WC 2026 · Forecasting Oxford Football Forecasting
🇳🇴

Norway

UEFA Group I
2.4% Champion probability ±0.05 MC-SE
Coach
Ståle Solbakken home · Norwegian
Elo (model)
1,914 world 15th
Squad value
€580M
Power → Reality
10th 11th −0.11 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Norway — stage progression

Round of 32: 87.72% (95% MC 87.52%–87.93%; MC-SE ±0.10 pts) Round of 32 reach 87.7% ±0.10 Round of 16: 50.80% (95% MC 50.49%–51.10%; MC-SE ±0.16 pts) Round of 16 reach 50.8% ±0.16 Quarter-final: 26.42% (95% MC 26.15%–26.69%; MC-SE ±0.14 pts) Quarter-final reach 26.4% ±0.14 Semi-final: 13.18% (95% MC 12.97%–13.39%; MC-SE ±0.11 pts) Semi-final reach 13.2% ±0.11 Final: 5.81% (95% MC 5.66%–5.95%; MC-SE ±0.07 pts) Final reach 5.8% ±0.07 Champion: 2.42% (95% MC 2.32%–2.51%; MC-SE ±0.05 pts) Champion reach 2.4% ±0.05

On the central forecast, Norway more likely than not reaches the Round of 16 (51%). Champion probability is 2.4% ± 0.05 pts.

Source · Oxford Football Forecasting model
Group I Confed Advance (top 2) Reach R32
1🇫🇷FranceUEFA68.1%95.2%
2🇳🇴NorwayUEFA50.8%87.7%
3🇸🇳SenegalCAF30.1%69.3%
4🇮🇶IraqAFC3.6%17.6%

Source · Oxford Football Forecasting model

Bracket position Half 0 · Quadrant 0

Earliest possible meetings

No collision rows recorded for this team.

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

Match 18 · 2026-06-16 · Boston Stadium away
Norway Iraq
71.9% win 20% draw 8.1% loss
Most likely 2–0 (15.7%) λ 2.03–0.54 Over 2.5 47% · BTTS 37%
Match 41 · 2026-06-23 · New York/New Jersey Stadium home
Norway Senegal
45.7% win 29.8% draw 24.5% loss
Most likely 1–1 (13.8%) λ 1.35–0.92 Over 2.5 40% · BTTS 46%
Match 61 · 2026-06-26 · Boston Stadium home
Norway France
24.5% win 29.6% draw 45.9% loss
Most likely 1–1 (13.8%) λ 0.93–1.37 Over 2.5 40% · BTTS 46%
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 Norway. 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

Norway vs the field

Elo rating: 1914 vs field median 1780 (1.08× the field) Elo rating 1914 med 1780 Recent NT form: 2.40 ppg vs field median 1.87 ppg (1.29× the field) Recent NT form 2.40 ppg med 1.87 ppg Squad value: €580M vs field median €286M (2.03× the field) Squad value €580M med €286M Squad form (global): 0.299 vs field median 0.211 (1.42× the field) Squad form (global) 0.299 med 0.211 Fitness readiness: 0.777 vs field median 0.707 (1.10× the field) Fitness readiness 0.777 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 (0.95× 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

Fig. D3 Bayesian projection residual g

Norway on the decoupling axis

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

g = +0.24 ± 0.08: 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
24.7mean age
24mean caps
65%in a top-5 league
22distinct clubs
3largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Ørjan NylandGKSevillaLa Liga +2.13z5400710
2Morten ThorsbyMFCremoneseSerie A +1.70z6441310
3Kristoffer AjerDFBrentfordPremier League +2.21z2,1550522
4Leo ØstigårdDFGenoaSerie A +1.70z2,6445381
5David Møller WolfeDFWolverhampton Wanderersno club data221
6Patrick BergMFBodø/GlimtEliteserien −0.13z3,5915430
7Alexander SørlothFWAtlético MadridLa Liga +2.13z2,919207226
8Sander BergeMFFulhamPremier League +2.21z3,0280661
9Erling HaalandFWManchester CityPremier League +2.21z4,473425055
10Martin Ødegaard (captain)MFArsenalPremier League +2.21z2,3151685
11Jørgen Strand LarsenFWCrystal PalacePremier League +2.21z1,3764286
12Sander TangvikGKHamburger SVBundesliga +1.84z90000
13Egil SelvikGKWatfordChampionship +2.21z3,645070
14Fredrik AursnesMFBenficaPrimeira Liga +1.14z4,4254221
15Fredrik André BjørkanDFBodø/GlimtEliteserien −0.13z3,4743211
16Marcus Holmgren PedersenDFTorinoSerie A +1.70z2,3411320
17Torbjørn HeggemDFBolognaSerie A +1.70z2,8850150
18Kristian ThorstvedtMFSassuoloSerie A +1.70z2,4804374
19Thelo AasgaardMFRangersPremiership −0.28z2,351785
20Antonio NusaFWRB LeipzigBundesliga +1.84z2,3205248
21Andreas SchjelderupMFBenficaPrimeira Liga +1.14z2,57511121
22Oscar BobbMFFulhamPremier League +2.21z7540202
23Jens Petter HaugeMFBodø/GlimtEliteserien −0.13z3,39214151
24Sondre LangåsDFDerby CountyChampionship +2.21z2,058330
25Henrik FalchenerDFVikingEliteserien −0.13z2,926610
26Julian RyersonFWBorussia DortmundBundesliga +1.84z3,8060431

Source · Official squad announcements · API-Football (global club coverage). 1 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 96%.

Diaspora in the hosts

22,433

4.0 per 1,000 of home population

Host-language familiarity

Foreign

primary language Norwegian Nynorsk

Climate adaptation gap

+1.2°C

home-vs-venue heat differential

Venue extremes

31°C

peak heat index · altitude up to 83 m

Travel

6h

max time-zone shift · nearest venue 5,651 km

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

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

Norway — Elo since 1950

1971 world #15
Norway Qualified-field median

Norway ends the series at 1971 Elo, the world’s 15th-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.

96% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
96% 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 Norway, 1 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →