WC 2026 · Forecasting Oxford Football Forecasting
🇪🇸

Spain

UEFA Group H
16.0% Champion probability ±0.12 MC-SE
Coach
Luis de la Fuente
Elo (model)
2,155 world 1st
Squad value
€1580M
Power → Reality
2nd 2nd +0.07 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Spain — stage progression

Round of 32: 99.19% (95% MC 99.14%–99.25%; MC-SE ±0.03 pts) Round of 32 reach 99.2% ±0.03 Round of 16: 71.43% (95% MC 71.15%–71.71%; MC-SE ±0.14 pts) Round of 16 reach 71.4% ±0.14 Quarter-final: 50.44% (95% MC 50.13%–50.75%; MC-SE ±0.16 pts) Quarter-final reach 50.4% ±0.16 Semi-final: 38.60% (95% MC 38.30%–38.90%; MC-SE ±0.15 pts) Semi-final reach 38.6% ±0.15 Final: 25.59% (95% MC 25.32%–25.86%; MC-SE ±0.14 pts) Final reach 25.6% ±0.14 Champion: 15.95% (95% MC 15.73%–16.18%; MC-SE ±0.12 pts) Champion reach 16.0% ±0.12

On the central forecast, Spain more likely than not reaches the Quarter-final (50%). Champion probability is 16.0% ± 0.12 pts.

Source · Oxford Football Forecasting model
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

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

Match 14 · 2026-06-15 · Atlanta Stadium home
Spain Cabo Verde
85.4% win 11.5% draw 3.1% loss
Most likely 2–0 (16.6%) λ 2.70–0.39 Over 2.5 60% · BTTS 31%
Match 38 · 2026-06-21 · Atlanta Stadium home
Spain Saudi Arabia
85.7% win 11.2% draw 3.1% loss
Most likely 2–0 (15.8%) λ 2.78–0.42 Over 2.5 62% · BTTS 32%
Match 66 · 2026-06-27 · Guadalajara Stadium away
Spain Uruguay
55.3% win 27.8% draw 16.9% loss
Most likely 1–0 (15.2%) λ 1.51–0.72 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 Spain. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.

Fig. D2 Relative to the 48-team median

Spain vs the field

Elo rating: 2155 vs field median 1780 (1.21× the field) Elo rating 2155 med 1780 Recent NT form: 2.20 ppg vs field median 1.87 ppg (1.18× the field) Recent NT form 2.20 ppg med 1.87 ppg Squad value: €1580M vs field median €286M (5.53× the field) Squad value €1580M med €286M Squad form (global): 0.309 vs field median 0.211 (1.46× the field) Squad form (global) 0.309 med 0.211 Fitness readiness: 0.841 vs field median 0.707 (1.19× the field) Fitness readiness 0.841 med 0.707 Familiarity / chemistry: 0.114 vs field median 0.015 (7.39× the field) Familiarity / chemistry 0.114 med 0.015 Experience (mean caps): 25 vs field median 25 (1.03× the field) Experience (mean caps) 25 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

Fig. D3 Bayesian projection residual g

Spain on the decoupling axis

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

g = −0.18 ± 0.11: 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 →

26players
25.4mean age
25mean caps
89%in a top-5 league
13distinct clubs
8largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1David RayaGKArsenalPremier League +2.21z4,6200120
2Marc PubillDFAtlético MadridLa Liga +2.13z2,782110
3Álex GrimaldoDFBayer LeverkusenBundesliga +1.84z4,12815130
4Eric GarcíaDFBarcelonaLa Liga +2.13z3,9531200
5Marcos LlorenteDFAtlético MadridLa Liga +2.13z3,9666230
6Mikel MerinoMFArsenalPremier League +2.21z2,09664210
7Ferran TorresFWBarcelonaLa Liga +2.13z2,729215624
8Fabián RuizMFParis Saint-GermainLigue 1 +1.70z2,4464416
9GaviMFBarcelonaLa Liga +2.13z8321295
10Dani OlmoFWBarcelonaLa Liga +2.13z2,85384912
11Yéremy PinoFWCrystal PalacePremier League +2.21z3,1885224
12Pedro PorroDFTottenham HotspurPremier League +2.21z3,9422170
13Joan GarciaGKBarcelonaLa Liga +2.13z4,139020
14Aymeric LaporteDFAthletic BilbaoLa Liga +2.13z2,4340452
15Álex BaenaMFAtlético MadridLa Liga +2.13z4,9209162
16Rodri (captain)MFManchester CityPremier League +2.21z2,3272614
17Nico WilliamsFWAthletic BilbaoLa Liga +2.13z2,1536306
18Martín ZubimendiMFArsenalPremier League +2.21z4,4786253
19Lamine YamalFWBarcelonaLa Liga +2.13z3,82826256
20PedriMFBarcelonaLa Liga +2.13z3,1882405
21Mikel OyarzabalFWReal SociedadLa Liga +2.13z3,181185224
22Pau CubarsíDFBarcelonaLa Liga +2.13z4,0541110
23Unai SimónGKAthletic BilbaoLa Liga +2.13z4,3200570
24Marc CucurellaDFChelseaPremier League +2.21z5,0241231
25Víctor MuñozFWOsasunaLa Liga +2.13z2,764721
26Borja IglesiasFWCelta VigoLa Liga +2.13z2,7171870

Source · Official squad announcements · API-Football (global club coverage). Every player’s club season matched in API-Football.

Diaspora in the hosts

138,885

3.0 per 1,000 of home population

Host-language familiarity

Shared

primary language Spanish · spoken in a host

Climate adaptation gap

−0.9°C

home-vs-venue heat differential

Venue extremes

37°C

peak heat index · altitude up to 1,671 m

Travel

7h

max time-zone shift · nearest venue 5,500 km

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

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

Spain — Elo since 1950

2216 world #1
Spain Qualified-field median

Spain ends the series at 2216 Elo, the world’s 1st-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.

100% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
100% 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). Full validation, calibration & conformal coverage →