• Projections and xPts for Groups A–F

• Main World Cup Projections Page

• World Cup Fixtures and Results (including Projections and xG)

• Even More Projections (Path Difficulty and Knockout Thresholds)

• PADDLIN’ Team Ratings

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Group G

Group H

Group I

Group J

Group K

Group L

Navigation

• Projections and xPts for Groups A–F

• Main World Cup Projections Page

• World Cup Fixtures and Results (including Projections and xG)

• Even More Projections (Path Difficulty and Knockout Thresholds)

• PADDLIN’ Team Ratings

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Data from Opta and Transfermarkt.

• Projections and xPts for Groups G–L

• Main World Cup Projections Page

• World Cup Fixtures and Results (including Projections and xG)

• Even More Projections (Path Difficulty and Knockout Thresholds)

• PADDLIN’ Team Ratings

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Group A

Group B

Group C

Group D

Group E

Group F

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Navigation

• Projections for Groups G–L

• Main World Cup Projections Page

• World Cup Fixtures and Results (including Projections and xG)

• Even More Projections (Path Difficulty and Knockout Thresholds)

• PADDLIN’ Team Ratings

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Data from Opta and Transfermarkt.

• Main World Cup Projections Page

• Projections and xPts for Groups A–F

• Projections and xPts for Groups G–L

• Even More Projections (Path Difficulty and Knockout Thresholds)

• PADDLIN’ Team Ratings

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Fixtures

Results

One note on “Adjusted xG”: This is not an xG that is attempting to measure only the likelihood of scoring every individual chance, and it is likely not to align precisely with published xG. This is an xG designed instead for predicting future performance. In particular the adjustments to Adj. xG regress the value of big chances and take into account favorite/underdog status and game state. For more on Adjusted xG, see:

• Explaining PADDLIN’: Expecting Goals

• How to Build a World Cup Model: Double Pivot on YouTube, Part II

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Navigation

• Main World Cup Projections Page

• Projections and xPts for Groups A–F

• Projections and xPts for Groups G–L

• Even More Projections (Path Difficulty and Knockout Thresholds)

• PADDLIN’ Team Ratings

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Data from Opta and Transfermarkt.

• Main World Cup Projections Page

• World Cup Fixtures and Results (with Projections and xG)

• Projections and xPts for Groups A–F

• Projections and xPts for Groups G–L

• Even More Projections (Path Difficulty and Knockout Thresholds)

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

PADDLIN’ Team Ratings

The neutral-pitch projected quality of all 48 World Cup teams based on the three different team quality measures underlying PADDLIN’. Ratings shown as the average points per match the team would be projected to win across a full round robin tournament in which all 48 teams played each other once.

• “Results” refers to a model based only on team results (win/draw/loss), goal difference and opposition quality.

• “xG” refers to a model that incorporates adjusted expected goals into the “Results” model for matches where xG is available.

• “+ Squad Value” refers to a model that incorporates estimates of squad value from Transfermarkt.com into the “xG” model.

• For more on how these ratings were constructed, read the Expecting Goals newsletter on PADDLIN’.

Team Attack and Defense Ratings

The team ratings split into attack and defense scores.

• “Att” shows how many goals the team would be projected to score against the average World Cup opponent on a neutral field.

• “Def” likewise shows how many goals the team would be projected to concede against average World Cup opposition.

• “Tilt” represents the team’s tendency to play a more attacking or defensive style, relative to the rest of the World Cup field. Positive means more attacking, negative means more defensive. (I ripped this idea off wholesale from Nate Silver’s PELE model.)

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Team Ratings Risers and Fallers

The teams whose underlying xElo Rating has increased or decreased the most based on their results and performances in this tournament.

These Risers and Fallers are different from those on the main page because this is focused only on changes in Team Rating not on their projections.

Note that xElo does not take into account squad value. It is the basis of the “xG” column in the first Ratings table above. So the rankings here do not take into account the squad value aspect of the model, which plays a significant role in match projections.

Navigation

• Main World Cup Projections Page

• World Cup Fixtures and Results (with Projections and xG)

• Projections and xPts for Groups A–F

• Projections and xPts for Groups G–L

• Even More Projections (Path Difficulty and Knockout Threshholds)

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Data from Opta and Transfermarkt.

Navigation

• Main World Cup Projections Page

• World Cup Fixtures and Results (including Projections and xG)

• Projections and xPts for Groups A–F

• Projections and xPts for Groups G–L

• PADDLIN’ Team Ratings

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Likely Thresholds for Knockouts Qualification

Based on the results of 100,000 simulations, the likelihood that a certain combination of points and goal difference will qualify a team for the World Cup knockout rounds.

For example, this shows that teams which end on 3 points with negative-2 goal difference reach the knockouts 64 percent of the time, but there is a 15 percent chance that their qualification is determined by a tie-breaker like goals scored or even fair play.

Path Difficulty

Every team’s most likely opponents in the first two knockout rounds, and an estimate of how difficult their path to the quarterfinals will be compared to an average team’s path with the same seed.

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Navigation

• Main World Cup Projections Page

• World Cup Fixtures and Results (including Projections and xG)

• Projections and xPts for Groups A–F

• Projections and xPts for Groups G–L

• PADDLIN’ Team Ratings

• Introducing PADDLIN’: Newsletter on Modeling and Methodology from Expecting Goals

• How to Build a World Cup Projection Model: Double Pivot Podcast (YouTube), Three-Part Series on PADDLIN’

Data from Opta and Transfermarkt.

The page will update after every match, and the tables have an “Updated at” time-stamp to confirm.

The PADDLIN’ system incorporates both results and statistical measures of underlying performance, including expected goals, for all international teams. It also uses estimates of player value from Transfermarkt to adjust these measures based on the projected quality of the team’s World Cup squad.

To read about these projections and methodology in more depth, check out the full pre-tournament newsletter:

• Introducing PADDLIN’: Expecting Goals World Cup Projections

You can also watch the three-part Double Pivot podcast YouTube series on how to build a World Cup statistical model.2

On the main page just below, you can find team-by-team odds of winning the tournament and reaching the various knockout rounds, recent match results and upcoming projections, and how the model’s projections have changed since the beginning of the tournament. And on all the sub-pages (linked below) there is much, much more.

Who’s Going to Win the World Cup (Probably)?

The latest updates to the full projection model, odds for all 48 teams of making the knockouts, winning it all, and everything in between.

For the specific odds for every group, and for the underlying Team Ratings these productions build from, see:

• Projections and xPts for Groups A–F

• Projections and xPts for Groups G–L

• PADDLIN’ Team Ratings

For even more projections, including what points totals and GD can secure qualification to the knockouts and which teams may draw the easiest and most difficult paths to the quarterfinals, see:

• Even More Projections (Path Difficulty and Knockouts Thresholds)

Recent and Upcoming Matches

Recently completed matches with actual and projected results, as well as PADDLIN’ adjusted xG, and upcoming matches with projected results and goals.

One note on “Adjusted xG”: This is not an xG that is attempting to measure only the likelihood of scoring every individual chance, and it is likely not to align precisely with published xG. This is an xG designed instead for predicting future performance. In particular the adjustments to Adj. xG regress the value of big chances and take into account favorite/underdog status and game state. For more on Adjusted xG, see:

• Explaining PADDLIN’: Expecting Goals

• How to Build a World Cup Model: Double Pivot on YouTube, Part II

For projections and adjusted xG for every match, see:

• World Cup Fixtures and Results (including Projections and xG)

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How the Projections Have Changed

A graph of World Cup winner over time, and tables showing the teams whose odds of making the Round of 16 have increased or decreased the most based on their results, the performances, and the way the draw stacks up.

This table shows the odds of winning the World Cup for the current favorites, with change over time for every match at the tournament.

This table shows the teams whose odds of winning the tournament have risen or fallen the most in the latest round of matches.

This table shows the teams whose odds of making the Round of 16 have changed the most from the start of the tournament.

Navigation

This is the base page for the PADDLIN’ World Cup projections, but there’s so much more! For even more in-depth tables and data check out:

• World Cup Fixtures and Results (including Projections and xG)

• Projections and xPts for Groups A–F

• Projections and xPts for Groups G–L

• Even More Projections (Path Difficulty and Knockouts Thresholds)

• PADDLIN’ Team Ratings

Data from Opta and Transfermarkt.

What I mean is: the World Cup is an analytics problem unlike any other.

What makes it special? Partly that everyone cares, and a model that gets it wrong gets to faceplant in front of all of them.1 But mostly the problem is technical. Take one of the biggest matches of the last World Cup

Morocco upset Portugal 1-0 and became the first African team to reach the semi-finals. In the (nearly) four years since, Morocco has played 53 matches, about 15 per year, and Portugal has played only 40. Of Portugal’s matches, 31 have been competitive fixtures, and every single one has been against a team in UEFA, the (mostly) European confederation. Of their seven friendlies, five were also against UEFA opposition and their two matches in March against the USA and Mexico were the first against non-UEFA competition. When Portugal kicked off a friendly against Nigeria on Wednesday, June 10, it was their first match against an African team since losing to Morocco.

Morocco’s record is slightly richer, but all the more telling for that. By playing in the Arab Cup in December 2025, Morocco did rack up five competitive matches against opponents from AFC, the (mostly) Asian confederation, but only two of those teams, Saudi Arabia and Jordan, will be at this World Cup. Other than a June friendly against Norway, the only matches Morocco has played against UEFA opponents since defeating Portugal were the ensuing World Cup semifinal and third-place match against France and Croatia, respectively.

If Morocco were to face Portugal at the World Cup again, what would you have to go on to predict that match? Both teams have played fewer matches than most club teams play in a single season but spread out over four years. They do not even have a single shared opponent since the World Cup, not even in a non-competitive fixture.

Even within the few truly competitive matches at major tournaments, problems arise. Portugal lost 2-0 in the Euros to Georgia, but that match took place after Portugal had clinched a spot in the knockouts with two straight victories, and manager Roberto Martínez chose to rest key starters because the result did not matter to the team’s advancement.

There are too few matches over too long a time to build a good system. Many of the matches are not competitive fixtures, and the remaining high-stakes matches are held nearly exclusively within fixed groups by confederation. And even some of the few matches left over happen under unusual competitive conditions.

Data coverage makes it all worse. Expected goals are published for every major league match in the world; for internationals the coverage is spotty, and outside Europe it is sparse.

All of that is what makes projecting the World Cup fun.

Introducing: Probabilistic Archetype, Dominance-Driven, Linking Interrelated National teams (PADDLIN’)

The apostrophe, of course, cuts off the final word “teams.”

This model seeks an answer to all the problems that bedevil any objective measurement of international team quality. It uses an Elo rating based on actual results and, where available, an xElo rating based on the projected result of adjusted expected goals, to get a first view of team quality that can be compared among teams around the world. The model does in fact increase the weights of matches in which a team put up a significant margin in goal difference or xG difference, actually incorporating the question of whether one team or another did suffer a paddlin’. It makes use of player values from Transfermarkt.com to adjust these Elo ratings, giving teams credit for having better players available beyond the effects they may have had on results in the past. While these crowd-sourced player ratings are far from definitive, work by Paul Johnson among others has demonstrated their utility for statstical projections. The model makes particular use of these values in situations where two teams do not have opponents in common, like Portugal and Morocco, and where teams have very few matches with expected goals coverage. The idea is, if we have a fair number of matches connecting two teams, and a reasonable amount of advanced stats coverage between them, the Transfermarkt values aren’t as important for distinguishing between the two teams. But if it is hard to say objectively what the relationship between two sides is based on their results and past opponents, the model leans more heavily on estimated player quality. In general, in matches between two teams in the same confederation, the model will lean more heavily on Elo, and in cross-confederation matches the model will lean more heavily on Transfermarkt.

There are a lot more pieces that go into this model, but let’s take a look at the output.

These are the projections for how the tournament will play out, which differ from the general team ratings in a couple important ways. These projections are based on a Monte Carlo simulation which runs the scheduled tournament over and over to find probabilities, so teams with more and less favorable draws will be pushed up or down. Further, this takes into account home field advantage. I will have more to say about international football HFA, the bane of my existence for the last several weeks, but in this model there are three home field effect. First is a general and substantial advantage for hosts, second is an adjustment based on the altitude the match is played at, and third is a smaller projected benefit for teams that traveled across fewer time zones to play at this World Cup. (Colombia is getting a little boost from that third factor, which is enough to slip them ahead of Germany.)

These are the underlying ratings which drive the model, before any specific tournament adjustments. Because the Transfermarkt adjustment is not applied equally to all matches, I do not have a final, single Elo rating for all teams. Instead, this is a “Round Robin Score” based on how many points every team would project to take in a 48-team neutral field round robin tournament between all the World Cup teams.2

PADDLIN’ World Cup Team Ratings: Round Robin Scores

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But this is an analytics blog, and there is in fact a soccer analytics story here too. Hull City has bad underlying numbers.

The Tigers managed to finish 21-10-15 in the Championship and snagged the final place in the playoff with a sixth-place finish. But even the most cursory look at the table suggests Hull City is not like the others. The teams just ahead in the table—Millwall, Southampton and Boro—had plus-15, plus-26 and plus-25 goal difference respectively, while Hull outscored its opponents by only four more goals. The teams that finished behind Hull—Wrexham, Derby County and Norwich City--look to be much more at Hull’s level, with plus-4, plus-8 and plus-7 GD.

The simple tallies of goals for and against place Hull City as not just the weakest side in the playoffs this season, but one of the worst teams to make the Championship playoffs this century.

And the underlying numbers are worse. The Tigers have conceded 685 shots while attempting just 517, for a minus-168 shot difference. Only relegated Sheffield Wednesday and 19th-place Charlton Athletic had worse shot numbers. Expected goals calculations only slightly improve the case: Hull City has the sixth-worst non-penalty expected goals difference in the league, at about minus-13.1 These are not the usual numbers of a playoff team, and certainly not of a promoted team.

Should the Tigers win the playoff final, they would be the worst promoted team by expected goals difference in the last 13 seasons, by a wide margin. No team has been won a place in the Premier League since 2014 with a negative expected goals difference, and Hull City at minus-13 would be a little more than 18 goals worse than the previous record-holder.

Only one team since 2001 has been promoted with fewer goals scored than goals conceded: Huddersfield Town in 2017. Huddersfield did have at least a positive non-penalty xGD that season, although Huddersfield’s xGD is still the second-worst of newly-promoted teams over this period. Where the Terriers lucked out was in the playoff final draw, as Reading won the other semi-final. That Reading side is probably the most comparable team to Hull City in the recent history of the Championship playoff. The 2016–17 Royals managed to finish in third place despite a plus-4 goal difference, and their minus-21 xGD and minus-146 shot difference indicate they were probably fortunate to outscore their opponents at all. Huddersfield, despite roughly even goals and expected goals difference, was rightly favored in that playoff.

What Do Bad Numbers Mean?

It is notable that among the promoted teams with the worst Championship expected goals difference statistics, several managed to secure safety in the Premier League for another season. Huddersfield survived for a season before going back down. Burnley under Sean Dyche had multiple seasons in the Premier League, and Nottingham Forest even qualified for the Europa League this season. Sunderland, having put up only a plus-11 xGD last season, currently sits in 10th place. While none of these teams had underlying numbers quite as poor as Hull City’s, the list does offer some hope.

In particular, it raises a broader question. Do the best promoted teams have a better chance at survival in the following season, or are their other factors which actually drive Premier League results? What should we expect from a team like Hull City if they do win the final?

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The videos and images have circulated widely as fans made claims and counter-claims over how the referees should have handled the call. But the question here is not so much about whether Pablo’s specific offense should have been reviewed. Rather, this controversy offers a crystallizing moment in the 2025–26 Premier League.

Clearly the traditional norms of allowed contact no longer applied here if players thought, correctly, they could get away with everything short of Pablo’s infraction. The grappling on this corner was no outlier. It exemplified the way the game is played in England now.

A further reason that this moment has captured the imagination of football fans is that Arsenal’s involvement is poignant. The Gunners, under manager Mikel Arteta and set piece coach Nicolas Jover, were the innovators in bringing the “meat wall” to the Premier League in the 2024–25 season. Arsenal began taking far more of their corner kicks to the six-yard box and impeding the keeper with a wall of bodies to prevent the keeper from claiming the cross, as demonstrated in the previous Expecting Goals newsletter. This tactic has spread throughout the league as many of Arsenal’s competitors quickly began copying this approach. Notably, West Ham started playing these inswinging corner kicks only midway through the 2025–26 season, after seeing how successful they were for opponents.

West Ham’s tactical change offers a perfect example of how innovations in set pieces have changed the game in the Premier League by forcing clubs to adapt or miss out on crucial goals. The meat wall tactic has two key components. The first is striking inswinging corners to the six yard box, closer to goal where goal-scoring rates are higher. But the second, without which the inswingers would reap little benefit, is placing bodies in the six-yard box to impede the keeper and prevent them from claiming high balls played into their traditional zone. David Reed showed on Sky Sports that the number of attackers in the six-yard box on corners has increased by about 70 percent over two seasons. As I discussed in the first Expecting Goals newsletter on set pieces, rates of keeper claims have actually decreased this season even as corner kicks are taken at historically high rates into the six-yard box.

None of this would be a major issue, and certainly clubs like West Ham would not have felt forced to adopt it themselves midseason, if it weren’t for the core fact about the new approach to corner kicks. This tactic works. Goal-scoring from corner kicks has increased from its traditional rate of three to four percent league-wide up to over five percent this season in the Premier League. That amounts to a nearly 40 percent increase in scoring from corner kicks because of these tactical changes.

The path from here to the absurd grappling on West Ham’s corner is straightforward. If attacking players are setting themselves in the way of the goalkeeper, the defenders will try to impede them from reaching their preferred positions just as Ødegaard has. And if all of that grabbing and holding is being allowed near the keeper, will the referees really stop players from doing so elsewhere in the penalty area? A further point made by former referee Graham Scott in The Athletic is that with so many players stacked on top of each other, it is impossible for one referee to have a sightline on every possible offense.

This problem is then exacerbated by a deep structural inequality in the rules of the game. If the referee calls a foul on Pablo, West Ham lose a corner kick opportunity which has been scored about five percent of the time in the Premier League this season. But if Rice’s foul on Mavropanos is called, that gives West Ham a penalty kick with a nearly eighty percent chance of being scored. If an attacking team fights and grapples on every corner and gets called for ten fouls while winning just one penalty, they have achieved a highly favorable outcome. The multiple different ways that West Ham players were attempting to harass Raya, without trying to play the ball themselves, have a real logic under the rules of the game. Attacking teams have every reason to take more and more risks committing fouls on corners unless their offenses are whistled at profoundly asymmetrical rates compared to defending teams.

What Is To Be Done?

Commentators have circulated a number of proposals to deal with this new corner kick tactic. If people agree that all this grappling is unpleasant to watch and the “meat wall” does not fit within the spirit of the game, then something needs to change either in the rules of soccer or their application. Here are a few of the live suggestions.

• Returning to a standard of “protecting” the goalkeeper with a lower bar for fouls against the keeper on corner kicks.

• Allowing fouls to be called before play is restarted.

• Barring attackers from the six-yard box at the moment the corner kick is taken.

• Copying an idea from field hockey and requiring defenders to start behind the end line while attackers must begin from further out.

I have previously suggested that perhaps ice hockey rules offer another option, with their category of “interference” by which any action which impedes a goalie within the “crease” around the goal is an infraction. This would explicitly write into the rules the lower bar for goalkeepers by establishing a new standard that impeding the keeper is simply no longer allowed. It also may be that a simple change in refereeing norms could suffice. While there are some worrying signs, especially in the Bundesliga, refereeing standards and norms of play have mostly prevented the same kinds of grappling and interference with the goalkeeper from becoming standard in continental European leagues.

But in this newsletter I want to offer something much simpler which I believe can address all of the problems with corner kicks at once.

What if corner kicks became free kicks and were no longer taken from the corner?

Here is my pitch. First, this rule change would not introduce a new kind of action within a match that is unfamiliar to fans, players, coaches or referees. It would not require a new set of refereeing or video replay standards. There would be offside review, but that review would be handled in the standard way that offside is adjudicated on all free kicks, and there would be no change to rules about fouls or allowed player positioning. It would not require stricter refereeing, which risks counterintuitively incentivizing more contact on corner kicks, not less, due to the imbalance in the value of attacking and defensive fouls.

Further, as I argued in the initial newsletter, it seems to be precisely the lack of offside on corner kicks and throw-ins that is being exploited by the new set piece revolutionaries. There has been no meaningful change in scoring from free kick set pieces even as goals from corners and throw-ins have increased at unprecedented rates.

Broadly, there are three types of dead balls which can be used to create set pieces. There are throw-ins, which can be launched into a crowded penalty area from the sideline, and corners likewise crossed in from the corner flag. And there are free kicks, a much more diverse set of opportunities spread all over the pitch which can be used for direct shot attempts, crosses, launches, or a variety of trick plays. The effects of the set piece revolution are seen entirely in the first two categories, while scoring from free kicks has actually decreased.

…

What do corners and throw-ins have in common that free kicks do not?

There is no offside rule on corners and throw-ins when they are taken.

In these two situations, unusually within the game of football, the defending team cannot determine by its shape the area within which the attacking team must play. The attacking team can then pack the penalty area with bodies in whatever formation they choose. It appears that this particular gap in the rules has been the primary opportunity identified and exploited in the Premier League this season.

Why are players grappling for position on every corner kick? Because at the moment the corner kick is taken, attackers are already allowed to stand in, or run into optimal attacking positions. Attackers will battle for those positions knowing that even if they get called for a foul, it is probably still worth the risk. Defenders and goalkeepers will have to physically protect those optimal attacking locations and prevent attackers from setting themselves up there. The competition for space created by the lack of offside on corners is effectively the same situation that obtains at the line of scrimmage in American football. Everyone is competing for access to the same valuable spaces and the contest will necessarily push the limits of allowed contact.

