Expecting Goals Premier League Team Ratings

A new method for evaluating soccer teams in the major European Leagues, and an opportunity to discover whether I am biased against your favorite team or not.

The bread and butter of sports analytics are ratings and projections. Anyone seriously engaged in sports analytics should have a few insights into which teams are truly the best, and which teams have been playing in ways that suggest their current position in the table or the standings misrepresents their real level.

On one level, this is a soccer analytics newsletter and obviously there should be Expecting Goals Team Ratings during the season. At the same time, team rating systems are one of the few areas of soccer analytics where people already have a wealth of options to choose from. Even after the tragic and still-mourned death of the FiveThirtyEight soccer model, the Elo-based Opta power rankings and the gambling-odds-based Pitchrank power rankings continue to provide insights into the competitive balance of global soccer. And at the league level, there are excellent models for the English Premier League at Analytic.football, Elevenify and the Cannon Stats newsletter.¹

If there were no Expecting Goals team ratings model, people could still get insights from soccer analytics about their favorite teams. And while I have worked hard to optimize this model and find new ways to use the statistical record to evaluate clubs, these are surely marginal gains.

I decided to build this ratings system for two reasons. The first is because it is fun. And the second is that building a ratings system opens up a variety of possible new studies and new ways of approaching studies.

Here’s how this system came to be. I was working on a study on the effects of red cards and teams playing with a man advantage, and as a first step I aggregated statistics in games with red cards to find how many goals teams scored before and after the red cards. Teams playing up a man, specifically 11 against 10, typically score and concede at rates that equate over a full match to about 2.0 non-penalty goals scored and 0.7 non-penalty goals conceded. This suggests the value of a red card is worth about 1.3 non-penalty goals. However, teams that get red carded have typically been playing worse in the match up to that point as well. The average non-penalty scoreline before a sending-off , scaled to a full match, favors the team that would later get a man advantage by about 1.3 to 1.0.² So perhaps the value of the red card is less than it seemed.

The question this raises is, why do we find that teams which eventually get a red card are already being outplayed before the red card? Is it because they are, on average, worse teams? Is it because teams that are losing tend to take risks that can lead to a sending-off? Or might the sample of games with red cards select teams that simply weren’t playing well that day, including but not limited to committing a red card offense? This question cannot be answered simply on the basis of what happened in those matches. It requires an objective measure of team quality that can be used to project game outcomes before the fact. It requires team ratings.

As I worked on this new model, I found that a ratings system opens up many more avenues for possible study. Some of them were pursued in building the current system, and others will become later newsletters and then additions and revisions to this model.

So, at least before these later revisions, what does the current team ratings model say and how does it work?

Premier League Team Ratings

Glossary

Team Performance: This is a measure of how well the team has played in its matches this season, based on statistics that best project future quality, adjusted for red cards and opposition quality. It is expressed in goal difference per match. You can think of this as, how many goals better or worse than the average team has this club played over the season?

Attack Performance: This is the attacking component of Team Performance. How many goals better has this club’s attack been, compared to the average team, over the season so far?

Defense Performance: This is the defensive component of Team Performance. How many fewer goals and scoring chances has this club’s defense conceded, compared to the average team, over the season so far? Note that better defensive performances here are negative, in the sense that the team has conceded fewer chances and goals than average.

Schedule Difficulty: This is the opposition quality adjustment, also expressed in goal difference per match. A team with “+0.1” schedule difficulty has played a schedule which is harder than average by a margin of 0.1 goals per match. This measurement takes into account home field advantage and the team ratings estimated quality of opposition. A positive Schedule Difficulty reflects a harder schedule, in the sense that the team’s typical opponent is better than average.

Team Rating: This is the current overall rating for this club, representing how my system will project their future performance. It is based on a weighted average of team performance combining goals and xG, regressed to estimated team value, and for promoted teams past performance is adjusted for league difficulty. Team Rating is scaled to 1.0, with 2.0 being a team that is roughly twice as good as the average team and 0.5 being a team that is roughly half as good as the average team.

Attack Rating: In the model itself, it is Attack and Defense Rating which are used to project matches. In the newsletter below I will get into the guts of what makes these up, but the basic components are what I listed under Team Rating. Like Team Rating, Attack Rating is scaled to 1.0, with 2.0 being an attack roughly twice as good as league average, and 0.5 half as good as league average.

Defense Rating: This is the defensive component of team rating. Note that while it is scaled to 1.0, now lower numbers are better. An 0.5 Defense Rating reflects a defense roughly twice as good as league average (that is, conceding half as many goals), and a 2.0 Defense Rating reflects a defense roughly half as good as league average.

Commentary

At this point in the Premier League season, the actual table bears shockingly little resemblance to the Expecting Goals Ratings table. While Arsenal have been as dominant statistically as their points total suggests, the second-place team in these ratings stands in fifth place, and the next two in twelfth and ninth place respectively.

Some of these divergences can be explained simply by going to FBRef and sorting by expected goals. Bournemouth, Tottenham and Sunderland all have negative expected goals difference despite their table position, and any model which is heavily based on xG will downrate them just as this one has.

But there are several other notable results here which are peculiar to this ratings system and may help explain exactly how it works before diving into the full methodology.

• Chelsea are not only ninth in the table but also tenth in expected goals difference. Their high rating here is a function of red card adjustment. Chelsea’s goal difference at even strength is plus-10 and their expected goals difference about plus-3. The Blues have struggled badly playing with 10 men for extended periods against Manchester United and Brighton, and this system accounts for that by scaling 10v11 and 11v10 performance by a factor of 1.4. That is, teams which are down a man are “credited” with about 40 percent better attacking and defensive performance than they actually managed in those uneven periods. With this adjustment, Chelsea move up the table.

• Fulham sit in 17th place in the table with the 16th-best xGD. However, my ratings estimate that Fulham have played the toughest schedule in the Premier League, rated at about 0.18 goals per match more difficult than average. While Fulham have not faced every one of the best teams in the league, they have yet to face any of their direct competitors at the bottom of the table. With matches against Wolves, Everton, Burnley, Forest and West Ham to come before year-end, Fulham will have every chance to move up the table and out of the relegation fight.

• There is not much in Aston Villa’s numbers here that would be surprising if you look at the xG table, but nonetheless it is worth emphasizing how strange their season has been. Aston Villa finished sixth in the PL in 2024–25, qualifying for the Europa League, on the back of a hot stretch run that also helped them establish one of the top six team ratings in my system at the beginning of this season. Since then, Villa’s attack has utterly disappeared. This season they are 19th in xG, 16th in shots, 19th in xG per shot, and tied for 16th in penalty area touches. While two recent victories on the back of three converted shots from range have improved their table position, Villa will need to start creating scoring chances at rates more similar to last season if they hope to compete for anything more than survival.

• The relegation chase. Sunderland have obviously shot to the top of the table, but by these ratings Leeds’ performances have been even better. Partly this is because Sunderland have played the easiest schedule in the league, rated at about 0.13 goals per match easier than average. But both teams are playing better than any promoted side in years. Leeds also have a roughly average team rating, in line with their Team Performance. This is because Leeds had dominant underlying statistics in the Championship, as well as a number of Premier League quality players, and so already coming into the season they were projected as a roughly average team. Sunderland, who were fortunate to be promoted, had an extremely poor Team Rating to start the season. Even nine matches this impressive have only been enough to pull their rating up to the level of some of the worst teams in the league.

• The Liverpool Team Rating. It should not be too surprising that Liverpool’s Team Performance has them in sixth place after a four-match losing streak in the league. But how has the Team Rating of one of the projected title favorites already fallen below Newcastle’s?