always want to get an indication of what might be in store in the future.

One other key use of xP is in the world of sports betting; bettors try to gain advantages by analysing individual match data and identifying which teams are performing better or worse than recent results suggest.

This means their odds might be favourable and therefore they're more worth betting on in the future as results may start to greater reflect performances.

The Weaknesses of Expected Points

Most xP models are based on Expected Goals, so they're entirely dependent on whether that particular xG model is accurate. The longer the time period analysed, and the larger the data bank created, the more accurate the xG and xP figures will be.

There are also several things that this metric doesn't take into account. For example, xP doesn't tend to measure how a team keeps possession or how they set up out-of-possession.

These numbers can't capture tactical shifts during the game, and can sometimes serve to oversimplify performances and overall matches.

In a dynamic, fast-paced, and ever-shifting sport, where moments of chaos are frequent, a metric like xP doesn't always tell us exactly what's taken place on the football field.

Factors like game-changing injuries, bookings and red cards, and bust-ups between opponents aren't factored in, and tactical aspects of the sport like pressing — where coaches can make significant decisions on whether to counter-press high up the pitch, sit in a low block, or enact something inbetween — can fly under the radar.

Therefore, it's important to think of metrics like xG as a potentially useful way of gaining additional insight into performances, but not a foolproof descriptor for what takes place on a football pitch.

We should take a holistic approach to modern metrics, using them in tandem with the more traditional eye test to come up with conclusions about performance.

Want to find out more about how Expected Goals is changing football? Check out our in-depth guide to how xG works.

Author: Fred Garratt-Stanley

Lead Content Writer

Fred Garratt-Stanley is an experienced football writer and journalist, specialising in industry insights, tactical analysis, and the culture of the game. He has contributed to publications such as NME, GQ, The Quietus, and Resident Advisor. As Lead Content Writer at Jobs In Football, he focuses on providing reliable, research-driven articles to help people navigate careers in the football industry.