WC 2026 · Forecasting Oxford Football Forecasting
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Uruguay

CONMEBOL Group H
1.4% Champion probability ±0.04 MC-SE
Coach
Marcelo Bielsa foreign · Argentine
Elo (model)
1,892 world 14th
Squad value
€589M
Power → Reality
17th 17th −0.02 pp · neutral draw

§ 01

The forecast

How far Uruguay goes — the survival probability at each stage of the bracket, from the tournament Monte-Carlo simulation. Each bar carries its ±1.96·MC-SE interval.

Fig. D1 Fixture-aware · 100k sims

Uruguay — stage progression

Round of 32: 89.61% (95% MC 89.42%–89.80%; MC-SE ±0.10 pts) Round of 32 reach 89.6% ±0.10 Round of 16: 37.22% (95% MC 36.92%–37.52%; MC-SE ±0.15 pts) Round of 16 reach 37.2% ±0.15 Quarter-final: 19.62% (95% MC 19.37%–19.87%; MC-SE ±0.13 pts) Quarter-final reach 19.6% ±0.13 Semi-final: 9.41% (95% MC 9.23%–9.59%; MC-SE ±0.09 pts) Semi-final reach 9.4% ±0.09 Final: 3.82% (95% MC 3.70%–3.94%; MC-SE ±0.06 pts) Final reach 3.8% ±0.06 Champion: 1.37% (95% MC 1.29%–1.44%; MC-SE ±0.04 pts) Champion reach 1.4% ±0.04

On the central forecast, Uruguay more likely than not reaches the Round of 32 (90%). Champion probability is 1.4% ± 0.04 pts.

Source · Oxford Football Forecasting model

§ 02

The group & the path

Group H advancement odds, the bracket half Uruguay sits in, and the earliest round they could meet each leading side.

Group H Confed Advance (top 2) Reach R32
1🇪🇸SpainUEFA71.4%99.2%
2🇺🇾UruguayCONMEBOL37.2%89.6%
3🇸🇦Saudi ArabiaAFC6.5%35.1%
4🇨🇻Cabo VerdeCAF4.5%27.3%

Source · Oxford Football Forecasting model

Bracket position Half 0 · Quadrant 1

Earliest possible meetings

No collision rows recorded for this team.

Collision = the earliest round the bracket wiring could pit Uruguay against that side. Full bracket & collision matrix →

§ 03

Match by match

Uruguay's three group fixtures, each with the predicted win / draw / loss split and the single most-likely scoreline. Probabilities are this team's own orientation; they sum to 100%.

Match 13 · 2026-06-15 · Miami Stadium away
Uruguay Saudi Arabia
63.2% win 25.5% draw 11.3% loss
Most likely 1–0 (17.7%) λ 1.63–0.55 Over 2.5 37% · BTTS 35%
Match 37 · 2026-06-21 · Miami Stadium home
Uruguay Cabo Verde
65.9% win 24.1% draw 10% loss
Most likely 1–0 (17.5%) λ 1.72–0.52 Over 2.5 39% · BTTS 34%
Match 66 · 2026-06-27 · Guadalajara Stadium home
Uruguay Spain
16.9% win 27.8% draw 55.3% loss
Most likely 0–1 (15.2%) λ 0.72–1.51 Over 2.5 39% · BTTS 41%
How a match forecast is built

Each pairing is scored by the ensemble (Dixon-Coles bivariate-Poisson, the Bayesian hierarchical model and the global LightGBM-Poisson, log-pooled), producing an 11×11 scoreline grid that is marginalised into win/draw/loss, expected goals (λ), over/under 2.5 and both-teams-to-score. These are the same distributions the tournament simulator consumes, oriented here to Uruguay. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.

§ 04

Strength profile

Where Uruguay stands against the median of the 48-team field, metric by metric. The dot is Uruguay; the dashed line is the field median (1.0×).

Fig. D2 Relative to the 48-team median

Uruguay vs the field

Elo rating: 1892 vs field median 1780 (1.06× the field) Elo rating 1892 med 1780 Recent NT form: 1.47 ppg vs field median 1.87 ppg (0.79× the field) Recent NT form 1.47 ppg med 1.87 ppg Squad value: €589M vs field median €286M (2.06× the field) Squad value €589M med €286M Squad form (global): 0.287 vs field median 0.211 (1.36× the field) Squad form (global) 0.287 med 0.211 Fitness readiness: 0.740 vs field median 0.707 (1.05× the field) Fitness readiness 0.740 med 0.707 Familiarity / chemistry: 0.015 vs field median 0.015 (1.00× the field) Familiarity / chemistry 0.015 med 0.015 Experience (mean caps): 24 vs field median 25 (0.98× the field) Experience (mean caps) 24 med 25

Read each row as a multiple of the field median: dots to the right of the dashed line are above-field, to the left below. Raw values are labelled on the right so the comparison is transparent.

Source · Oxford Football Forecasting model

§ 05

History vs squad

The decoupling residual g — whether the squad’s market value sits above, or below, what the team’s record predicts.

Fig. D3 Bayesian projection residual g

Uruguay on the decoupling axis

aligned (0) ← record > squad price squad valued > record →

g = −0.15 ± 0.05: the record outruns the squad price — the team has achieved more than its comparatively modest squad value would predict.

Source · Oxford Football Forecasting model
What g means — and its limits

g is the residual from regressing a team’s current squad market value on its history-based strength in the Bayesian hierarchical model. Positive g means the squad is valued above what the team’s record predicts; negative means the record outruns the squad’s price (the side achieves more than its market value implies). Regressed on out-of-sample success the slope is positive — squad-rich sides go a touch further — but not statistically significant at n = 3 tournaments, so treat a single team’s g as a descriptive read, not a hard prediction. The full decoupling essay →

§ 06

The squad

All 26 selected players — club, league (with relative strength), club-season minutes and goals, caps. Sort any column.

26players
24.0mean age
24mean caps
46%in a top-5 league
22distinct clubs
3largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Sergio RochetGKInternacionalSerie A +1.03z1,3050350
2José Giménez (captain)DFAtlético MadridLa Liga +2.13z1,5371998
3Sebastián CáceresDFAméricaLiga MX +0.22z3,0261240
4Ronald AraújoDFBarcelonaLa Liga +2.13z1,6414271
5Manuel UgarteMFManchester UnitedPremier League +2.21z1,4690361
6Rodrigo BentancurMFTottenham HotspurPremier League +2.21z2,6391743
7Nicolás de la CruzMFFlamengoSupercopa do Brasil +1.03z3,6006345
8Federico ValverdeMFReal MadridLa Liga +2.13z4,1639739
9Darwin NúñezFWAl-HilalPro League −0.86z1,60193813
10Giorgian de ArrascaetaMFFlamengoSerie A +1.03z6,032316013
11Facundo PellistriFWPanathinaikosSuper League 1 +0.03z7950392
12Santiago MeleGKMonterreyLiga MX +0.22z1,530080
13Guillermo VarelaDFFlamengoSerie A +1.03z3,3752280
14Agustín CanobbioMFFluminenseSerie A +1.03z3,75411151
15Emiliano MartínezMFPalmeirasSerie A +1.03z2,8841100
16Mathías OliveraDFNapoliSerie A +1.70z1,8270352
17Matías ViñaDFRiver Plate—— no club data—431
18Brian RodríguezFWAméricaLiga MX +0.22z3,02516324
19Rodrigo AguirreFWUANLLiga MX +0.22z8941103
20Maximiliano AraújoMFSporting CPPrimeira Liga +1.14z3,5047283
21Federico ViñasFWOviedoLa Liga +2.13z2,5079112
22Joaquín PiquerezMFPalmeirasSerie A +1.03z4,3434190
23Fernando MusleraGKEstudiantes—— no club data—1340
24Santiago BuenoDFWolverhampton Wanderers—— no club data—80
25Juan Manuel SanabriaMFReal Salt Lake—— no club data—51
26Rodrigo ZalazarMFBragaPrimeira Liga +1.14z2,6251882

Source · Official squad announcements · API-Football (global club coverage). 4 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 85%.

§ 07

Tournament context

The host-nation environment this team meets — diaspora support, climate and altitude exposure at their venues, language familiarity.

Diaspora in the hosts

60,779

18.0 per 1,000 of home population

Host-language familiarity

Shared

primary language Spanish · spoken in a host

Climate adaptation gap

+2.8°C

home-vs-venue heat differential

Venue extremes

42°C

peak heat index · altitude up to 1,671 m

Travel

3h

max time-zone shift · nearest venue 7,221 km

Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records

§ 08

Elo trajectory

Uruguay's long-run strength against the qualified-field median, 1950–2026.

Fig. D4 eloratings.net method · year-end values

Uruguay — Elo since 1950

1975 world #14
Uruguay Qualified-field median

Uruguay ends the series at 1975 Elo, the world’s 14th-ranked side — above the qualified-field median.

Source · eloratings.net
Which Elo is this?

This line is the public eloratings.net series (year-end ratings), which terminates exactly at the current rating and world rank shown on the marker. It is a different number from the Elo shown in the header band (a panel-normalised rating used inside the forecast); the two are ~0.99 correlated but on different scales. We keep them distinct rather than blend them.

§ 09

Data coverage

85% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
85% Fitness-readiness coverage Where below 100%, part of the fitness signal is imputed by the de-biasing layer.
n = 3 Out-of-sample tournaments The model is validated on three held-out World Cups; it matches the market, it does not beat it.

Validated on n=3 held-out tournaments; coverage below 1.0 means part of this squad's club-form/fitness is imputed (the global de-biasing layer). For Uruguay, 4 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →