Forma
Forma · World Cup 2026 Hub · as of 2026-06-27

Every club watches the World Cup.
Forma watches what it tells us.

Five modules built on the open WC26 dataset: a tactical fingerprint of all 48 nations, a knockout simulator running 10,000 brackets, a Hidden Value Index of tournament risers, a club→country talent pipeline, and a daily Forma Spotter. 64 of 72 matches in. Refreshing daily.

Matches
64/72
Teams placed
48
Bracket sims
10,000
Risers surfaced
30
Module 01 — Tactical Identity Map

48 nations. One map.

Possession, shot rate, foul rate, set-piece reliance, defensive load, and chaos — distilled into a tactical fingerprint and clustered into six archetypes. The dataset has no event coordinates, so shot rate stands in for verticality and foul rate stands in for pressing. Hover any dot. Filter by archetype.

← STYLE AXIS 1 →← STYLE AXIS 2 →MEXRSAKORCZECANBIHQATSUIBRAMARHAISCOUSAPARAUSTURGERCUWCIVECUNEDJPNSWETUNBELEGYIRNNZLESPCPVKSAURUFRASENIRQNORARGALGAUTJORPORCODUZBCOLENGCROGHAPAN
Possession control
Long spells with the ball, sustained build-up, low chaos.
ALGARGAUTBRACOLJPNMEXMARNORPANSCOSUIUSA
Vertical transition
Short possessions, fast attacks, high shots-per-touch.
BELCANEGYENGFRAGERESPTURURU
Compact counter
Cede possession, soak pressure, strike on turnover.
CPVCIVCROECUIRNNZLPARSWE
Set-piece dependent
Disproportionate share of chances from corners and dead balls.
IRQNEDPORSENKORTUN
Defensive lockdown
High save and block load, low offensive output, ugly but effective.
CUWQATKSA
Chaos creators
High variance both ways — entertaining, unpredictable, hard to model.
AUSBIHCODCZEGHAHAIJORRSAUZB
Tournament fingerprint, not season fingerprint. The dataset has no event coordinates, so 'Shot rate' is a verticality proxy and 'Foul rate' is a pressing proxy — both will move with referee profile and game state. Sample size shown per team; expect identities to sharpen through the knockouts. Dot opacity reflects sample size.
Module 02 — Knockout Simulator

10,000 brackets. A model strength rating, not a literal forecast.

Per-team xG ratings blended with an elo prior, Poisson-sampled match outcomes, run end-to-end through the remaining bracket. Bracket pairings are seeded by qualification order, not by FIFA's draw rules — these numbers are a strength rating, not a quoted title probability.

Read this firstBrackets are seeded by qualification order, not by FIFA's draw rules — the official R32 pairings are not in the open dataset. These numbers are a model strength rating, not a literal title probability for any specific team's actual path.
Simulations
10,000
As of
2026-06-27
Top model rating
ESP
10.2% strength share
Most likely final
Spain v Argent
2.5%

Model strength rating — top 12

From 10,000 sims
ESPSpain
10.2%
ARGArgentina
8.8%
GERGermany
8.4%
CANCanada
7.0%
PORPortugal
6.7%
FRAFrance
5.9%
BELBelgium
5.0%
NEDNetherlands
4.4%
USAUSA
4.0%
ECUEcuador
3.7%
MEXMexico
3.7%
COLColombia
3.6%

Most likely finals

  1. 1Spain v Argentina2.5%
  2. 2Germany v Argentina2.3%
  3. 3Spain v Portugal2.1%
  4. 4Germany v Portugal2.0%
  5. 5Canada v Argentina1.8%
What this model is missing

Per-team xG-for and xG-against from completed group matches, blended with an elo-derived prior using confidence weighting (matches played / 3). The elo→xG mapping (1.0 + (elo−1500)/600, clamped 0.5–2.2) is an uncalibrated heuristic, not fit to historical data. Match outcomes are Poisson-sampled with team-strength-adjusted expected goals. Extra time uses a 30% scoring rate; penalties resolve with a coin flip. Bracket pairings are seeded by qualification order (R32[0] vs R32[1], etc.) — they are NOT FIFA's official draw, so a given team's strength rating does not correspond to its real path. Model ignores injuries, suspensions, travel, fatigue, home-crowd effects, goalkeeper identity, and the actual draw. Treat outputs as a relative strength rating, not a literal title probability.

Module 03 — Hidden Value Index

Whose tournament contribution outruns their market rank.

Tournament Contribution Score versus current Transfermarkt valuation, position-normalised. Tiered as Strong Riser, Riser, or Watch. The percentile-points delta is a directional signal — not a quoted valuation or transfer-fee forecast.

Position
Age
30 of 30 surfaced · 525 eligible
José VozinhaGK · 40 · CPV
GD Chaves
Strong Riser
Current market value
50K
+89 percentile pts above market rank

Plays for GD Chaves. against above-average opposition.

Farrukh SayfievDEF · 35 · UZB
FK Neftchi Farg'ona
Strong Riser
Current market value
225K
+85 percentile pts above market rank

Plays for FK Neftchi Farg'ona. against above-average opposition.

Timothy John PayneDEF · 32 · NZL
Wellington Phoenix FC
Strong Riser
Current market value
350K
+82 percentile pts above market rank

Plays for Wellington Phoenix FC. 1 g+a in 2 matches' worth of minutes. against above-average opposition.

Behruzjon KarimovDEF · 18 · UZB
Surkhon FK
Strong Riser
Current market value
350K
+81 percentile pts above market rank

Plays for Surkhon FK. against above-average opposition.

Dhia Jirjis FransDEF · 32 · IRQ
Persib Bandung
Strong Riser
Current market value
325K
+79 percentile pts above market rank

Plays for Persib Bandung. against above-average opposition.

Eloy Victor RoomGK · 37 · CUW
Miami FC
Strong Riser
Current market value
200K
+78 percentile pts above market rank

Plays for Miami FC. against above-average opposition.

Mahmoud Ahmed Nizar AlrashdanMID · 27 · JOR
Qatar SC
Strong Riser
Current market value
600K
+76 percentile pts above market rank

Plays for Qatar SC. 1 g+a in 2 matches' worth of minutes. against above-average opposition.

Umarbek EshmurodovDEF · 33 · UZB
Nasaf Qarshi FC
Strong Riser
Current market value
450K
+76 percentile pts above market rank

Plays for Nasaf Qarshi FC. against above-average opposition.

Johny PlacideGK · 38 · HAI
SC Bastia
Strong Riser
Current market value
250K
+76 percentile pts above market rank

Plays for SC Bastia. against above-average opposition.

Fouad Aboud Amir AlammariMID · 28 · IRQ
KS Cracovia
Strong Riser
Current market value
1.0M
+73 percentile pts above market rank

Plays for KS Cracovia. 1 g+a in 2 matches' worth of minutes. against above-average opposition.

Ibrahim Mahmoud AbunadaGK · 26 · QAT
Al Rayyan SC
Strong Riser
Current market value
400K
+73 percentile pts above market rank

Plays for Al Rayyan SC. 1 g+a in 3 matches' worth of minutes. against above-average opposition.

Hasan Maknazi Ahmed MaknaziDEF · 24 · IRQ
Al Karma SC
Strong Riser
Current market value
450K
+72 percentile pts above market rank

Plays for Al Karma SC. against above-average opposition.

Talib Raheem Fahad TalibGK · 31 · IRQ
Al Talaba SC
Strong Riser
Current market value
375K
+71 percentile pts above market rank

Plays for Al Talaba SC. against above-average opposition.

Abdoulaye SeckDEF · 34 · SEN
Maccabi Haifa FC
Strong Riser
Current market value
400K
+71 percentile pts above market rank

Plays for Maccabi Haifa FC. against above-average opposition.

Lionel Nzau MpasiGK · 31 · COD
Le Havre AC
Strong Riser
Current market value
400K
+69 percentile pts above market rank

Plays for Le Havre AC. against above-average opposition.

Hashim Rahman Akam HashimDEF · 27 · IRQ
Al Zawra'a SC
Strong Riser
Current market value
700K
+69 percentile pts above market rank

Plays for Al Zawra'a SC. against above-average opposition.

Mohamed Zaky Mostafa ZicoMID · 29 · EGY
Pyramids FC
Strong Riser
Current market value
1.5M
+68 percentile pts above market rank

Plays for Pyramids FC. 2 g+a in 2 matches' worth of minutes. against above-average opposition.

Carl Fred SainteMID · 23 · HAI
El Paso Locomotive FC
Strong Riser
Current market value
125K
+67 percentile pts above market rank

Plays for El Paso Locomotive FC. against above-average opposition.

Dominique Celidor SimonMID · 25 · HAI
FC Tatran Pre š ov
Strong Riser
Current market value
125K
+67 percentile pts above market rank

Plays for FC Tatran Pre š ov. against above-average opposition.

Ahmed Nadhir BenboualiFWD · 26 · ALG
Györi ETO FC
Strong Riser
Current market value
600K
+66 percentile pts above market rank

Plays for Györi ETO FC. 1 g+a in 2 matches' worth of minutes. against above-average opposition.

Dennis HadzikadunicDEF · 27 · BIH
UC Sampdoria
Strong Riser
Current market value
1.2M
+65 percentile pts above market rank

Plays for UC Sampdoria. 1 g+a in 3 matches' worth of minutes. against above-average opposition.

Hany Gamal Mohamed HanyDEF · 30 · EGY
Al Ahly FC
Strong Riser
Current market value
1.5M
+64 percentile pts above market rank

Plays for Al Ahly FC. 2 g+a in 3 matches' worth of minutes. against above-average opposition.

Roderick Alonso MillerDEF · 34 · PAN
Turan Tovuz
Strong Riser
Current market value
200K
+64 percentile pts above market rank

Plays for Turan Tovuz. against above-average opposition.

Elijah Henry JustMID · 26 · NZL
Motherwell FC
Strong Riser
Current market value
2.5M
+63 percentile pts above market rank

Plays for Motherwell FC. 3 g+a in 3 matches' worth of minutes. against above-average opposition.

Gaël Roméo KakutaFWD · 35 · COD
AEL FC
Strong Riser
Current market value
250K
+62 percentile pts above market rank

Plays for AEL FC. against above-average opposition.

Melvin Feycal MastilGK · 26 · ALG
FC Stade Nyonnais
Strong Riser
Current market value
300K
+62 percentile pts above market rank

Plays for FC Stade Nyonnais. against above-average opposition.

Ismael Khaleel Zaid IsmaelMID · 24 · IRQ
Al Talaba SC
Strong Riser
Current market value
500K
+62 percentile pts above market rank

Plays for Al Talaba SC. against above-average opposition.

Sherzod NasrullaevDEF · 27 · UZB
Pakhtakor Tashkent FK
Strong Riser
Current market value
1.2M
+61 percentile pts above market rank

Plays for Pakhtakor Tashkent FK. against above-average opposition.

Wali Faeq Youssef AmynMID · 22 · IRQ
AEK Larnaca FC
Strong Riser
Current market value
550K
+60 percentile pts above market rank

Plays for AEK Larnaca FC. against above-average opposition.

Ruslanbek JiyanovMID · 25 · UZB
PFC Navbahor Namangan
Strong Riser
Current market value
800K
+60 percentile pts above market rank

Plays for PFC Navbahor Namangan. against above-average opposition.

This is what Forma's Recruitment module does year-round.
Across 30,000+ players. Not just the ones playing in a World Cup.
See how →

Players need 180+ tournament minutes to surface. The percentile-points delta compares a player's tournament contribution rank against their market-value rank within their position group. It is a directional signal, not a quoted valuation or transfer-fee forecast.

Module 04 — Talent Pipeline

Where the World Cup is actually trained.

Club→country minutes flow. Some nations run on the Premier League. Some on Liga MX. A few on leagues you wouldn't expect.

Callout #1
Other supplies the most WC26 minutes
48,840 minutes across the tournament so far.
Callout #2
Qatar's squad runs heaviest through Other
2,490 minutes on the pitch are wearing Other club shirts day-to-day.
Callout #3
England keeps it at home
81% of their WC26 minutes come from clubs in their own top flight.
Where the World Cup is actually trained — nation → club league
MEXCANBIHUSANEDJPNSUIBRAMARPARGERSWEBELCZEQATAUSTURCIVTUNIRNESPRSAKORHAIEGYCPVKSACUWNZLURUSCOECUPORCROSENFRAJORCOLNORARGALGAUTENGIRQGHAPANCODUZBOtherPremier LeagueBundesligaLigue 1Serie ALa LigaMajor League SoccerBrasileirão Série AEredivisieEFL ChampionshipSaudi Pro LeagueLiga MXPrimeira LigaSouth African PSLUEFACONMEBOLCONCACAFAFCCAFOFCOther

Clubs supplying the most WC26 minutes

  1. 1
    Arsenal FC
    Premier League · 13 players
    2,400 min
  2. 2
    FC Bayern München
    Bundesliga · 12 players
    2,220 min
  3. 3
    Paris Saint-Germain
    Ligue 1 · 13 players
    2,160 min
  4. 4
    FC Barcelona
    La Liga · 12 players
    2,130 min
  5. 5
    Manchester City FC
    Premier League · 13 players
    2,070 min
  6. 6
    Real Madrid C. F.
    La Liga · 9 players
    1,950 min
  7. 7
    Liverpool FC
    Premier League · 8 players
    1,920 min
  8. 8
    Al Hilal SC
    Saudi Pro League · 9 players
    1,650 min
  9. 9
    Mamelodi Sundowns FC
    South African PSL · 6 players
    1,620 min
  10. 10
    Borussia Dortmund
    Bundesliga · 7 players
    1,410 min
  11. 11
    Lille OSC
    Ligue 1 · 8 players
    1,380 min
  12. 12
    SK Slavia Praha
    Other · 5 players
    1,350 min

Domestic share — squads who keep it at home

  1. ENGEngland
    81%
  2. RSASouth Africa
    73%
  3. GERGermany
    70%
  4. MEXMexico
    56%
  5. EGYEgypt
    44%
  6. KSASaudi Arabia
    44%
  7. ESPSpain
    39%
  8. BRABrazil
    23%
  9. FRAFrance
    20%
  10. AUTAustria
    18%
  11. PORPortugal
    17%
  12. SCOScotland
    15%

Minutes — not players — is the unit, because minutes is what wins matches. Club→league mapping covers 72% of squad-minutes; the rest sits in `Other`. Domestic-share treats a nation's own top-flight league as domestic via an exact league→nation map; partial substring matches are intentionally not used (they conflate North/South Korea and the two Congos). Club→league mapping coverage: 72%.

Module 05 — Daily Spotter

One card. Every match day. Through the final.

One number. One player. One tactical observation. Auto-surfaced from the data — nothing invented.

Forma Spotter · Sat 27 Jun
4 matches this day
Number of the day
+1.8 goals vs xG
Belgium overperformed their xG
Scored 5, expected 3.25 (vs New Zealand).
Player of the day
Leandro Trossard
Belgium
2G · 0A · 30 min
Tactical note
2 wins today came from "Vertical transition" teams.
Earlier days
Fri 26 Jun · 4 matches
+0.9 goals vs xG
Netherlands overperformed their xG
Nicolas Pepe · 2G 0A
Thu 25 Jun · 4 matches
-0.6 goals vs xG
Paraguay underperformed their xG
José Vinicius · 2G 0A
Wed 24 Jun · 4 matches
+1.7 goals vs xG
Mexico overperformed their xG
Johan Kula Manzambi · 1G 1A
Tue 23 Jun · 4 matches
62%
Portugal dominated possession
Ronaldo Cristiano Ronaldo · 2G 0A
Mon 22 Jun · 4 matches
-0.6 goals vs xG
Austria underperformed their xG
Kylian Mbappe · 2G 0A
Sun 21 Jun · 4 matches
+0.9 goals vs xG
Cabo Verde overperformed their xG
Mikel Oyarzabal · 2G 1A
Sat 20 Jun · 4 matches
63%
Ecuador dominated possession
Cody Mathès Gakpo · 2G 1A
Fri 19 Jun · 4 matches
-1.0 goals vs xG
Scotland underperformed their xG
Matheus Matheus Cunha · 2G 0A
Thu 18 Jun · 4 matches
+2.2 goals vs xG
Canada overperformed their xG
Jonathan Christian David · 3G 0A
Wed 17 Jun · 4 matches
68%
Portugal dominated possession
Harry Edward Kane · 2G 0A
Tue 16 Jun · 4 matches
-0.4 goals vs xG
Algeria underperformed their xG
Lionel Andrés Messi · 3G 0A
Mon 15 Jun · 4 matches
+0.8 goals vs xG
New Zealand overperformed their xG
Elijah Henry Just · 2G 0A
Sun 14 Jun · 4 matches
63%
Germany dominated possession
Deniz Undav · 1G 2A
Sat 13 Jun · 4 matches
-1.3 goals vs xG
Haiti underperformed their xG
Breel Donald Embolo · 1G 0A
Fri 12 Jun · 2 matches
+1.2 goals vs xG
USA overperformed their xG
Folarin Jolaoluwa Balogun · 2G 0A
Thu 11 Jun · 2 matches
57%
Mexico dominated possession
Inbeom Hwang · 1G 1A
Methodology · Footnote

What this hub is — and isn't.

Built from the open WC26 Kaggle dataset. Aggregated team stats per match, no event-level coordinates. Group-stage sample is 3 matches per team — every model output below labels its sample size and is presented as directional, not as a quoted projection.

Identity Map: 6-axis style vector (possession share, shot rate per ball-time as a verticality proxy, foul rate as a pressing proxy, set-piece reliance via corners-per-shot, defensive load via saves per match, chaos via opponent-xG variance), z-scored across the 48 nations, projected to 2D via PCA, clustered with k-means k=6. The dataset has no event coordinates — shot rate and foul rate are imperfect proxies and will move with game state and referee profile. With ~3 group matches per team and 6 features, archetype boundaries are noise-prone and will sharpen through the knockouts. A tournament fingerprint, not a season fingerprint.
Simulator: Per-team xG-for and xG-against from completed group matches, blended with an elo-derived prior using confidence weighting (matches played / 3). The elo→xG mapping (1.0 + (elo−1500)/600, clamped 0.5–2.2) is an uncalibrated heuristic, not fit to historical data. Match outcomes are Poisson-sampled with team-strength-adjusted expected goals. Extra time uses a 30% scoring rate; penalties resolve with a coin flip. Bracket pairings are seeded by qualification order (R32[0] vs R32[1], etc.) — they are NOT FIFA's official draw, so a given team's strength rating does not correspond to its real path. Model ignores injuries, suspensions, travel, fatigue, home-crowd effects, goalkeeper identity, and the actual draw. Treat outputs as a relative strength rating, not a literal title probability.
Hidden Value Index: Tournament Contribution Score blends goals+assists per 90 (40%), starts share (20%), share of team minutes (20%), and average opponent elo (20%), then position-normalised to a percentile within position group. Value Pressure = TCS percentile − market-value percentile within position. The page leads with current Transfermarkt value plus the percentile-points delta as a directional signal — not a quoted valuation or transfer-fee forecast. Players need 180+ tournament minutes to be eligible. The model says nothing about wages, contract length, buy-out clauses, or transfer feasibility. With only 3 group matches, opponent-strength weighting is largely a group-difficulty constant; the signal sharpens through the knockouts.
Talent Pipeline: Minutes — not players — is the unit, because minutes is what wins matches. Club→league mapping covers 72% of squad-minutes; the rest sits in `Other`. Domestic-share treats a nation's own top-flight league as domestic via an exact league→nation map; partial substring matches are intentionally not used (they conflate North/South Korea and the two Congos).
Daily Spotter: every claim is auto-surfaced from observable extremes across the day's matches. No human narrative is layered on top at v1.

Sources. Built from the Kaggle: mominullptr/fifa-world-cup-2026-dataset. Last refresh: 2026-06-27. Snapshot regenerated daily on every site rebuild.

Every model output on this page is presented as directional. None of it is a quoted projection, a recommendation, or a substitute for the kind of work Forma does for our clients — which is built on far richer data and analyst review.

Forma Football Analytics

We build analytics that survive a real club's questions.

This hub is a public snapshot of how we think. Our real work — for clubs, on full-season data, with analyst review — goes further than any open dataset can.