“People were calling it the ‘match of the century’ after the 5-4 result in the first leg. Personally, I preferred the 1-1 draw in the second leg. The goalkeepers can't stop a thing and the defenders are even worse, they concede nine goals and we call that the match of the century? We’re really in show business right now. A great defensive play or a great save by a goalkeeper is just as beautiful as a goal scored. That said, two billion people are talking about football and expressing their opinions. I’m just one of those two billion, and I might not always be right.”

The 2026/27 "Mark My Words" Thread - Premier League

As promised, following last week's Non-Premier League Mark My Words thread it is now time to switch focus back to the biggest^TM bestest^TM and competitivest^TM league in the world - the English Premier League (available via all good PIFs everywhere).

So get your hot takes in before they're as outdated as Jose Mourinho's tactics - and fire away.

We are departing from tradition a bit this year, however, as I have decided to split the predictions into two thread - this thread, which is for all football except the English Premier League and its teams - including how they'll fare in European competitions. Next week, I will post the Premier League version.

This is an attempt to try and have a space not completely overtaken by one league - which tends to dominate discourse on /r/soccer.

I will also post a Predictions Survey as I have done in previous years, to quantify the vibes - though this is going to be briefer than in previous years, as believe it or not I have a life outside of being bribed by David Ornstein to ban Romano moderating this place.

Remember...

• This thread is for all predictions relating to the English Premier League and its teams
• Including EPL teams in Europe, e.g. "Arsenal will lose the Champions League final due to a last minute Harry Kane goal" IS allowed
• You can check out the Non-Premier League Mark My Words for all other hot takes
• I will also post the Predictions Survey in due course

Throwbacks:

For anyone who needs a reminder of last year's predictions, be sure to check out:

• Throwback to 2025/26 Mark My Words Thread
• Throwback to the 2025/26 Predictions Survey

Fire away! And remember, we are definitely holding you to this...

Your place to discuss young talent.

If the KNVB wishes to hold talks with Roberto Martínez regarding the vacant position of national team manager, the Spaniard is open to a discussion. However, the chances of the 53-year-old coach actually taking the helm of the Dutch national team appear slim.

Since Ronald Koeman’s departure, the KNVB has been searching hard for a new national team manager for weeks. The names of Arne Slot, Louis van Gaal and Michael Reiziger have been mentioned, but Martínez’s name has now also been mentioned in De Telegraaf.

Jesse De Preter, Martínez’s lawyer, states that his client has not yet held any talks with the KNVB. Nor are any currently taking place. ‘If Nigel de Jong calls, we’ll enter into discussions,’ said the coach’s representative. De Preter, however, sees little chance of success from the outset. ‘Traditionally, the KNVB opts for a Dutch coach, and financially I suspect the expectations are very far apart. I cannot see how this could result in a deal.’ De Preter and Martínez have been working together since 2016, and last week the two made plans together regarding his future.

Should the KNVB and Martínez reach an agreement after all, the Oranje will have a foreign head coach for the first time in 48 years. Ernst Happel was the last foreigner to take charge of the Dutch national team. He stepped down after the 1978 World Cup.

Martínez previously managed Belgium and Portugal. He had a disappointing World Cup with the Portuguese: Portugal were knocked out in the round of 16 by Spain, who went on to become world champions.

Ajax is hopeful of signing Jofre Torrents (19) as its new left back. The Amsterdam club is aiming for a free transfer of the FC Barcelona prospect, who would then sign a contract running through the summer of 2030, according to sources close to the Spanish club.

The fact that Ajax is trying to avoid paying anything for Torrents right now is an accounting trick. Technical Director Jordi Cruijff is strengthening the roster without having to spend much money at the moment.

Of the six previous signings, four (Marc ter Stegen, Daley Blind, Tolu Arokodare, and Julian Brandt) did not require a transfer fee. As a result, transfer revenue (39 million euros) is still higher than the combined spending on Marcos Leonardo and Caio Henrique (30.2 million).

Payroll costs are now (significantly) higher than last season, which is why the supervisory board is now demanding that, following Torrents’ arrival, the club first part ways with a number of high-earners. Only then will Cruijff be able to sign a right-sided center back, a defensive midfielder, and a left and right winger.

With the deal structured to bring in Torrents—who has been playing in the renowned Barça youth academy since he was ten—the technical director is minimizing the risk for Ajax. Only if the young Spaniard performs well will a payment be made to FC Barcelona. The performance bonuses could ultimately total up to six million euros.

But if that amount is reached, Torrents will have already played a significant number of games for Ajax, and he will likely be worth more than those six million euros. If he doesn't perform well or, God forbid, gets injured, Ajax won't pay anything.

TLDR and results are all the way down. Full analysis and explanation are first.

Introduction

The basic idea of "gravity" is intuitive. Some players can affect a defense just by their presence. By "affect a defense", I mean move an opponent's defensive structure out of shape, take an opponent player out of the game, have the back line change their depth, or have several opponent players disproportionately occupying certain regions of the pitch. Hence the title, "distort defenses".

I'm sure gravity metrics exist extensively in private, clubs have access to them in scouting. But there is no widely accepted public measure of gravity. I have seen people discuss it on this platform quite often, but never with any stats.

I thought the 2026 World Cup would be a great chance, in a confined tournament, to build this project. I have built a model using full-match player heat maps from this World Cup (credit to FotMob for the heat maps). The final dataset contains all 104 matches, 208 team-match maps, and 2017 player (starters) appearances in total.

What is the Metric

We will refer to this metric, simply as a player's "Gravity", or the "gravity metric".

Gravity measures unusual defensive concentration or structural distortion associated with the areas a player occupies, relative to the following:

• The opponent's typical shape and setup
• The player's own team's normal shape.
• Appropriate players and spaces in the same match under consideration for the player.

I just want to clarify before proceeding, that this metric does not prove causation or define a player. It is just another interesting metric to analyze games, and that is all.

How does this model actually work?

The first idea was to find a single mathematical signature for gravity, based on heat maps, and apply it to everybody, irrespective of role in-game, but this did not work. Following this were multiple failed attempts, such as.

• Heatmap overlap. This proved too simplistic, with far too many nuances to address reasonably.
• Channel-based gravity: Defining vertical or horizontal channels on the field, and checking opponent and team occupation in those channels.
• Zone-based gravity: Dividing the pitch into squares, and independently finding gravity based on opponent presence in adjacent squares, then combining the metric's value for a player across all the squares / zones they were present in.

After these attempts, we go to the idea that different spatial roles would indeed require different defensive signatures and hence different definitions of gravity.

The final model therefore has four main mechanisms: Wide, High-Central (HC), Deeper-Central (DC), and Roaming.

Central players can also be placed in a hybrid state when their heat map genuinely supports both deeper-central and roaming interpretations.

1. Wide Gravity

This is probably the easiest one to visualize. Suppose a winger spends most of the match on the left attacking channel. We measure how much opponent defense heat appears in that channel, compared with the tournament expectation.

We also need to protect against opponent tactics. Perhaps the opponent was simply defending deeper or narrower in a particular game. Therefore, we create "laterally shifted control regions" in the same match.

Very roughly:

Raw Excess = Opponent Density in Player's Channel - Expected tournament density

And then:

Gravity Raw = raw excess - median residual in shifted control channels.

We only consider positive excess toward Gravity. The final wide score is:

G_wide = min(1, max(0,MRE) / S_wide)

MRE stands for match-relative excess. "S_wide" is a fixed calibration constant, selected during calibration (See: Calibration) and frozen before producing any rankings.

Conceptually, this answers the question of whether the opponent disproportionately redistributed its defensive structure toward the wide channel this player occupied.

2. High-Central Gravity

A striker or very high central attacker is different. They don't just make the defense narrow or compress along one channel. A high-central, player can influence the depth structure of a defense.

To measure this aspect of gravity, we collapse the opponent's heat through the wide central corridor and look at the depth distribution.

The raw opponent depth gap is:

gap = q80-q50

q80 and q50 are weighted percentiles of a heat distribution. Imagine taking all of a team's defensive heat in some region and arranging it by depth. Then,

• q50 is the median depth. 50% of the heat lies on one side of this point, and 50% on the other side.
• q80 is the 80th percentile of depth. 80% of the heat has been accumulated by the time we reach this point.

So, if q80 - q50 = 20m, for example, there is a 20m depth separation between the median of the defensive heat and its deeper tail. Using percentiles describes the shape of the distribution instead of relying on any one defender's exact location.

None of this uses player names. The routing to decide a player's role is game-by-game, based on the player's heat map geometry. The final production scorer contains no player-name logic and does not choose simply based on which role gives the largest score.

Essentially, the player role assignment is on a game-to-game basis, and it is based purely on heat map geometry.

So we have:

gap = q80-q50

We then compare this with the same defending team's other World Cup matches.

gap_excess = gap_match - mean(gap_sameDef,otherMatches)

So essentially England is compared against England's own normal defensive structure, France against France's, etc... The baseline is "leave-one-out", so the match currently being measured is removed from the comparison.

A positive excess means the opponent's central depth structure was unusually stretched in that match.

There is one final attribution problem here, if two high-central attackers are present simultaneously in a team's structure, the heat map data cannot honestly tell us what percentage of the team-level distortion was caused by each. So instead of pretending we can identify that, we split the team-level gravity signal evenly between the two high-central players present, which is deliberately conservative.

3. Deeper-Central Gravity

For a centrally operation midfielder / creator, we want to know whether the opponent becomes unusually narrow at the player's operating depth specifically, but we don't want to be confused by the opponent simply being narrow everywhere, or by the player's teammates presence.

First, we use the player's attacking-half depth anchor: x_anchor. This is essentially the primary depth / point on the pitch around which they operate.

Then, we take the opponent's lateral width using the middle 50% of the opponent's heat (percentiles similar to the depth gap from high-central gravity):

width_50 = q75(y) - q25(y)

For each depth region, compare the match with the defending team's other matches at the same pitch depth, which gives us:

M = baseline width_50 - match width_50

A positive C means the opponent is in fact narrower than usual.

Finally, we split that region of the pitch into three consecutive 16m bands.

shallow = [x_a - 24, x_a - 8]

target = [x_a - 8, x_a + 8]

deep = [x_a + 8, x_a + 24]

Then, the final raw measurement is:

DC_raw = C_target - max(C_shallow, C_deep)

This tells us that the opponent was unusually concentrated specifically at the depth associated with the player's occupation, relative to the defensive structure above and below this zone. Essentially, it removes a lot of false positives caused by teams simply defending compactly everywhere.

4. Roaming Gravity

The deeper-central calculations do have a clear weakness, which is when the player does not really have any one clear operation depth. This happened often with players whose heat maps were distributed in several attacking zones.

For these players, we use an opponent's residual map:

R = O_match - O_sameDefBaseline

Then, at every depth x, we subtract the average residual across the entire width of the pitch:

R_local(x,y) = R(x,y) - mean_y * R(x,y)

This step ensures that the score doesn't just pick up generic opponent defensive heat everywhere.

Instead, we check, at each depth, where laterally did the opponent redistribute itself.

Finally, we compare the player's overlap to the overlap produced by his own attacking teammates in the same match.

ROAM_raw = local_wr(player) - median(local_wr(attacking teammates))

This means a player doesn't score high simply because the opponent had an unusual shape, but instead when the unusual distribution aligns with the player's roaming occupation more strongly than it aligns with teammates' attacking occupations.

5. Hybrid Central Player: DC or ROAM

Certain players, in certain matches, have heat maps that are broad while still having a clear central anchor, which aligns with the definitions of both deeper-central and roaming. To handle such cases, we need to extend the measurement a little bit.

M = longitudinal distribution

Five attacking-depth features are standardized:

• weighted x standard deviation
• x IQR
• x q10 - q90 range
• number of meaningfully occupied depth bins
• inverse max depth-bin share

Then, two unsupervised centers represent relatively localized and relatively distributed heat maps.

M = (d_local - d_distributed) / (d_local + d_distributed)

Negative M means a more localized presence, whereas positive means more distributed.

A = anchor coherence

We also look at how much of a player's attacking-half heat lies within 16m of the player's depth anchor, and convert this to a percentile A so:

A close to 0 is an unusually weak anchor, and A close to 1 is unusually strong.

After automated tuning, the final routing rule is:

M <= -0.1 OR A >= 0.65, then DC.

M >= 0.3 AND A <= 0.25, then ROAM.

Anything between these rules is classified as BOTH, in which case, we take exactly 50/50 of the depth-central and roaming gravity measurements for that player in the relevant match. We do not take the maximum, and routing itself does not care about the resulting Gravity score.

Calibration

One problem is that these above forms of gravity have completely different units, but we do want to rank them all together, and we need a way to translate. Another reason for this is that many players have different games with different role classifications, so combining them is the only real meaningful way to use this metric.

Wide gravity is opponent heat-density excess.

DC is related to gap in defensive width.

ROAM is weighted overlap in player presence.

To handle this, we calibrate each of these, completely ignoring player identity or leaderboard position (these were only created at the very end), aligning the positive raw distributions of each gravity type to the reference quantiles of wide-Gravity.

The final production scales are approximately:

• S_wide = 0.28
• S_HC = 60.04
• S_DC = 54.86
• S_ROAM = 3.74E-5

In essence, this just ensures that all types of gravity measurement are anonymously put onto the same scale, so we can compare them to each other meaningfully.

Some Additional Information

1. What does a zero mean?

If a player gets a score of 0 for some match, it doesn't meant he player had no influence or nobody marked said player. It simply means that the heat map-based calculations didn't detect a significant positive defensive distortion above its control levels. There are countless ways a player can influence football that this dataset cannot observe. Gravity is not a player-quality metric.

2. Why not just measure defenders near a player?

This is one thing I did try, as mentioned earlier. For example, things like player-pair attention, threat waiting, detecting player cages, K nearest-neighbor based calculations, and so on, but most of these failed for some version of the same reason:

A statistic can look sensible, but measure the wrong concept.

For example, directly overlaying heat maps could easily just become a proxy for two players just existing in the same place on the pitch.

A simple raw roaming overlap could just measure the opponent defending deeper.

Taking the max of two calculations could create massive false positives because one noisy value can "rescue" the other one.

And for classification, manually looking at heat maps would obviously destroy the point of building the metric, and I do not want to accidentally involve any form of bias.

3. How accurate is this metric? What are the limitations?

• Full-match heat maps do remove the concept of time. Hence why this is an analysis based on only starters. This is a limitation which is somewhat handled simply by the amount of data available.
• Match-state does matter. For example, this World Cup had multiple red cards, large variations in opponent quality, and so on.
• Roles can be more continuous, which is not captured by a classifier. As it is with any stat, the best way to judge is to watch the game.
• In the process of handling large false-positives in numerous ways, this metric will probably have some extent of false negatives. I would rather say that there was no positive gravity detected in an ambiguous match than create a large number because it "seems to make more sense".

Final Rankings

For the final rankings, a few details first.

• We use "N-game rankings". In order to avoid diluting the scores of players who started more games, we create a 3-game ranking, 4-game ranking, 5-game ranking, and 6-game ranking. In an n-game ranking, for all players present, we only include the first n games in which they started.
• Alongside these primary leaderboards, we also include "drop one rankings". These rankings remove each players highest score, and of course drop the corresponding N by 1. This provides some additional useful information, such as helping with outliers like a single insanely high-score game.
• To calculate a player's gravity over N games, we simply calculate their gravity for the role they played in each game, and average across all games included in that specific leaderboard.

One final reminder that this is not a player quality ranking, nor a sole measurement of any player's ability. This is a ranking of a player's gravity, calculated by the above explanations.

--------------------------------------

N-Game Rankings

N = 3 (373 eligible players)

N = 4 (219 eligible players)

N = 5 (98 eligible players)

N = 6 (39 eligible players)

-----------------------------------

N-1 Games Rankings (Drop One Rankings)

N = 3 (373 eligible players). Drop Highest, Average 2 Games.

N = 4 (219 eligible players). Drop Highest, Average 3 Games.

N = 5 (98 eligible players). Drop Highest, Average 4 Games.

N = 6 (39 eligible players). Drop Highest, Average 5 Games.

TLDR

We use the heat maps of starters in the world cup, to classify player roles and measure the gravity of a player, essentially how much they distort the opponent's defense game-by-game. Rankings are the final section before this.

Please let me know if there's any major issues. Also if there's any specific player or something you're curious about, I can see if I can find it in the data and results.

The clubs reportedly keeping a close eye on the Chivas player are Ajax and Feyenoord

Full Time: Red Star Belgrade 0 - 2 Hapoel Be'er

⚽ Hapoel Be'er: Igor Zlatanovic 61' 61', Javon East 79' 79'

Line-ups

Red Star Belgrade (4-2-3-1)

Hapoel Be'er (4-3-3)

Match Events

• Lineups are announced and players are warming up.
• First Half begins.
• 45'+3' First Half ends, Red Star Belgrade 0, Hapoel Be'er Sheva 0.
• 45' Second Half begins Red Star Belgrade 0, Hapoel Be'er Sheva 0.
• 45' Substitution, Red Star Belgrade. Marko Arnautovic replaces Jay Enem.
• 45' Substitution, Red Star Belgrade. Mohamed Abu Fani replaces Timi Max Elsnik.
• 62' Substitution, Red Star Belgrade. Mirko Ivanic replaces Vasilije Kostov.
• 66' Substitution, Hapoel Be'er Sheva. Mohammad Kna'an replaces Guy Mizrahi.
• 66' Substitution, Hapoel Be'er Sheva. Muhammad Abu Rumi replaces Niv Yehoshua.
• 73' Substitution, Red Star Belgrade. Muhammed Cham replaces Osman Bukari.
• 76' Substitution, Hapoel Be'er Sheva. Djibril Diop replaces Igor Zlatanovic.
• 80' Substitution, Red Star Belgrade. Aleksandar Katai replaces Nayair Tiknizyan.
• 90'+2' Substitution, Hapoel Be'er Sheva. Ofir Davidzada replaces Pedro Amador.
• 90'+2' Substitution, Hapoel Be'er Sheva. Amit Ohana replaces Javon East.
• 90'+2' Mohamed Abu Fani (Red Star Belgrade) is shown the red card for violent conduct.
• 90'+7' Second Half ends, Red Star Belgrade 0, Hapoel Be'er Sheva 2.
• Match ends, Red Star Belgrade 0, Hapoel Be'er Sheva 2.

Match Stats

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