By allowing the defense to set a line beyond which attacking players may not stray, if they want to be available to receive the ball, this rule would immediately prevent most forms of goalkeeper obstruction. It would also significantly limit grappling as attackers would now be running toward optimal positions rather than camping out there already.

I do not see this as the only potential solution or necessarily the best one, but I want to explore the free kick solution because it can be approached analytically, and because it has a clean and simple logic for how it would remove a set of negative incentives to higher-quality play on set pieces.

From Where Should the New Free Kick Be Taken?

This is a simple question of analytics. It combines two objective questions. What was the rate of goal-scoring from corner kicks before the meat wall took over the Premier League? What is the rate of goal-scoring from free kick set pieces based on the location from which the free kick was taken? Then it just takes a quick cross-reference to determine which areas of the pitch see free kicks scored at the same rate as corner kicks used to be scored.

Here is the map of estimated likelihood for goal-scoring from free kick set pieces.

The dotted white line represents the “break-even point” where the likelihood of scoring from a free kick matches the traditional goal-scoring rate from corners in the Premier League. The dotted blue line represents a similar break-even point, but for the current goal-scoring rate from corners.

The white line shows that it used to be the case that winning a corner kick was never as good as winning a free kick on the wing around the edge of the penalty area extended. But the blue line shows that this season in the Premier League, every corner kick is the equivalent, on goal-scoring terms, of that free kick from 18 yards out on a wing.

Following the output of the model, a free kick taken from about 25 yards out from goal, on the sideline, would be roughly equivalent to a traditional corner kick in goal likelihood. If offside applied on these new free kicks, it would create a different competitive dynamic from what exists currently on corners, and I believe it would eliminate most of the issues that were so clearly evident on West Ham’s disallowed goal.

Now, it may be that these same problems can be more or less solved by keeping attackers out of the six-yard box. I am worried that attackers would still be subject to the same incentives to grapple and obstruct the keeper, as they would only need to cover a few yards while the ball was in the air. As with any rules change, it would need to be trialed and the results would have to be analyzed. This would be true of turning corner kicks into free kicks as well, and in particular the increased number of set pieces subject to offside video review might be unappealing to fans. And of course perhaps a simple change in refereeing instructions could be enough to make the difference.

I see the free kick proposal as the most conservative of the radical rule change proposals. If a reasonable equilibrium can be achieved without removing the “corner” from “corner kick,” that is preferable. But if such incremental changes do not work, and the league is looking at complex new rules of player positioning like field hockey-style corners, the free kick proposal offers a much simpler way to solve the problem of the meat wall.

A recent analysis piece on Sky Sports looked at the “meat wall” corner kick tactic of placing attackers in the path of the keeper to prevent him from claiming the ball even when it is served into the center of the six yard box. David Reed found that the number of attackers in the six yard box on corners has increased by about 70 percent over just two seasons. In another study, Laura Hunter identified that as the season has gone on, teams have changed their tactics on corner kicks to prioritize inswingers to the goal mouth, with Liverpool having a particularly dramatic shift. This strategy is gaining new converts rather than slowing down.

On throw-ins, there are even proposed rules reforms. Setting up a long throw takes time, especially to move the defenders into position and bring the long throw specialist over to pick up the ball. A recent piece on Opta Analyst by Ali Tweedale found that the time spent with the ball out of play before throw-ins has increased by nearly 20 percent this season, from a little under 9 minutes per match to a little under 11 minutes per match. IFAB, the commission responsible for setting rules across football, has proposed to empower referees to begin a five-second count if a team seems to be taking too long to play a throw-in or a goal kick. It remains to be seen if this time limit will be enforced in a way that makes the long throw tactic less effective or simply speeds it up a little bit.

And on the pitch, the set piece goals keep coming. Set pieces account for just under 30 percent of goals scored this season, up from a little over 20 percent in recent seasons. And as the last newsletter shows, this sudden tactical shift in the Premier League has arisen not from a complex array of factors but from two clearly identifiable new ideas: the long throw and the “meat wall” corner kick.

At the same time as these tactics have increased goal production from set pieces, open-play goal scoring remains depressed. As a result, corners and throw-in set pieces combined have accounted for about 23 percent of goals scored in the Premier League this season, compared to an average of about 15 percent in recent seasons. Nearly a quarter of goals now come from corners or long throws.

How did this happen? Who is responsible for this sudden tactical shift, and how did it take over the Premier League so quickly?

A Revolution, But Who Are the Revolutionaries?

This question has a short answer, and then it also has a long answer that digs back into an earlier era in the Premier League and offers a set of contrasts that can help us understand better what is going on today. That longer answer will take up much of the length of this newsletter. It will also introduce the paradox of the Rory Delap Trap.

Still, there is a short answer.

It was Brentford and Arsenal.

In the 2025–26 season, fully ten Premier League clubs have taken at least half of their deep throw-ins long into the penalty area. In the two preceding seasons, only Brentford at about 64 percent broke that barrier. Likewise Mikel Arteta’s Arsenal, with over 75 percent of their corner kicks played to the six-yard box, stand out as the clear innovators whose corner tactics are now being copied by the rest of the league. (Arsenal’s claims prevented numbers in 2024–25 also lapped the league before the “meat wall” was copied by many of their competitors.)

One striking finding here is that the two tactics have distinct innovators, and no club before this season was executing them both. Brentford has retained a strategy on corners that makes less use of crowding the keeper than most, while Arsenal still do not play the majority of their deep throw-ins long. Instead, new adopters like Everton, Crystal Palace, Spurs and Bournemouth have taken ideas from both Arsenal and Brentford to try to maximize all of their set pieces.

It is important to grasp why Premier League teams have so suddenly altered their approach to set pieces. Clubs and managers have adopted these tactics because they work. And this is true not merely at the level of individual plays, as the previous newsletter demonstrated, but also at the team level. Clubs that adopted these tactics have been rewarded with better set piece results.

The New Corner Kick Model Takes Over

These new corner kick tactics have clearly been effective over the last several seasons for clubs which have chosen to adopt them, first Arsenal and then a spate of copycats.

And just as many teams adapted their set piece strategies over the last summer to adopt Arsenal’s and Brentford’s innovations, this has also happened during the season. Along with Liverpool, as Laura Hunter had noted, West Ham likewise in the late fall began targeting the six-yard box at much higher rates.

The speed of the adoption of these corner kick tactics raises an obvious question. While it is not hard to understand why clubs have picked up strategies that help them score more goals from set pieces, why did it take until the last few seasons for teams to find these innovations? There was a longstanding tradition, in English football particularly, of focusing on set pieces as a way to even out the advantages of more skilled and technical possession sides. Were they unable to identify the right tactics?

The Pre-History of the Meat Wall

When looked at over a longer time horizon, the sudden development of a dominant corner strategy in the last two seasons appears as a spike following a long trough. Back in 2009–10 and 2010–11 in particular, there were more teams playing their corner kicks directly to the center of the penalty area, and even, if to a lesser degree, preventing opposition goalkeeper claims.

Sam Allardyce’s Blackburn Rovers, Tony Pulis’ Stoke City and Mick McCarthy’s Wolves all show up here with strategies that resemble Arteta’s contemporary version.

Why didn’t they catch on? The simple reason is that these clubs were not particularly effective in converting their corners. Blackburn, Stoke and Wolves ran only slightly above a league average of 0.04 goals per corner in this period.

And this relative inefficiency was probably not random, as a closer analysis of their approach shows crucial differences from the modern approach.

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In the midst of working on last week’s set piece study, I pulled some data together on player shot and goal production from set pieces, and on teams’ primary set piece takers and set piece targets. I found that player set piece goals and assists are best predicted not by open play statistics, but by a combination of that player’s set piece production and by their role on the team’s set plays. These results suggest that set piece production should be understood as a separate aspect of player statistical production, and players should be evaluated and projected in distinct ways on set piece and open play skills. That is an interesting result even if it ended up being extraneous to the argument of last week’s newsletter.

I also did a few media appearances last week including a guest spot on the FML FPL podcast,1 which covers Fantasy Premier League. And I realized that my little set piece study included some actionable findings for fantasy players: measuring set piece targets helps predict goal-scoring. So that will be this week’s mini-study. But, with all respect to Alon’s lovely podcast, that was not the most important newsletter media this week.

I Made “Meat Wall” Happen

Last week I published a piece on the set piece revolution in the English Premier League and we recorded an episode of the Double Pivot Podcast discussing the study as well.

This weekend, Jonathan Northcroft wrote a column for The Times which discusses and builds upon this study, focusing particularly on how corner kick strategies have changed. With more corner kicks being struck on inswinging trajectories toward the goal mouth and more players crowding the goalkeeper to prevent claims, Northcroft suggests that clubs will prioritize bigger and stronger keepers. Perhaps Manchester City’s signing of Gianluigi Donnarumma already reflects a recognition of this tactical change.

Northcroft also picked up on a phrase we used on the podcast, describing how teams form a “meat wall” of bodies to prevent goalkeepers from reaching crosses hit to the six-yard box, which normally they would be able to claim. It was exciting to see this newsletter get picked up in major sports media, and I cannot say enough good things about the image that the Times photo editors selected of the “meat wall” in action for Arsenal against Manchester United.

(Thank you to Bluesky user Nick of the Northbank (@nicktheinventor.bsky.social) for alerting me to the article and sharing this image of the physical newspaper.)

We can only hope that “meat wall” gains momentum from here and becomes a football cliche. It has been my dream since I was a little boy that I might introduce a useful and evocative, but vaguely nauseating, term into common sports parlance.

To step back from my own feelings, the fact that this newsletter did reach a wider audience may point back to one of the study’s conclusions. Anyone who has been watching the Premier League this season has seen the set piece explosion and could easily describe the twin tactics of long throws and crowding the keeper which account for most of its revolutionary effects on the game. This study collected the data to confirm more precisely those observations and intuitions, but those observations are widely shared. Public recognition of the set piece revolution is a necessary first step to any experimentation with rule changes to rebalance the game tactically and discourage these simple but seemingly dominant tactics. When and whether such reactions arise will be one of the major stories to follow in football over the next year.

And as long as set pieces remain of elevated importance to the game, it will be even more important to understand their place in the measurement of player production.

The Mini-Study: Predicting Set Piece Statistics

I have written a set of studies for this newsletter on projecting player production using past data, incorporating age curves into projections, and accounting for positional and tactical context. But in those studies, when I looked at shots, goals, assists and shot assists, I did not separate set piece statistics from open play statistics. This appears to have been a mistake.

The year-to-year correlation of these statistics shows that future set piece goal-scoring has a much closer relationship to past set piece shot production than to overall or open-play shot production. Now, correlations do not necessarily tell the whole story, as they are a rough measure which is also affected by underlying variance as well as real relationships. As I go through the data we will find some uses for open play statistics in projecting set piece numbers as well.

Note that direct free kick shots have been removed from set piece goals for this analysis. DFK shots have an easily knowable value and assignment process, and so they should be handled separately from set piece chances on crossed or launched balls.

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As an analyst, I have watched these questions closely for several reasons. I have always had the intuition that the answer to the first two questions was “yes”, and that a skilled team of coaches and analysts could impose their ideas on the sport via set pieces to an extent that is impossible to achieve in dynamic, open-play football. Many analytics consultancies, most notably StatsBomb, have sought to develop an edge by working on set piece design.

The final reason that I have been watching these developments is that I came to soccer analytics from baseball analytics, and baseball was my first love as a sports fan. I saw the so-called “three true outcomes” take over the sport as teams, informed by analytics, realized it was a better strategy for batters to play for walks and home runs and for pitchers to play for strikeouts. The rate of balls hit into play and the amount of action on the field decreased for the simple reason that the best way to play the sport, based on the rules on the books, was not to try to put the ball in play as much. In soccer, a revolution in set pieces seems like the most obvious way that specialized analysis could change the game.

Of course, it is not necessarily bad for the game to change. A shift in strategy might lead to more goals and more excitement. Perhaps set pieces could become situations which reward clever play design that fans come to appreciate. The question, then, is what are the peculiar characteristics of the set piece moment? Which situations are being exploited to new ends, and in what ways? That will provide a more objective basis for evaluating what kind of change the game is undergoing and how the leagues should respond to it, if at all.

Set Piece and Open Play Goals in the Premier League

I am not the first person to notice a major shift in the composition of goal-scoring in the Premier League this season. Ryan O’Hanlon wrote about set pieces for ESPN already in October. An excellent recent article by Hamzah Khalique-Loonat at The Times extended this analysis and emphasized the same key point. Set piece goal-scoring has increased by a significant margin, and yet there have been fewer goals scored in the Premier League than in recent seasons, not more. Open-play goal-scoring has decreased by even more than set piece goal-scoring has increased.1

It is certainly intuitive that as football teams determine they can score goals from set pieces using new, optimized techniques, they will also take fewer risks in attacking possession. But this graph shows another, or perhaps a related explanation. The rate of open-play scoring in the 2020s has been significantly elevated compared to the previous decade, and so part of this effect appears to be the winding down of a short era of greater open-play excitement.

This data suggests that the drop in goals was caused by confluence of two factors: a broader defensive reaction against the high-scoring game of the early 2020s, in combination with a new emphasis on set pieces which enables less risky open-play attacking.

One rough estimate of attacking play is penalty area touches, and this shows a significant drop in penalty area touches for players at attacking positions from a peak in the early 2020s. It also shows that set piece penalty area touches make up a greater and greater percentage of total touches.

As Khalique-Loonat noted in another piece for The Times, touches, shots and goals by strikers are all down this season. This analysis shows that other attackers, although to a lesser degree, have also seen fewer open-play penalty area touches compared to their recent averages.

It seems likely that improvements in defensive tactics and conditioning have also made attacking more difficult, and the shift to set pieces can be understood both as a reaction to tactical trends and a cause of these trends.

But up until this point I have been talking about “set pieces” as an undifferentiated group. An analysis of the specific types of set pieces that are being exploited and the different means of this exploitation can bring to light more precisely what is happening in the Premier League this season.

Crosses and Throw-ins: A Working Theory

The “set piece revolution” may be a misnomer.2 The increase in goal-scoring has not come from every set piece equally, and in particular it has arisen from two unusual types of play.

Broadly, there are three types of dead balls which can be used to create set pieces.3 There are throw-ins, which can be launched into a crowded penalty area from the sideline, and corners likewise crossed in from the corner flag. And there are free kicks, a much more diverse set of opportunities spread all over the pitch which can be used for direct shot attempts, crosses, launches, or a variety of trick plays. The effects of the set piece revolution are seen entirely in the first two categories, while scoring from free kicks has actually decreased.

The long throw-in cross has been a marginal-at-best part of most Premier League teams’ arsenal for the last decade, and it has suddenly risen in importance to be nearly equal to free kicks. Corner kicks have seen more variance over time, but this season certainly marks a major new high.

It may be that these changes are unrelated, but this result suggests at minimum a hypothesis about what part of the rule book the set piece revolution exploits.

What do corners and throw-ins have in common that free kicks do not?

There is no offside rule on corners and throw-ins when they are taken.

In these two situations, unusually within the game of football, the defending team cannot determine by its shape the area within which the attacking team must play. The attacking team can then pack the penalty area with bodies in whatever formation they choose. It appears that this particular gap in the rules has been the primary opportunity identified and exploited in the Premier League this season.

If Anything is a ‘Revolution’, it is the Long Throw

There have been over 0.12 goals per match this season from throw-in set pieces. This means that in a typical weekend of watching the Premier League, you would expect to see a goal scored from a long throw. This is an increase of more than double from the 2024–25 season, which saw about 0.055 goals from throw-in set pieces, and an increase nearly four times over from the longer-run baseline of about 0.03 goals from long throws.

The basic explanation for this is simple. Teams play far more throw-ins directly into the penalty area than ever before.

The percentage of throws taken from attacking areas directly into the penalty area has nearly tripled compared to the 2021–25 average. Somewhat fewer throws are taken backward, while only half as many are taken to the attacking wing zone.

This radical shift in throw-in strategy can also be seen from a graph of frequency over time.

While small changes were already visible before this season, the change has been truly radical. Within one season, crosses surpassed both types of short throws to become the most common play used on a throw-in from attacking areas.

This mass change in the approach to throw-ins has required a new focus on talent, as teams have identified which of their players has the strongest overhead throw. And it has required specialized work to draw up ways to turn the floated ball in the middle of the penalty area into the best scoring chance possible. But to a great degree, this is simply an opportunity that has been hiding in plain sight for years.

When crosses accounted for less than 20 percent of attacking throw-ins, they led to shots and goals at much higher rates than short throws. On a tactical level, it seems obvious that teams should investigate whether they can increase their usage of long throws to realize these better results at scale. And that is what has happened.

The increased tactical focus on throw-in set pieces appears to have allowed Premier League teams not only to maintain existing levels of efficiency on these plays but even to increase their production. Long throws over the past four seasons have led to shots about 21 percent of the time and goals two percent of the time. This season, even as teams have taken more and more throws long, success rates have increased, with almost 24 percent of long throws leading to shots, and 2.4 percent to goals.

Further, it is possible that the increased use of long throws has had other, positive knock-on effects. The rate of shots and goals from throw-ins taken short has increased by about the same amount as on long throws. When opposition teams have to defend the penalty area against a potential long throw, the attacking team also has the option to run other plays or simply take a short pass in for a cross.

These success rates suggest that, so long as football’s rules remain the same, teams will have little excuse for not making throw-in set pieces a core part of their attacking game. The gains cannot be denied.

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Free Kicks: The Opportunities Left Behind

We know that goals from free kick set pieces have decreased this season in the Premier League. Expected goals tell a similar story, with expected goals per free kick running at about 0.12 per match compared to a past average closer to 0.13.4

There has been a small increase in the use of free kicks as set pieces, especially from midfield, but the amount of change is much smaller than the numbers with throw-ins.

The clearest change here is a meaningful, but not enormous increase in the rate of set pieces played from central areas in the attacking half but not the attacking third. Teams are launching these balls forward more often, but not by more than five to ten percentage points. The large majority of free kicks from these deep areas are still taken short.

There are some peculiar drops in free kick set piece frequency in a few of the areas near the goal. I do not have good explanations for these zones specifically. My primary hypothesis is that teams are playing somewhat more complex set pieces occasionally, and the rules I have set up do not capture these well. One notable point about free kicks in the attacking third, however, is that teams already use these for set pieces the vast majority of the time anyway. If there were to be a large increase in set pieces, it would have to come in what John Muller has called the “Dyche Zone” of launched free kicks from deep areas.

To try to capture more precisely if free kick strategies have changed, I created a model of the likelihood of a free kick being taken as a shot, a cross or a launch. This season there have been somewhat more launches than expected, but only by a small amount.

These results suggest that there is not much room left for teams to play more crosses or shots. But the launched “Dyche Free Kick” still accounts for less than one-third of free kicks from the deepest areas of the attacking half, and only about ten percent in the most advanced areas of the defensive half. John Muller has shown the potential of these free kicks, and even in the midst of the set piece revolution there is little evidence they are being exploited to the fullest. Possibly, when teams feel they have finally maximized crowding the keeper on throw-ins and corners, there will still be more new set piece chances to create.

Corner Kicks: A Dominant Strategy

One might have thought there would not be more room to maximize corner kick goal scoring. Almost all corners are already used as set pieces and kicked long. But it turned out that there are better and worse options for corner kick placement, and significant value to be gleaned from these adaptations.

In a study for Sky Sports, Adam Smith, Nick Wright and David Forster showed that one major component of the increase in set piece goal-scoring has been an aggressive focus on in-swinging corners taken to the six-yard box. The area directly in front of the goal-mouth has been the main target, now accounting for nearly three in ten corner kicks. And that is probably only some of the corners aimed at the goal mouth, as even the best corner kick takers do not hit their spots every time.

The graph of the increase in corners to the six-yard box resembles the long-throw graph for its dramatic shift, although this one does take place over two seasons rather than one.

These crosses obviously target locations where the goalkeeper should be able to get to the ball. But despite this, the rate at which goalkeepers are claiming crosses has gone down in the Premier League. The current trend of surrounding the opposition goalkeeper with bodies, so they cannot patrol the area closest to goal and prevent shots from close range, is clearly working.

I built a model to estimate the likelihood of a cross being claimed, using a similar method to the free kick model, to account for the significant increase in crosses played into the vicinity of the goalkeeper. The model suggests that this season in the Premier League, keepers have been able to claim only about two-thirds of the crosses they normally would under past conditions. Strikingly, in 2024–25, the first season where crosses to the six-yard box surpassed all other crosses as the most common corner kick routine, the rate of claims also spiked and the model more or less predicted the number of times a keeper would rise to catch the ball.

It is true that keeper punches on corner kicks have increased this season, and so keepers are at least getting to somewhat more crosses than these numbers suggest. But punches are always a secondary option for a goalkeeper, which they take when they are unable to safely claim. Again, this suggests the crowding the keeper strategy is working.

This strategy may help explain one of the primary oddities in the corner kick data. Unlike the situation with throw-in crosses, the increase in goal-scoring from crosses to the six-yard box has not been paired with an increase in shots from these chances.

Once again, I included the expected goals numbers in the hopes of explaining why so many more goals have been scored from a very small increase in shot attempts. Here, xG can offer only a partial explanation. Clearly the chances being created in the six-yard box are better than in past seasons, according to the xG model, but only to a degree that explains somewhat less than half the increase in goals scored from these shots.

Two possible explanations present themselves, both of which likely have some merit. The first is that part of the corner kick goal explosion this season has been a fluke of finishing, and the scoring rate from corners should come down to some degree. The second is that the strategy of crowding the keeper is throwing off the xG model, which was not trained on data from this tactical context.

There are two other points to draw from this graph. First is the own goal number. Own goals make up a small, but still significantly larger percentage of goals from corner kicks taken to the six-yard box than corner kicks taken to other locations. Playing a ball more or less at the goal increases the chances it will take a deflection off a defender on the way in.

Second, we see a large increase here in goals scored from short corners. The xG data suggests this is probably more of a fluke than anything else. But it seems possible again that changing the direction of attack unexpectedly, with the keeper surrounded, might also lead to benefits that the xG model is not able to pick up.

One reason I am emphasizing the importance of the tactical change of goalkeeper interference is that past data suggests aiming crosses to the six-yard box was not an obvious winner. This is different from the evidence on throw-ins where playing them long has always been more successful than taking them short. With corners, the inswinger hit to the goal-mouth only becomes a dominant strategy in combination with other changes which magnify the advantages of such crosses.

Conclusion: Looking to the Future, and Germany

Anyone who has been watching the Premier League this season could have told you that set pieces have taken over ever more of the game. This newsletter, to a great degree, serves only to confirm what your lyin’ eyes have already told you. But what is going to come next?

In his piece for The Times, Hamzah Khalique-Loonat reported that he had spoken to an IFAB official about set pieces in September and they had no concerns at the time. While some changes in the rules may be implemented, in particular a shot clock for bringing the ball back into play, I am skeptical these will make any major dent in the effectiveness of these strategies. A long throw specialist sprinting rather than jogging over to take a throw-in will save some time, but it is unlikely to make long throws meaningfully less of a dominant strategy.

What we will have to see in the Premier League is whether any tactical counterstrokes exist. So far none has arisen. It is possible that the set piece trend is like innumerable previous tactical trends, an innovation just waiting for a counter-innovation to come along and re-balance the game. But it is also possible, and it tends to be my hypothesis, that set pieces are a real gap in the football rulebook, situations where a dominant strategy exists and clubs will have few choices but to follow the trend, at least until more significant reforms to the rulebook are implemented.

If that is true, the question may not be what responses to set pieces there are, but rather whether set pieces have been fully exploited yet. In particular I wonder if launched free kicks will become a larger part of the game as teams look for new edges.

But even more than the Premier League, I will be watching the top leagues around continental Europe. If these strategies work, they will surely be picked up elsewhere.

In particular, the Bundesliga shows signs of its own tactical shift to set pieces.

While the increase in goals from throw-ins this season is small, that is because the long throw had already peaked last season. The rates of goal-scoring from long throws in the Bundesliga in 2024–25 and 2025–26 are the two highest of any league since 2010–11 other than the Premier League this season.

On corner kicks, the data points in the same direction.

Goals from corner kicks are up in the Bundesliga, and crosses hit to the goal-mouth have increased significantly. Nearly 40 percent of corners are being placed in the six-yard box.

This appears to also correlate with a decline in keeper claims, although it is worth noting that Bundesliga goalkeepers are far more reticent to claim crosses at baseline.

Watching matches in the Bundesliga, I have noticed several teams making use of recognizable Premier League strategies around the goal during corner kicks. I would love to hear from more dedicated German football fans if this trend is something they have noticed as well.

One of the theories of the Premier League’s set piece revolution has been that Premier League refereeing standards are a core factor. The idea that goalkeeper “should be stronger” when he is blocked by a wall of bodies from reaching the ball is certainly common in English commentary. It may be that refereeing norms which are kinder to keepers elsewhere will limit the effect these tactical trends. It is also possible that refereeing standards simply have not been stress tested yet, and there is more room to make use of physicality, or just masses of human bodies, in the continental leagues as well.

What the Bundesliga data suggests is that it is already happening, and it is already succeeding, if to a lesser degree.

I am by no means certain that the set piece revolution has only just begun. Any number of dynamic responses, from defensive adjustments to national norms of play and refereeing, could prevent the continuation of these trends in England or their extension to other football leagues. At the same time, corner kick and long-throw tactics have been adopted and demonstrated their effectiveness in the Premier League with remarkable speed. At this rate of spread, top football leagues across the world could see rapid shifts in goal-scoring and tactical trends within the year. I am particularly interested in whether this summer’s World Cup will crystallize these trends for an even larger audience.

There is every chance that the set piece revolution has only just begun.

Appendix on Method: Defining “Set Pieces”

Identifying which situations can be identified as set pieces begins as the most basic football question and eventually develops into an irreducibly uncertain quandary that can only be resolved with some best-guess heuristics.

Obviously when a player plays a dead ball cross into the center of the penalty area and their teammate heads it home, that’s a set piece goal. If a player launches a long throw into the penalty area and it isn’t cleared and in the chaos a defender kicks it into the net, that’s a set piece goal. But there are two important edge cases. The first is, when does a dead ball play constitute a set piece? How far does the ball need to be kicked or thrown to start a set piece action? And second, when does a set piece end? If a ball is cleared out of the penalty area but recovered by the attacking team and they cross it in again during the second phase, is that still the same set piece?

For this analysis, I made the following choices.

1. All corner kicks constitute set piece opportunities even if they are taken short.

2. Throw-ins are counted as set pieces when they are thrown into the penalty area, or when the data is tagged as a “throw-in set piece” by the provider, indicating that the action involved a designed play aimed at creating a good shooting opportunity within the next couple touches.

3. Free kicks are counted as set pieces when they are taken directly as a shot, played as a cross, or launched at least 20 yards and into the central area of the pitch, either into the penalty area or a region about eight yards extended beyond the penalty area. Likewise, when an action is labeled as a “set piece” for some other characteristic not immediately visible in the on-ball data, that will also count the action as a set piece.

Further, I chose to count second phase opportunities within the same set piece action. A “second phase” ends when the possession itself ends, either with a turnover or a dead ball, or when the ball is recycled into the defensive half of the pitch, or 20 seconds elapse from the initial playing of the dead ball. These definitions mean that my numbers may differ slightly at the margins from other studies, but they clearly accord with the findings of others in broader terms.

All data from Opta unless otherwise noted.

You can read the initial newsletter introducing the model for a full description of the method, but broadly it is based on a combination of expected goals created and conceded, actual goals created and conceded, and estimated team value from TransferMarkt, with adjustments for schedule difficulty, home field advantage, and time played with a man advantage or disadvantage.

This post will include the updated Expecting Goals Power Rankings for each of the big five European leagues: the Premier League, the Bundesliga, Serie A, La Liga and Ligue 1. Because the model is based on league football only, the ratings are only scaled relative to the league, they do not project how well two teams from different leagues would fare against one another.

Here’s what the columns in the table mean.

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?

Performance +/-: This is the change in the team’s performance rating from the previous published version of the power rankings (in this case December 5).

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.

Rating +/-: This is the change in the team’s projected rating since the previous published version of the power rankings (in this case December 5).

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.

Premier League Power Rankings

See the actual Premier League table from fbref.com here.

Commentary

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You can read the linked post for a full description of the model, but broadly it is based on a combination of expected goals created and conceded, actual goals created and conceded, and estimated team value from TransferMarkt, with adjustments for schedule difficulty, home field advantage, and time played with a man advantage or disadvantage.

This post will include the updated Expecting Goals Power Rankings for each of the big five European leagues: the Premier League, the Bundesliga, Serie A, La Liga and Ligue 1. Because the model is based on league football only, the ratings are only scaled relative to the league, they do not project how well two teams from different leagues would fare against one another.

Here’s what the columns in the table mean.

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.

Premier League Power Rankings

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So I did what anyone in this situation would do. I built a statistical model.

In some ways this is just a newsletter about how cool machine learning is. I put together a set of data with election results and census demographics by New York City election districts and I fed them into a “Gaussian Mixture Model” to create clusters of similar election districts, and of the 20 clusters it created, 15 had clear and coherent stories, and shockingly coherent geographies, despite the lack of geographic markers in the data. I merged two of the clusters into one group because they made more sense together than apart, and so that left me with 14.

(For a little more on the method and process, see the Appendix on Method at the conclusion of this newsletter.)

I should say here that writing about political geography entails making generalizations about populations. In many of these areas, there are large groups making up 20 percent or more of the population whom I do not discuss. That does not mean they are unimportant. Further, obviously a majority 70 percent of a particular population, whether black or East Asian, lower income or college educated, is made up of many thousands of people irreducibly different from one another. What this analysis seeks to do is not to deny those obvious facts about people. Rather, I think it is striking that despite the ways people are ever unpredictable and changing, we can still identify population level trends by geography and electoral and demographic factors.

Let’s get into the different groups.

The Mamdani Base

The core of Zohran’s base in the primary and the general lay in the Brooklyn and Queens waterfront neighborhoods of Greenpoint, Williamsburg, Bushwick and Astoria.

Mamdani put up margins of 60 points and more in these districts, amid surging voter turnout. This cluster also has the highest percentage of younger adults of any that the model identified. It is professional-class and highly educated but not exceptionally high-income. These are areas with multiple Democratic Socialist local representatives already, and it is hardly surprising they supported a DSA Mayoral candidate.

Several other areas also gave large support to Mamdani, and while these areas are significantly different, perhaps what ties them together most is a high percentage of young people and professional-class voters.

This captures large sections of the neighborhoods of Clinton Hill, Crown Heights, Bedford-Stuyvesant, Prospect-Lefferts Gardens and Flatbush in Brooklyn, and Central Harlem in Manhattan. These are majority black areas with significant young, college-educated, left-progressive populations. Mamdani did not quite rack up the same majorities here as he did in North Brooklyn and Astoria but it was close.

Although Mamdani got far fewer 1st place votes in the ranked-choice primary here than he did in the previous cluster, nonetheless when the ranked choice voting was completed he had two-thirds of the vote. In these affluent, professional-class, white progressive areas, voters used their full ballots and Brad Lander got his best numbers in the mayoral race. Thanks to the ranked-choice system and Lander’s full-throated cross-endorsement with Mamdani, those votes were eventually assigned to Mamdani.

This map shows the neighborhoods of Park Slope, Carroll Gardens and Boerum Hill in Brooklyn, Long Island City in Queens, and a bit of the East Village, Chelsea and Hell’s Kitchen in Manhattan. The best thing about this map is how it identified Morningside Heights, near Columbia University, as sharing these qualities with Park Slope.

I am not sure if the identification of these particular Manhattan neighborhoods—Morningside Heights aside—as being significantly different from the rest of affluent, primarily-white Manhattan is correct. As we go through more of these and my grasp of the political geography gets progressively weaker outside Brooklyn, I will have more uncertainty.

This cluster, even more so than the previous one, is not quite as geographically coherent, with a few little dots strewn around on perhaps dubious grouping logic. But the core of it remains compelling to me. We have chunks of Williamsburg and Bushwick in North Brooklyn, Red Hook and Sunset Park on the Brooklyn waterfront, a bit of the Lower East Side, Spanish Harlem, West Harlem and Washington Heights in Manhattan. These are lower-income areas, majority Hispanic with significant white and black populations, but again very young and with a significant college-educated minority. Mamdani’s margins here were not as enormous as in the above clusters, but he won in both the primary and the general by over 20 points.

Here we also see the first story of the 2020s shift to the right. Trump gained seven points in these districts between 2020 and 2024, but in the down-ballot Comptroller race in 2025, Mark Levine won those voters back and more.

For a fuller analysis of the recent Republican trend in New York City’s voting and whether 2025 saw it reversed, see the 2025 election analysis newsletter, to which this post is a companion.

This map cuts a swath through a series of central and eastern Queens neighborhoods, including Richmond Hill, Ozone Park, and South Ozone Park. These are mixed-race and heavily South Asian areas where Mamdani ran shockingly strong in both the primary and the general election compared to the recent level of Democratic support. He won 61 percent of the vote in the November election, just about exactly equal to Kamala Harris’ share in 2024. If you wanted to look for Trump-Mamdani voters, this is where you should go. It seems very plausible that a South Asian candidate running for Mayor was able to connect with and win over an unusual number of South Asian voters, even among those who had trended away from Democrats in recent elections.

Here, support for Mamdani coincided with a dramatic shift back toward Democrats. Harris had lost 16 points of support from Biden in 2020, and Levine won it all back even beyond Mamdani’s impressive win.

Swing Areas in the Primary

During the primary, Mamdani ran surprisingly strong in a number of areas where Democrats had mostly struggled recently. Generally he did not end up with majorities in these neighborhoods in the general election because large numbers of conservatives did eventually turn out to vote. But his margins in the primary made up a meaningful part of his victory, and we see a number of hopeful signs for Democrats in the general election results.

This is another somewhat spotty cluster, but I think it still captures something. We have heavily Hispanic, working class neighborhoods of Woodhaven, Elmhurst, and Corona in Queens, as well as some chunks of the Bronx. We can see here that lower-income, more moderate Hispanic voters supported Mamdani in significant numbers in the primary and the general election. And just as with the heavily South Asian areas above, this coincided with a massive reversion to Democratic support in down-ballot elections. Where Harris had fallen to 63 percent support in 2024, Mark Levine won 79 percent just one year later.

Mamdani actually won a small majority in the primary in Flushing, Sunset Park, and Manhattan Chinatown, but a more conservative general electorate made these swing regions in November. Here, strikingly, we see the same pattern of recent Democratic losses being reversed, but now in an area where Andrew Cuomo also received a large share of the vote. Voters in New York City’s Chinatowns were splitting their tickets, not supporting the Democratic nominee for Mayor but backing down-ballot Democrats like Mark Levine by high margins that hadn’t been seen in some years.

Large areas of Southern Brooklyn and Northeastern Queens with a mixed White and Asian population have leaned to the right in recent years. In Bayside and Bensonhurst, voters backed Cuomo in both the primary and general election, by notably larger margins in the general. But at the same time, down-ballot Democrats like Mark Levine and Kayla Santosuosso in Southern Brooklyn managed to win majorities. These areas are more conservative on the whole than Flushing and Sunset Park, with larger populations of conservative white voters, but they swung back to Democrats in 2025 nonetheless.

Swing Areas in the General

The story of the 2025 Mayoral election was that Andrew Cuomo couldn’t hold on to his base. The middle- and working-class moderate Democrats who backed him in the primary ended up giving small majorities to Mamdani in the general.

In the black middle class neighborhoods of Southeastern Queens—Jamaica, Springfield Gardens, Queens Village, Rochdale, Cambria Heights, and Laurelton among others—Cuomo managed 40 point majorities in the primary, albeit with depressed turnout. Flatlands and Canarsie in Brooklyn showed similar numbers. But in the general election, majorities of voters in these 90+ percent Democratic neighborhoods ended up preferring Zohran Mamdani. The election was lost when Cuomo could not hold on to these votes, and it was never clear that he or his campaign understood this was where the election would be decided.

We see an extremely similar story in the lower-income neighborhoods of East Flatbush, Brownsville and East New York in Brooklyn, and in the Northeast Bronx. A nearly 30-point shift toward Mamdani from the primary to the general.

Both this and the preceding cluster demonstrate that there was much less of a shift to the right among black voters in New York City than among Hispanic and Asian voters. What small shifts did occur also appear to have disappeared in 2025.

The black and Hispanic South Bronx, which contains many of the poorest neighborhoods in the city, tells a similar story about the Mayoral election as Mamdani gained about 20 points between the primary and the general.

In 2024, the Bronx was a surprising epicenter of Democratic losses, as even in 2022 Kathy Hochul’s vote share in the Bronx had held up reasonably well. Just a year later, Mark Levine won back the double-digit losses Democrats had suffered in these districts.

Cuomo Conservatives and the Manhattan Mystery

Cuomo’s base in this election was Republicans and Manhattan.

I’m sure there is much diversity in the many neighborhoods of Southern Staten Island, in Brighton Beach, Breezy Point, and other highly Republican areas. But the model saw Harris at 25 percent and lumped them all together. And for the purposes of looking at the 2025 election, this is correct. Republicans were motivated to vote against Zohran Mamdani and they did so by enormous margins.

Those margins and turnout, however, were nothing like what Cuomo racked up among Haredi Jewish voters. Mamdani won under 10 percent of the vote in these areas and turnout even increased from the 2024 general election.

One of the reasons I decided I would publish these clusters is that the model found most of New York City’s Haredi communities despite the fact that the census has no data on religious affiliation. The peculiar voting patterns and demographics of these neighborhoods identified themselves to the clustering model.

So finally we are left with Manhattan. These are wealthy, mostly white areas that have historically voted for Democrats by enormous margins. And ultimately, they did support Mark Levine 78–22. But in an election where Democrats were running well ahead of their baseline everywhere, even in Staten Island, it is notable that Democrats lost ground in Manhattan, as well as in Riverdale in the Bronx.

As I said in my election analysis, I would guess that the dynamics here are peculiar to Manhattan and Riverdale. It will remain to be seen whether Manhattan moves to the right while the rest of the city moves left.

On New York City: The Big Apple: The Greatest City in the World

This model helps to demonstrate the immense complexity of New York City’s politics. There is no single ‘Democratic Base’ or ‘Working Class’ vote in New York City. There are many distinct political ecosystems, and the political climate of the 2020s has impacted them in similar but never identical ways.

While the topline result showed a city swinging back to the left, that shift was built on a complex coalition. Young progressives in Astoria, older Black homeowners in Jamaica, and South Asian families in Richmond Hill ended up voting in similar ways, but they all took different paths to get there, through the 2020s and then during the primary. And that’s just in Queens alone. The underlying dynamics which made New York City a bellwether for the nation in recent years likely stem from this very complexity. Whether we want to understand New York City for its own sake, or for what it can tell us about national political dynamics, we must start with this diversity before identifying the trends that can tie different people and different neighborhoods together.

Appendix on Method

This model was built from publicly available data from the US Census Bureau and the New York City Board of Elections. There is an enormous and probably incomprehensible public spreadsheet with the data I used for this analysis which you are welcome to play around with. The census provided information on race/ethnicity, class and education. The election data focused on dynamics since 2020, looking at general election outcomes in 2020, 2021, 2022 and 2024, as well as primaries in 2021 and 2025. This took into account total vote share, changes in vote share between elections, and shifts in turnout.

I merged the census data with the election data by extrapolating from geographical overlaps between census tracts and election districts. Because New York City updates its voting districts every year, sometimes more than once a year, older election results are also extrapolated according to geography. (This is why, if you look at the spreadsheet, it is assigning fractional votes to Joe Biden and Eric Adams. That’s from the geographic extrapolation.)

The “Gaussian Mixture Model” took this data and reduced it to 20 clusters. Five of the clusters had to be discarded as ultimately incoherent. Two of the clusters made much more sense grouped together than separated.

Broadly, machine learning is only as good as the data you feed into it and your ability to make sense of its outputs. I knew which data I had used in the past to explain political trends in New York City, and I provided those to the model rather than every single line of data I could find. The results of the model were, to my eye, shockingly compelling and coherent. But still five of them did not work at all. So I selected the ones that did appear meaningful, and that’s how this newsletter came to be.

New York City in the 2020s is not an outlier; it is a bellwether. The drift of non-white working-class voters away from Democrats began here in 2021, foreshadowing the national catastrophe of 2024. Now, the 2025 results are telling a different story.

Beneath the unusual structure of the Mayoral race, the data reveals a massive swing back toward Democrats, driven not by off-cycle turnout quirks but by former Trump supporters changing their minds. Despite all the drama of 2025 in New York City, the most important story might be how normal the election ultimately was. It is precisely that normality which makes New York City worth deep consideration for electoral analysis.

How Zohran Won the Primary

Now, to explain how the 2025 general election can offer insights into the national political situation, I need to explain the New York City Mayoral primary, which was anything but a normal American election.

Here’s a full map of city results from the primary.

The first way this election was unusual is that New York City has ranked-choice voting in its primaries, which allowed multiple candidates to cross-endorse and encourage their supporters to rank allied candidates. Thanks to cross-endorsements, Mamdani consolidated the support of eliminated candidates in the ranked-choice rounds to expand his lead. He won the first round vote by a little under 8 points, 43.8–36.1, but then in the final round he defeated Cuomo by about 13 points, 56.4–43.6. (In this newsletter I will generally be using the final-round head-to-head vote when I talk about the primary, and that is what the above map displays.)

Second, the primary election was driven significantly by differential turnout. Consistently, rates of voting ran higher in the areas where Mamdani’s support was concentrated.

The area—in blue on the left and green on the right—running from the Queens waterfront down through Brooklyn and around Prospect Park gave Mamdani his largest margins anywhere in the city. These areas also saw some the highest rates of voter turnout, compared to the 2021 Mayoral primary, in the city.

By contrast, most of Cuomo’s strongest regions, from Southern Queens to Central Brooklyn and the Bronx, had some of the lowest rates of turnout in the city. These are light red on the left and purple on the right.

There were 14 Assembly Districts where turnout in the 2025 primary did not surpass 2021 turnout, and Cuomo won 13 of them.

But the few districts where Cuomo did perform well in the primary and where voter turnout did increase from the 2021 baseline are perhaps just as indicative of the dynamics of this primary. AD45 and AD48 in Southern Brooklyn are home to large Haredi Jewish communities who have recently strongly supported Trump, as well as significant numbers of Republicans. AD62 in Southern Staten Island is the most consistently Republican area of the city, while AD76 in the Upper East Side is a moderate, affluent Democratic area. All four regions are heavily white. In areas where Cuomo’s support was highest among white voters, his campaign turned out supporters at good rates.

But most of Cuomo’s support did not come from affluent moderate or conservative whites. It came from black and Hispanic middle class and working class voters in Brooklyn, Queens, and the Bronx. And those are precisely the areas where voter turnout fell.

(If you are interested in the political geography of New York City, see the accompanying newsletter, “The Political Geography of New York City”.)

The core of Cuomo’s failure in the primary campaign was that his campaign, with its focus on Israel-Palestine issues and on the dangers of socialism, did successfully turn out voters in areas where these topics were pertinent. But for the working and middle class black and Hispanic voters that made up Cuomo’s real base, the campaign had little in the way of message or outreach. Further, with his median-voter focused message on affordability, Mamdani was able to reach moderate voters who may not share his larger ideology. Mamdani cut down the margins of his losses in most of his opponent’s base areas, winning on average about 40 percent of the vote there while racking up 70 percent or better numbers in his best districts. And of course Cuomo failed to drive turnout among most of his base.

Mamdani, by contrast, motivated a historic shift in primary voter turnout.

The 2025 New York City General Election: A Normal Election

While the primary was marked by an unusual voting structure and massive shifts in the voting population, the general election ended up a surprisingly normal affair. Even though Andrew Cuomo ran an independent, third-party campaign and Republican nominee Curtis Sliwa refused to step aside for him, Mamdani’s eventual winning coalition was not all that different from a typical Democrat’s coalition.

Obviously Mamdani, running in a three-way race against a former Democratic governor, ended up with a much lower overall vote share than Kamala Harris (51 percent as opposed to 68 percent). However, the shape of his support is easily recognizable. Mamdani lost Republican areas and won Democratic areas, if with lower margins in some of the Democratic areas. The only real exception is the Upper East Side of Manhattan, a Democratic area where Cuomo nonetheless notched a clear victory.

But for the most part, Mamdani won because Democrats voted for the Democratic nominee in a general election.

A map of the change in vote share from the primary shows that Mamdani gained votes in Democratic areas and lost votes in Republican areas.

It was precisely in heavily Democratic areas of the Bronx, Southern Queens and Central to Eastern Brooklyn where Mamdani made major gains compared to the primary. Republicans turned out in force in Staten Island, Southern Brooklyn and Northern Queens to vote against Mamdani. But at the same time a critical group of Cuomo supporters from the primary, moderate and mostly black and Hispanic Democrats, flipped to back Mamdani and gave him the margin he needed.

Once again, Andrew Cuomo’s campaign, buoyed by support from Donald Trump, successfully won over many of the Republicans they were targeting. But they lost Democratic support for every Republican they won and that’s not a good trade in New York City.

It’s worth returning to the point that Republicans turned out to vote. While Mamdani had a massive turnout advantage in the primary, the November electorate showed no obvious trend either way compared to 2024.

One of my favorite little indicators of how far to the right a New York City electorate tilts is the percentage of the vote that came from Republican Staten Island, from everywhere in the borough excluding the North Shore.

Unlike in 2018 when Democrats massively outvoted Republicans in a blue wave year, here Republicans made up a healthy slice of the electorate.

There are two big takeaways from this point. The first is that the Mamdani campaign did not discover a way to change the electorate with a new message. Instead, it seems that Republicans were unusually motivated to vote against a socialist candidate, leading to an electorate that was more balanced, or perhaps even slightly tilted to the right, compared to expectations in an off-cycle election.

Republican voters have agency, and they expressed their preferences in clear terms. Right-wing media, influential figures, and voters themselves recognized and opposed Mamdani’s ideology in particular.1 Conservatives are nowhere near a majority in New York City, and so they were outvoted. The story here is that a Democratic Socialist running on the Democratic line can absolutely win a high-stakes election in a large Democratic city, but not without facing a substantial right-wing backlash.

At the same time, even if this election result doesn’t validate a new way of winning elections for the left, it does offer a surprisingly representative view of the electorate right now. A “normal” election in an off cycle year doesn’t come along very often, and this is an opportunity for deeper analysis.

A Normal Election is a Representative Election

The problem with drawing any conclusions from the November election is that the Mayoral contest itself can’t offer any particular insights because it was a three-way race. But there were two citywide elections, for Comptroller and Public Advocate, contested between Democrats and Republicans, appearing on the same ballot.

Mark Levine won the two-party vote for Comptroller by a 77–23 margin, and Jumaane Williams won the two-party vote for Public Advocate 75–25. These were the best citywide candidate numbers since 2020. (Chuck Schumer managed 74–26 in his Senate re-election in 2022, that’s the closest one.)

These were down-ballot candidates, where odd things can happen. However, it is worth noting that in the 2021 citywide election, the Public Advocate and Comptroller candidates (Williams and Brad Lander) ran only one or two points ahead of Adams.

Further, there were some tightly-contested City Council races in New York City this cycle which offer a more granular view of voter shifts. In particular, Council District 30 in Central Queens and Council District 47 in Southern Brooklyn had open seat elections, which means they were unaffected by incumbency effects. Both were tightly contested, as Republicans had made major gains in these areas since 2020. The Democratic candidates, Kayla Santosuosso in CD47 and Phil Wong in CD30, won by double digits. Levine and Williams put up similar numbers.

It is important to note that it was Democrats in two-ways contests who ran strongly here, not Zohran Mamdani. Cuomo won both CD30 and CD47, by three and five points respectively. What the down-ballot races show us is that while Mamdani gained most of his votes from Democrats and liberals, there was a crucial slice of voters who chose Cuomo while voting Democratic down-ballot. These swing areas of New York City have swung back, but the candidates who can win here need to win over a significant minority of Cuomo voters.

The down-ballot races all seem to confirm that, beneath the three-way Mayoral election, Democrats had an extremely strong election in New York City. With an electorate probably more conservative than the 2024 electorate, Democrats outperformed Harris’ baseline by something in the range of six to eight points, and by more in these swing council districts.

As I have previously written, New York City was a bellwether for the nation in the Biden years. The collapse of support for Democratic candidates in 2021 and 2022 in New York City ended up being a preview for the nationwide Democratic losses in 2024. The concentration of losses among non-white voters, particularly among Hispanic and Asian voters, as well as white people without college degrees, was a notable feature of weak runs by Eric Adams and Kathy Hochul in 2021 and 2022, and then a nationwide problem for Democrats in 2024.

While Harris trailed Biden’s support level by 6.6 points citywide, she fell about 8-9 points in Council Districts 30 and 47. And CD30 and CD47 are not among the places where Harris lost the most votes from Biden. Nonetheless, they are representative of the shift away from Democrats in 2024.

(CD47 spans most of the Southern Brooklyn waterfront while CD30 is a relatively conservative area of Central Queens.)

So this raises a question. If Democrats gained back ground unusually in two council districts which had shifted right, were Democratic gains in 2025 concentrated in precisely those areas?

A comparison of Levine’s and Williams’ vote shares with Kamala Harris’ from 2024 and Joe Biden’s from 2020 shows that this appears to be the case.

Levine and Williams saw only modest improvements over Harris among the white conservative voters of Southern Staten Island, Northeastern Queens, and Rockaway Beach / Breezy Point. But across more heavily Asian and Hispanic areas of Northern and Central Queens, the Bronx and Southern Brooklyn, their gains almost perfectly mirror Harris’ losses.

This map does not quite look like 2021 to 2024 never happened, but it is close.

The 2025 election in New York City shows a massive swing back toward Democrats in a city which had been the epicenter of Democratic losses over the last several years. This swing back did not happen because of differential turnout, despite 2025 being an off-cycle election, because Zohran Mamdani motivated conservatives to turn out to vote. It looks like somewhere between six to eight percent of voters simply changed their minds about Republicans.

The Exceptions: Upscale Manhattan and the Haredi Vote

We see massive gains for Mark Levine, and some for Jumaane Williams, in areas of Southern Brooklyn, South Williamsburg, Central Queens and Far Rockaway which are home to large Haredi Jewish communities. The vote in Haredi areas tends to move more like a bloc, with influential rabbis able to activate large majorities with their endorsements, and often happy to swing back and forth between Democrats and Republicans in local elections to ensure funding and support. The fact that Levine, and to a lesser degree Williams, were able to sway Haredi votes is a specific local dynamic best treated as an outlier.

To account for this, and to describe the political geography of New York City more clearly, I built a “Gaussian Mixture Model” based on electoral and census data to categorize the political regions of New York City. (See the companion newsletter on the political geography of New York City for a full discussion of these model results and what they can tell us about the city.)

One thing that came out very clearly from this analysis was the distinct political behavior and demographic qualities of Haredi districts. If we exclude the districts in this cluster, Levine and Williams end up almost exactly even, at 75 percent, Harris is unchanged at 69 percent and Biden jumps up slightly to 77 percent.

But there’s another clear outlier on the map. The swath of Manhattan from Battery Park and Tribeca on the lower west of Manhattan, up through midtown and both the Upper West Side and Upper East Side bordering Central Park, shows a notable trend against Democrats. This was also a region where Cuomo ran unusually strong in the general election. My model found a large cluster that maps directly to these districts: heavily Democratic, well-educated, extremely high-income, mostly white areas where Zohran had limited primary support. The cluster model also identified Riverdale in the Bronx as having similar dynamics.

In these areas Levine and Williams lost about 5 points of support compared to Harris, unlike in the rest of the city where they made large gains.

So what is going on here? I am torn between two possible explanations. To what degree did affluent white voters in Manhattan, who opposed a Mamdani mayoralty, vote Republican down-ballot in the hopes of balancing out Mamdani rather than because they have changed their political leanings? And to what degree have a minority of Manhattan Democrats reacted to the politics of 2025 by turning to the Republican Party?

If we exclude the Manhattan cluster from analysis as well, the graph looks pleasingly symmetrical.

My guess is that this is not quite right, and Democrats really have lost some percentage of Manhattan voters in the welter of 2025. It is easy to imagine the story of moderate Democratic voters, many of them Jewish, who were negatively polarized against the pro-Palestinian protest movement and then against a socialist candidate for Mayor. But even if that is what happened, will these voters continue voting Republican after 2025? I would guess there are a fair number who will swing back in elections where state and federal, rather than local offices are up for a vote.

Further, for the question of what New York City says about the country, is this then a story peculiar to New York City, a story of voters activated by the Columbia protests and the backlash to the Mamdani campaign?

If the goal is to interpret what New York City says about the rest of the country, we should not lean too heavily on the peculiar dynamics of Manhattan. I think here we have a situation where dynamics truly peculiar to New York City have been the primary driver of election results, and this is less likely to be representative of the nation as a whole.

And in that case, this data reflects a city where Republicans had been gaining ground steadily for years, in ways that were representative of movements in the nation as a whole, and where in a high-turnout election with a representative electorate, Democrats gained seven to nine points above their previous level. That is a very large shift and it was probably primarily a matter of voters changing their minds about whether voting for Republicans is a good idea.

The National Context

In a week where Democrats saw a 13-point overperformance in a special election in Tennessee with nearly midterm-level turnout, this analysis can be added to the growing evidence of a blue wave collected by G. Elliott Morris at Strength in Numbers. Further, the specific shape of Democratic gains in New York City matches with the national trend.

Nate Cohn at the New York Times identified in the special election results from Virginia and in particular from New Jersey evidence that Democratic gains were likely driven as much by persuasion as turnout. Both exit polling and voter registration data suggest that the electorate was, while Democratic-leaning, not so blue that it could account for most of the Democratic wins on the night.

Further, some of the biggest changes from 2024 occurred in North Jersey counties, many heavily Hispanic, which had flipped to Trump in 2024. An analysis by the polling expert Mary Radcliffe found that Trump’s approval rating has been dropping fastest among precisely the groups Trump won over in 2024: younger voters and Hispanic voters. The polling is not granular enough to identify shifts among Asian voters, particularly Asian voters without a college degree, which are so striking in the New York City data. But it would make sense that yet another group of voters who tried out voting Republican in 2024 have decided perhaps it was not for them.

Election Day 2026 is still far off, and as always there is no more powerful driver of political outcomes than Events. But the signal from the 2025 data is becoming undeniable. New York City offers some of the strongest proof yet of a building blue wave. Precisely because Mamdani’s candidacy activated conservative opposition and generated a highly representative electorate, the down-ballot results from this election offer an unusually clear picture of former Trump voters, numbering perhaps seven to nine percent of the electorate, actively changing their minds.

Despite these seemingly shocking numbers, Liverpool’s end of season slide was not considered anything notable at the time. The win for Chelsea made a major difference in the Blues’ chase for a Champions League spot, but it didn’t matter to Liverpool. The Reds had clinched the title the week before at Tottenham. They had nothing to play for.

Famously, the difference between European sporting competition and the formats in use in North America is that in league play in Europe, there’s almost always something on the line. The teams at the bottom risk relegation to a lower league while at the top qualification for different levels of European competition remains at stake until late into the season. Only rarely does a team have more than a few weeks of matches where the points don’t matter for something.

But perhaps because there are so few of these matches, there is broadly an acceptance of the notion that teams ease up after their fate is sealed for the next season. No one asked what was wrong with Liverpool despite the obvious change in their performance level.

So this raises the most basic sort of sports analytics question. Are the intuitions of soccer fans correct? Do teams actually stop playing their best when they have “nothing to play for”? And how exactly does their play change?

What To Expect: Jonathan Liew’s Study

In a 2014 study in The Telegraph, Jonathan Liew identified 76 matches in the Premier League between 2010⁠–11 and 2013⁠–14 in which at least one of the teams involved had nothing to play for. He found that these teams did indeed play worse, taking fewer points per match. And strikingly not only did their goals conceded increase, but so did their goals scored per match. But the increase in goals conceded outpaced the increase in goals scored, leading to an aggregate decrease in goals difference.

In this study, I wanted to update Liew’s study using a larger set of data that would allow for more fine-grained analysis. It was always striking to me how intuitive Liew’s findings were, that teams with nothing to play for just stopped defending, and maybe took some more risks to attack. These results suggested that soccer players really do prefer not to defend so much, that defending is a thankless task involving hard work, focus, and putting your body on the line which players would love a chance to opt out from. And perhaps it is even the case that without the normal pressures of league football competition, players can express themselves more in attack.

The core questions here, then, are how much worse do teams play when they have “nothing to play for”, are these changes in play different in defense and attack, and do teams which have secured different places in the table, whether that means winning a title or facing relegation, show different effects from one another?

Who Has Nothing To Play For? Building the Data Set

So the first task is identifying the relevant matches. I looked at matches played in the big five European leagues starting in the 2010⁠–11 season.1 In these league seasons, teams might have “something to play for” at many different levels.

• Title. Obviously the team that finishes first wins the title.

• Champions League group stage qualification. In different seasons and leagues, somewhere between the top two and top five sides might gain automatic qualification to the Champions League group stage with their finish.

• Champions League playoff qualification. Teams finishing 3rd or 4th, in some seasons, have won access to the Champions League playoffs rather than directly to the group stage.

• Europa League and Conference League group stage or playoff qualification. This follows the same structure as Champions League qualification for the group stages or playoffs, but takes on many different forms season to season.

• Relegation / Promotion playoff. In the Bundesliga and Ligue 1 in most seasons under consideration, the top-ranked team among the bottom three in the league goes to a relegation / promotion playoff.

• Relegation. The bottom two or three teams (or the bottom four from Ligue 1 in 2022⁠–23) are sent down to a lower division.

A confounding factor is that qualification rules are fluid. Domestic cups in particular offer an alternate pathway to Europa League qualification, and when lower-placed teams win domestic cups, they can take one of the league’s qualification slots. Rather than slog through 15 years of domestic cup results to track the shifting Europa spots, I implemented a strict filter to avoid false positives. Based on the relevant league and UEFA rules, I found the places which corresponded to title-winning, Champions League qualification, and relegation, and identified the moment in the season where clubs mathematically locked in these places. I estimated the Europa qualification places based on the maximum number of places which might be given to Europa qualifiers and then excluded these places from consideration entirely. This ensures that I can be equally definitive that the final group below, teams which have guaranteed survival but cannot qualify for Europe, includes no one who could qualify for the Europa or Conference Leagues via their league position.

This method identified:

• 203 matches played by 63 different teams which had clinched a league title

• 249 matches played by 127 different teams which had secured a Champions League group stage place, as well as 15 matches played by 12 teams that had secured a playoff spot

• 337 matches played by 127 teams that were guaranteed relegation, as well as one team that had clinched a spot in the relegation playoff

• 1,115 matches played by 542 teams that were guaranteed survival in the top division but would not earn a place in Europe via their league position

Their results in these matches were easily distinguishable from their results in previous matches, to highly statistically significant degrees.

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And it means that while I covered the Premier League earlier this week, I still have a lot more Power Rankings to share.

La Liga Team Ratings and Power Rankings

La Liga table at FBRef for comparison

Glossary

If you want to see the method and explanation for these power rankings and how they were developed, that is in my earlier post this week, along with some proposals for future study.

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.1

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.

La Liga Commentary

• We covered La Liga in some detail on the most recent subscriber episode of the Double Pivot podcast, using these team ratings and our observations of the matches to break down the table.

• Probably the most immediately striking there here is that while Real Madrid rate as the best team in Spain, Barcelona do not join them in that top tier so far. Barca have struggled with injuries, but also the defense has been conceding far too many good chances. It is very, very hard to win a title when you are conceding the sixth-most expected goals in the league. My system here rates their defense a little better than raw xG would suggest, both because there are two penalties in the xG total and because Barca have allowed only ten non-penalty goals, a more midtable total. So far these performances over nine matches have only dropped their Team Rating a little behind Madrid’s. Clearly there is time for a comeback.

• Villarreal play Espanyol on November 8. I may regret it but I am planning to watch and the season data so far suggests it has a chance to be an entertaining match between two teams that have, at least so far this season, been playing good attacking football.

• But the La Liga midtable really tells the story of Spanish football right now. The dominant tactical trend among sub-elite clubs is Getafe-style negative football, a style built on preventing opposition chances with a defensive approach that doesn’t rely so much on conceding opposition possession but on blowing up midfield with physicality, longballs and aerials, and driving down the percentage of live ball play in the match. It’s not great.

Serie A

• Serie A table at FBRef.

• As it has been for several years running, Serie A remains the most competitive top league in Europe. Inter have consistently had the best underlying numbers, but not by such large margins that the normal variation of soccer2 couldn’t allow Milan or Napoli to sneak in for a title. With Inter sitting third, three points behind Roma and Napoli, such drama may be on offer again in Italy.

• If there was one thing most people agreed on before this Serie A season kicked off, it was that Atalanta losing longtime manager Gian Piero Gasperini to Roma would mean that Atalanta’s magic in developing attacking talent would leave for Roma as well, or perhaps that Gasperini had lost his touch and it wouldn’t work at Roma either. But somehow what has happened is that Atalanta remain one of the best teams in the league with a new breakout striker in Nikola Krstović and Roma have joined the ranks of Serie A’s leaders due to an elite defense, which is carrying a still sputtering attack. Football!

• Promoted Cremonese are in eighth place now but with the second-worst expected goals difference in the league, and even this model which gives significant credit for goals scored has them among the worst sides in the league. It might be they have booked enough points to stay up but it looks fluky.

• Generally the bottom of the table in Italy looks just as competitive as the top so there may be a dramatic relegation battle here as well.

Bundesliga

• Bundesliga table at FBRef.

• Bayern Munich’s 2.80 Team Rating is by a good margin the best of any team in the leagues covered here. At the same time, Bayern’s dominance over Germany is not new. Their Team Rating has run over 3.0 for periods in the last decade, in 2019 under Nico Kovač and earlier in 2017 under Carlo Ancelotti. During Pep Guardiola’s tenure between 2014 and 2015 the Team Rating almost never dropped below 3.0 and nearly touched 4.0. Bayern have unquestionably dominated the league so far but it will take even a higher level than this to match their modern heights.

• It is striking that despite RB Leipzig running more or less even on Team Performance with Dortmund and Leverkusen, the model has not lifted their Team Rating up to a similar level. It is never clear to me what rate of updating is best, especially when a team has historically been competitive but has a terrible off year. Perhaps the club mean could incorporate a larger sample of past data. In any case, Leipzig’s negative expected goals difference last season will clearly take more time to filter out.

• Gladbach have fallen all the way to last place and they rate among the bottom teams in the league across all metrics, but the one reed of hope is they have played the toughest schedule in the Bundesliga. On the other side of the coin are Stuttgart, who have played seven of their eight matches against teams in the bottom half of the table with the only exception being eighth-place FC Köln.

Ligue 1

• Ligue 1 table at FBRef.

• The most striking thing here is not so much that PSG are not running away with the league — they have been rotating the squad in league matches with abandon and are still in first place with a perfect Champions League record as well. Rather, it is the apparent stratification of Ligue 1, a league more commonly marked by parity (PSG excluded) over the last decade. The top clubs are putting some distance between themselves and the rest of the table.

• Marseille have played about one-third of all their league minutes either with a man advantage or disadvantage. They have a 24-11 goal difference overall but a 16-8 goal difference at even strength. This ends up not making too large a difference in Marseille’s overall team rating because of the way the red card adjustment is implemented, with a 40 percent credit or penalty to attacking and defending. Marseille creating about five xG and scoring eight goals while up a man over a few matches rates as such strong play, despite the favorable situation, that they still remain near the top of the ratings.

All underlying data from Opta unless otherwise noted.

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?

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Every individual statistic in soccer carries within it the dynamism of the game. The work of soccer analytics is finding ways to pull apart and measure the various, overlapping relationships that are encoded in every measurement of team and player actions on the pitch.

Over the next several newsletters,1 I will examine new ways to account for these contexts and possibly offer revisions to previous results. As a first step, I have taken some of the results of the previous study and re-built models around them to look for places where contexts may be showing through. This newsletter will dig into the player-position data from the last study to offer a few new ways to identify the dynamic, tactical effects, and then examine the influence of larger competitive environments on player longevity.

The Positional Aging Spectrum

Players typically get on the ball further and further away from goal as they age, but this finding about touch profiles can be deepened with positional data. I took the playing time by position records of players from 2010-2025 in the big five leagues and built a generalized additive model to predict playing time by position in the next season with age as a factor. This creates an estimate at each age of how much a player retains their playing time at a particular position or is likely to move to a different position.

As in the previous studies, I have grouped positions together in large buckets: center back, fullback, central midfielder, wide midfielder / winger, central attacking midfielder, and striker. To begin, this is the model output for the positional aging paths of strikers.

This shows that players who get minutes at striker at a young age are often converted to wide attackers, but the tendency to move to the wing dissipates by the early 20s. There is over time a small increase in movement from striker to the 10, but ultimately strikers do not move off the position commonly after the age of 26 or so. This model suggests that after peak age, strikers tend to stay at striker with a retention rate around or above 90 percent.

If you add up the model outputs for all the different positions at age 26, you get 0.04, or 4 percent to WM, about 2 percent to AM, and negligible numbers elsewhere. That means that maybe 7 percent of minutes at striker are not retained but move to another position, and a player who plays striker at age 26 should be expected to play about 93 percent of their minutes at striker in the next season.

Although it is the case that players tend to move further away from goal as they age, this effect does not seem to be reflected in the data on positional changes among strikers. And it is also hard to find among wingers and wide midfielders.

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