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Iraq Norway
8.1% win 20% draw 71.9% loss
Most likely 0–2 (15.7%) λ 0.54–2.03 Over 2.5 47% · BTTS 37%
🇮🇶
Iraq
0.0% Champion probability ±0.00 MC-SE
- Coach
- Graham Arnold foreign · Australian
- Elo (model)
- 1,618 world 56th
- Squad value
- €23M
- Power → Reality
- 41st 42nd −0.00 pp · neutral draw
§ 01
The forecast
How far Iraq 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
Iraq — stage progression
Iraq is most likely eliminated before the knockout rounds: 18% to clear the group. Champion probability 0.00%.
§ 02
The group & the path
Group I advancement odds, the bracket half Iraq sits in, and the earliest round they could meet each leading side.
| Group I | Confed | Advance (top 2) | Reach R32 | |
|---|---|---|---|---|
| 1 | 🇫🇷France | UEFA | 68.1% | 95.2% |
| 2 | 🇳🇴Norway | UEFA | 50.8% | 87.7% |
| 3 | 🇸🇳Senegal | CAF | 30.1% | 69.3% |
| 4 | 🇮🇶Iraq | AFC | 3.6% | 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 Iraq against that side. Full bracket & collision matrix →
§ 03
Match by match
Iraq'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
Iraq France
5.2% win 16.6% draw 78.2% loss
Most likely 0–2 (17.2%) λ 0.44–2.24 Over 2.5 50% · BTTS 32%
away
Iraq Senegal
14.6% win 27.6% draw 57.8% loss
Most likely 0–1 (16.7%) λ 0.64–1.52 Over 2.5 37% · BTTS 38%
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 Iraq. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where Iraq stands against the median of the 48-team field, metric by metric. The dot is Iraq; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
Iraq 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
Iraq on the decoupling axis
g = −0.55 ± 0.05: 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
25.5mean age
27mean caps
8%in a top-5 league
20distinct clubs
3largest club bloc
| # | Player | Pos | Club | League | Club min | Gls | Caps | NT gls |
|---|---|---|---|---|---|---|---|---|
| 1 | Fahad Talib | GK | Al-Talaba | — | — no club data | — | 21 | 0 |
| 2 | Rebin Sulaka | DF | Port | — | — no club data | — | 55 | 1 |
| 3 | Hussein Ali | DF | Pogoń Szczecin | Ekstraklasa −0.29z | 526 | 2 | 26 | 1 |
| 4 | Zaid Tahseen | DF | Pakhtakor | — | — no club data | — | 27 | 1 |
| 5 | Akam Hashim | DF | Al-Zawraa | — | — no club data | — | 13 | 1 |
| 6 | Manaf Younis | DF | Al-Shorta | — | — no club data | — | 33 | 1 |
| 7 | Youssef Amyn | MF | AEK Larnaca | 1. Division −0.31z | 129 | 0 | 26 | 2 |
| 8 | Ibrahim Bayesh | MF | Al Dhafra | Pro League −0.09z | 129 | 0 | 75 | 8 |
| 9 | Ali Al-Hamadi | FW | Luton Town | League One +2.21z | 455 | 1 | 19 | 5 |
| 10 | Mohanad Ali | FW | Dibba | — | — no club data | — | 71 | 27 |
| 11 | Ahmed Qasem | FW | Nashville SC | US Open Cup −0.71z | 2,662 | 3 | 2 | 0 |
| 12 | Jalal Hassan (captain) | GK | Al-Zawraa | — | — no club data | — | 101 | 0 |
| 13 | Ali Yousif | FW | Al-Talaba | — | — no club data | — | 6 | 1 |
| 14 | Zidane Iqbal | MF | Utrecht | Eredivisie +0.74z | 358 | 0 | 24 | 2 |
| 15 | Ahmed Maknzi | DF | Al-Karma | — | — no club data | — | 6 | 0 |
| 16 | Amir Al-Ammari | MF | Cracovia | Ekstraklasa −0.29z | 2,505 | 1 | 50 | 3 |
| 17 | Ali Jasim | FW | Al-Najma | Pro League −0.86z | 893 | 3 | 36 | 2 |
| 18 | Aymen Hussein | FW | Al-Karma | — | — no club data | — | 94 | 33 |
| 19 | Kevin Yakob | MF | AGF | Superliga −0.53z | 3,043 | 7 | 8 | 0 |
| 20 | Aimar Sher | MF | Sarpsborg 08 | Eliteserien −0.13z | 1,792 | 0 | 6 | 0 |
| 21 | Marko Farji | FW | Venezia | Serie B +1.70z | 21 | 0 | 11 | 0 |
| 22 | Ahmed Basil | GK | Al-Shorta | — | — no club data | — | 15 | 0 |
| 23 | Merchas Doski | DF | Viktoria Plzeň | Czech Liga +0.20z | 2,664 | 1 | 31 | 1 |
| 24 | Zaid Ismail | MF | Al-Talaba | — | — no club data | — | 6 | 0 |
| 25 | Mustafa Saadoon | DF | Al-Shorta | — | — no club data | — | 16 | 0 |
| 26 | Frans Putros | DF | Persib | — | — no club data | — | 27 | 0 |
Source · Official squad announcements · API-Football (global club coverage). 14 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 46%.
§ 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
329,830
7.0 per 1,000 of home population
Host-language familiarity
Foreign
primary language Arabic
Climate adaptation gap
−1.6°C
home-vs-venue heat differential
Venue extremes
35°C
peak heat index · altitude up to 83 m
Travel
8h
max time-zone shift · nearest venue 9,371 km
Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records
§ 08
Elo trajectory
Iraq's long-run strength against the qualified-field median, 1960–2026.
Fig. D4 eloratings.net method · year-end values
Iraq — Elo since 1960
Iraq Qualified-field median
Iraq ends the series at 1751 Elo, the world’s 56th-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
46% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
46% 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 Iraq, 14 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →