WC 2026 · Forecasting Oxford Football Forecasting
🇳🇱

Netherlands

UEFA Group F
4.9% Champion probability ±0.07 MC-SE
Coach
Ronald Koeman home · Dutch
Elo (model)
1,944 world 9th
Squad value
€1076M
Power → Reality
7th 7th −0.37 pp · tough draw

Fig. D1 Fixture-aware · 100k sims

Netherlands — stage progression

Round of 32: 92.31% (95% MC 92.15%–92.48%; MC-SE ±0.08 pts) Round of 32 reach 92.3% ±0.08 Round of 16: 52.19% (95% MC 51.88%–52.50%; MC-SE ±0.16 pts) Round of 16 reach 52.2% ±0.16 Quarter-final: 34.88% (95% MC 34.58%–35.17%; MC-SE ±0.15 pts) Quarter-final reach 34.9% ±0.15 Semi-final: 19.77% (95% MC 19.52%–20.02%; MC-SE ±0.13 pts) Semi-final reach 19.8% ±0.13 Final: 10.05% (95% MC 9.86%–10.23%; MC-SE ±0.10 pts) Final reach 10.0% ±0.10 Champion: 4.85% (95% MC 4.72%–4.99%; MC-SE ±0.07 pts) Champion reach 4.9% ±0.07

On the central forecast, Netherlands more likely than not reaches the Round of 16 (52%). Champion probability is 4.9% ± 0.07 pts.

Source · Oxford Football Forecasting model
Group F Confed Advance (top 2) Reach R32
1🇳🇱NetherlandsUEFA52.2%92.3%
2🇯🇵JapanAFC37.9%83.1%
3🇸🇪SwedenUEFA17.8%60.2%
4🇹🇳TunisiaCAF6.6%33.2%

Source · Oxford Football Forecasting model

Bracket position Half 0 · Quadrant 0

Earliest possible meetings

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

Match 11 · 2026-06-14 · Dallas Stadium home
Netherlands Japan
42.2% win 30.9% draw 26.9% loss
Most likely 1–1 (14.2%) λ 1.25–0.94 Over 2.5 37% · BTTS 45%
Match 35 · 2026-06-20 · Houston Stadium home
Netherlands Sweden
58.6% win 24.7% draw 16.7% loss
Most likely 1–0 (11.9%) λ 1.79–0.85 Over 2.5 49% · BTTS 49%
Match 58 · 2026-06-25 · Kansas City Stadium away
Netherlands Tunisia
68.3% win 22.2% draw 9.5% loss
Most likely 1–0 (15.8%) λ 1.87–0.56 Over 2.5 44% · BTTS 37%
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 Netherlands. 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

Netherlands vs the field

Elo rating: 1944 vs field median 1780 (1.09× the field) Elo rating 1944 med 1780 Recent NT form: 2.00 ppg vs field median 1.87 ppg (1.07× the field) Recent NT form 2.00 ppg med 1.87 ppg Squad value: €1076M vs field median €286M (3.76× the field) Squad value €1076M med €286M Squad form (global): 0.252 vs field median 0.211 (1.19× the field) Squad form (global) 0.252 med 0.211 Fitness readiness: 0.822 vs field median 0.707 (1.16× the field) Fitness readiness 0.822 med 0.707 Familiarity / chemistry: 0.025 vs field median 0.015 (1.60× the field) Familiarity / chemistry 0.025 med 0.015 Experience (mean caps): 30 vs field median 25 (1.19× the field) Experience (mean caps) 30 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

Netherlands on the decoupling axis

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

g = +0.41 ± 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
24.8mean age
30mean caps
96%in a top-5 league
20distinct clubs
3largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Bart VerbruggenGKBrighton & Hove AlbionPremier League +2.21z3,4200280
2Jurriën TimberDFArsenalPremier League +2.21z3,5434240
3Marten de RoonMFAtalantaSerie A +1.70z3,9182431
4Virgil van Dijk (captain)DFLiverpoolPremier League +2.21z5,04899112
5Nathan AkéDFManchester Cityno club data595
6Jan Paul van HeckeDFBrighton & Hove AlbionPremier League +2.21z3,4113110
7Justin KluivertMFBournemouthPremier League +2.21z1,0902120
8Ryan GravenberchMFLiverpoolPremier League +2.21z4,2216261
9Wout WeghorstFWAjaxEredivisie +0.74z2,24695214
10Memphis DepayFWCorinthiansSerie A +1.03z3,2791010955
11Cody GakpoFWLiverpoolPremier League +2.21z3,67694919
12Mats WiefferDFBrighton & Hove AlbionPremier League +2.21z1,9462151
13Robin RoefsGKSunderlandPremier League +2.21z3,373010
14Tijjani ReijndersMFManchester CityPremier League +2.21z3,1076317
15Micky van de VenDFTottenham Hotspurno club data201
16Guus TilMFPSV Eindhovenno club data61
17Noa LangFWGalatasaraySüper Lig +0.49z1,0482153
18Donyell MalenFWRomaSerie A +1.70z1,688155213
19Brian BrobbeyFWSunderlandPremier League +2.21z1,9787111
20Teun KoopmeinersMFJuventusSerie A +1.70z2,9894283
21Frenkie de JongMFBarcelonaLa Liga +2.13z2,6651652
22Denzel DumfriesDFInter MilanSerie A +1.70z2,06857111
23Mark FlekkenGKBayer LeverkusenBundesliga +1.84z3,1500110
24Crysencio SummervilleFWWest Ham UnitedPremier League +2.21z2,864710
25Jorrel HatoDFChelseaPremier League +2.21z2,265280
26Quinten TimberMFMarseilleLigue 1 +1.70z1,1810111

Source · Official squad announcements · API-Football (global club coverage). 3 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 88%.

Diaspora in the hosts

176,636

10.0 per 1,000 of home population

Host-language familiarity

Foreign

primary language Dutch

Climate adaptation gap

+7.8°C

home-vs-venue heat differential

Venue extremes

47°C

peak heat index · altitude up to 273 m

Travel

6h

max time-zone shift · nearest venue 5,591 km

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

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

Netherlands — Elo since 1950

2005 world #9
Netherlands Qualified-field median

Netherlands ends the series at 2005 Elo, the world’s 9th-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.

88% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
88% 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 Netherlands, 3 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →