away
Curaçao Germany
1.3% win 5.2% draw 93.5% loss
Most likely 0–3 (13.0%) λ 0.44–3.85 Over 2.5 80% · BTTS 35%
🇨🇼
Curaçao
0.0% Champion probability ±0.00 MC-SE
- Coach
- Dick Advocaat foreign · Dutch
- Elo (model)
- 1,434 world 96th
- Squad value
- €34M
- Power → Reality
- 48th 44th 0.00 pp · neutral draw
§ 01
The forecast
How far Curaçao 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
Curaçao — stage progression
Curaçao is most likely eliminated before the knockout rounds: 5% to clear the group. Champion probability 0.00%.
§ 02
The group & the path
Group E advancement odds, the bracket half Curaçao sits in, and the earliest round they could meet each leading side.
| Group E | Confed | Advance (top 2) | Reach R32 | |
|---|---|---|---|---|
| 1 | 🇩🇪Germany | UEFA | 62.5% | 98.1% |
| 2 | 🇪🇨Ecuador | CONMEBOL | 50.7% | 93.3% |
| 3 | 🇨🇮Côte d'Ivoire | CAF | 31.8% | 80.2% |
| 4 | 🇨🇼Curaçao | CONCACAF | 0.4% | 5.2% |
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 Curaçao against that side. Full bracket & collision matrix →
§ 03
Match by match
Curaçao'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%.
away
Curaçao Ecuador
3.6% win 14.1% draw 82.3% loss
Most likely 0–2 (18.5%) λ 0.35–2.38 Over 2.5 51% · BTTS 27%
home
Curaçao Côte d'Ivoire
7.3% win 18.7% draw 74% loss
Most likely 0–2 (15.9%) λ 0.53–2.13 Over 2.5 49% · 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 Curaçao. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where Curaçao stands against the median of the 48-team field, metric by metric. The dot is Curaçao; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
Curaçao vs the field
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.
§ 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
Curaçao on the decoupling axis
g = −0.35 ± 0.10: the record outruns the squad price — the team has achieved more than its comparatively modest squad value would predict.
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
25.6mean age
23mean caps
8%in a top-5 league
22distinct clubs
2largest club bloc
| # | Player | Pos | Club | League | Club min | Gls | Caps | NT gls |
|---|---|---|---|---|---|---|---|---|
| 1 | Eloy Room | GK | Miami FC | — | — no club data | — | 71 | 0 |
| 2 | Shurandy Sambo | DF | Sparta Rotterdam | Eredivisie +0.74z | 1,753 | 0 | 8 | 0 |
| 3 | Juriën Gaari | DF | Abha | — | — no club data | — | 60 | 1 |
| 4 | Roshon van Eijma | DF | RKC Waalwijk | Eerste Divisie +0.74z | 2,270 | 0 | 28 | 1 |
| 5 | Sherel Floranus | DF | PEC Zwolle | Eredivisie +0.74z | 2,980 | 1 | 28 | 0 |
| 6 | Godfried Roemeratoe | MF | RKC Waalwijk | Eerste Divisie +0.74z | 2,478 | 0 | 28 | 1 |
| 7 | Juninho Bacuna | MF | Volendam | Eredivisie +0.74z | 717 | 0 | 49 | 15 |
| 8 | Livano Comenencia | MF | Zürich | Super League −0.07z | 1,522 | 0 | 20 | 2 |
| 9 | Jürgen Locadia | FW | Miami FC | — | — no club data | — | 13 | 1 |
| 10 | Leandro Bacuna (captain) | MF | Iğdır | — | — no club data | — | 72 | 16 |
| 11 | Jeremy Antonisse | FW | Kifisia | Super League 1 +0.03z | 2,006 | 1 | 27 | 4 |
| 12 | Sontje Hansen | FW | Middlesbrough | Championship +2.21z | 540 | 1 | 6 | 1 |
| 13 | Tyrese Noslin | FW | Telstar | Eredivisie +0.74z | 2,562 | 2 | 7 | 1 |
| 14 | Kenji Gorré | FW | Maccabi Haifa | Ligat Ha'al −0.55z | 1,122 | 3 | 38 | 6 |
| 15 | Ar'jany Martha | MF | Rotherham United | — | — no club data | — | 9 | 2 |
| 16 | Jearl Margaritha | FW | Beveren | Challenger Pro League −0.07z | 1,604 | 5 | 22 | 5 |
| 17 | Brandley Kuwas | FW | Volendam | Eredivisie +0.74z | 2,298 | 7 | 35 | 2 |
| 18 | Armando Obispo | DF | PSV Eindhoven | Eredivisie +0.74z | 1,414 | 2 | 6 | 0 |
| 19 | Gervane Kastaneer | FW | Terengganu | — | — no club data | — | 29 | 9 |
| 20 | Joshua Brenet | DF | Kayserispor | Süper Lig +0.49z | 797 | 0 | 18 | 2 |
| 21 | Tahith Chong | MF | Sheffield United | — | — no club data | — | 6 | 3 |
| 22 | Kevin Felida | MF | Den Bosch | Eerste Divisie +0.74z | 3,367 | 4 | 19 | 1 |
| 23 | Riechedly Bazoer | DF | Konyaspor | Süper Lig +0.49z | 950 | 0 | 5 | 0 |
| 24 | Deveron Fonville | DF | NEC | — | — no club data | — | 2 | 0 |
| 25 | Tyrick Bodak | GK | Telstar | — | — no club data | — | 4 | 0 |
| 26 | Trevor Doornbusch | GK | VVV-Venlo | KNVB Beker +0.74z | 56 | 0 | 8 | 0 |
Source · Official squad announcements · API-Football (global club coverage). 9 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 65%.
§ 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
505
3.0 per 1,000 of home population
Host-language familiarity
Shared
primary language English · spoken in a host
Climate adaptation gap
+8.8°C
home-vs-venue heat differential
Venue extremes
47°C
peak heat index · altitude up to 273 m
Travel
1h
max time-zone shift · nearest venue 1,944 km
Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records
§ 08
Elo trajectory
Curaçao's long-run strength against the qualified-field median, 1950–2026.
Fig. D4 eloratings.net method · year-end values
Curaçao — Elo since 1950
Curaçao Qualified-field median
Curaçao ends the series at 1606 Elo, the world’s 96th-ranked side — below the qualified-field median.
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
65% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
65% 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 Curaçao, 9 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →