WC 2026 · Forecasting Oxford Football Forecasting
🇧🇪

Belgium

UEFA Group G
2.5% Champion probability ±0.05 MC-SE
Coach
Rudi Garcia foreign · French
Elo (model)
1,893 world 18th
Squad value
€613M
Power → Reality
11th 10th +0.41 pp · soft draw

Fig. D1 Fixture-aware · 100k sims

Belgium — stage progression

Round of 32: 93.92% (95% MC 93.77%–94.06%; MC-SE ±0.08 pts) Round of 32 reach 93.9% ±0.08 Round of 16: 61.90% (95% MC 61.60%–62.20%; MC-SE ±0.15 pts) Round of 16 reach 61.9% ±0.15 Quarter-final: 33.98% (95% MC 33.69%–34.28%; MC-SE ±0.15 pts) Quarter-final reach 34.0% ±0.15 Semi-final: 13.82% (95% MC 13.61%–14.03%; MC-SE ±0.11 pts) Semi-final reach 13.8% ±0.11 Final: 6.30% (95% MC 6.15%–6.45%; MC-SE ±0.08 pts) Final reach 6.3% ±0.08 Champion: 2.48% (95% MC 2.38%–2.58%; MC-SE ±0.05 pts) Champion reach 2.5% ±0.05

On the central forecast, Belgium more likely than not reaches the Round of 16 (62%). Champion probability is 2.5% ± 0.05 pts.

Source · Oxford Football Forecasting model
Group G Confed Advance (top 2) Reach R32
1🇧🇪BelgiumUEFA61.9%93.9%
2🇮🇷IR IranAFC39.5%79.4%
3🇪🇬EgyptCAF28.1%67.6%
4🇳🇿New ZealandOFC6.0%27.6%

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 Belgium against that side. Full bracket & collision matrix →

Match 16 · 2026-06-15 · Seattle Stadium home
Belgium Egypt
52.9% win 28.5% draw 18.6% loss
Most likely 1–0 (14.8%) λ 1.47–0.77 Over 2.5 39% · BTTS 42%
Match 39 · 2026-06-21 · Los Angeles Stadium home
Belgium IR Iran
48.2% win 28.6% draw 23.2% loss
Most likely 1–1 (13.5%) λ 1.45–0.93 Over 2.5 42% · BTTS 47%
Match 64 · 2026-06-27 · BC Place Vancouver away
Belgium New Zealand
75.5% win 17.4% draw 7.1% loss
Most likely 2–0 (15.1%) λ 2.27–0.57 Over 2.5 54% · BTTS 39%
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 Belgium. 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

Belgium vs the field

Elo rating: 1893 vs field median 1780 (1.06× the field) Elo rating 1893 med 1780 Recent NT form: 2.07 ppg vs field median 1.87 ppg (1.11× the field) Recent NT form 2.07 ppg med 1.87 ppg Squad value: €613M vs field median €286M (2.15× the field) Squad value €613M med €286M Squad form (global): 0.173 vs field median 0.211 (0.82× the field) Squad form (global) 0.173 med 0.211 Fitness readiness: 0.718 vs field median 0.707 (1.02× the field) Fitness readiness 0.718 med 0.707 Familiarity / chemistry: 0.031 vs field median 0.015 (2.00× the field) Familiarity / chemistry 0.031 med 0.015 Experience (mean caps): 37 vs field median 25 (1.50× the field) Experience (mean caps) 37 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

Belgium on the decoupling axis

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

g = +0.29 ± 0.06: the squad is valued above its record — the transfer market rates this side above what its results have earned.

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
25.5mean age
37mean caps
77%in a top-5 league
18distinct clubs
3largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Thibaut CourtoisGKReal MadridLa Liga +2.13z4,54501090
2Zeno DebastDFSporting CPPrimeira Liga +1.14z1,4510261
3Arthur TheateDFEintracht FrankfurtBundesliga +1.84z2,9831331
4Brandon MecheleDFClub BruggeJupiler Pro League −0.07z4,700791
5Maxim De CuyperDFBrighton & Hove AlbionPremier League +2.21z1,5102194
6Axel WitselMFGironaLa Liga +2.13z2,533113812
7Kevin De BruyneMFNapoliSerie A +1.70z1,360511937
8Youri Tielemans (captain)MFAston VillaPremier League +2.21z3,02928513
9Romelu LukakuFWNapoliSerie A +1.70z74112690
10Leandro TrossardFWArsenalPremier League +2.21z2,976105112
11Jérémy DokuFWManchester CityPremier League +2.21z3,46412437
12Senne LammensGKManchester UnitedPremier League +2.21z2,970020
13Mike PendersGKStrasbourgLigue 1 +1.70z4,639000
14Dodi LukébakioFWBenficaPrimeira Liga +1.14z1,0870306
15Thomas MeunierDFLilleLigue 1 +1.70z2,73428010
16Koni De WinterDFMilanno club data80
17Charles De KetelaereFWAtalantaSerie A +1.70z3,0365306
18Joaquin SeysDFClub BruggeJupiler Pro League −0.07z5,671650
19Diego MoreiraMFStrasbourgLigue 1 +1.70z5,509730
20Hans VanakenMFClub BruggeJupiler Pro League −0.07z8,20725347
21Timothy CastagneDFFulhamPremier League +2.21z2,4010632
22Alexis SaelemaekersMFMilanno club data242
23Nicolas RaskinMFRangersPremiership −0.28z5,1616132
24Amadou OnanaMFAston VillaPremier League +2.21z2,7232291
25Nathan NgoyDFLilleLigue 1 +1.70z3,476340
26Matias Fernandez-PardoFWLilleLigue 1 +1.70z3,106820

Source · Official squad announcements · API-Football (global club coverage). 2 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 92%.

Diaspora in the hosts

52,097

4.0 per 1,000 of home population

Host-language familiarity

Shared

primary language German · spoken in a host

Climate adaptation gap

−2.7°C

home-vs-venue heat differential

Venue extremes

29°C

peak heat index · altitude up to 45 m

Travel

8h

max time-zone shift · nearest venue 5,614 km

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

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

Belgium — Elo since 1950

1956 world #18
Belgium Qualified-field median

Belgium ends the series at 1956 Elo, the world’s 18th-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.

92% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
92% 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 Belgium, 2 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →