Group I · Matchday 1
IraqvsNorway
2026-06-16·18:00 localPredictions finalised
The forecast
Match-outcome probability
- Iraq win13.3%
- Draw24.5%
- Norway win62.2%
A 305-point Elo gap frames this as a significant mismatch, yet the model still gives Iraq a 5% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Iraq
- ·Iraq has won 4 of their last 6 matches, including two FIFA World Cup qualification games.
- ·The ensemble model's home win probability of 13.2% is notably higher than the Elo model's 3.7%, suggesting other underlying factors contribute to a slightly more optimistic outlook for Iraq.
Favoring Norway
- ·Norway holds a significant Elo advantage, with a delta of 305 points over Iraq.
- ·Norway's expected goals (xG) of 1.75 is nearly three times higher than Iraq's 0.61 xG.
- ·Norway is ranked 29th in the FIFA rankings, indicating a substantial quality difference compared to Iraq, whose FIFA rank is not provided.
- ·Historical context from video notes indicates a dominant 4-1 victory for Norway over Iraq, highlighting Norway's attacking prowess and the performance of Erling Haaland.
What the model can't fully price
- ·The model does not account for the impact of 1 player carrying a fitness doubt across the squads, as its lineup channel currently contributes zero.
- ·Specific venue conditions are not factored into the probabilities as the venue information is null.
Form check
Iraq
SteadyIraq has shown mixed but generally positive form recently, securing 4 wins in their last 6 matches, including two World Cup qualifiers. However, they also suffered two losses in the Arab Cup.
4 wins in last 6 matches
Norway
SteadyNorway's recent form includes 3 wins, 2 draws, and 1 loss in their last 6 outings. Their World Cup qualification matches have been particularly strong, with two 4-1 victories.
Scored 4 goals in two of their last three World Cup qualification matches
Analysis
How it plays out
Both sides run a balanced system, so this becomes a test of who executes the same ideas better on the day. Norway will expect to hold 56% possession. Iraq need their shape to stay compact without the ball and be clinical when they win it back.
What decides it
Erling Haaland's 13.2% scoring probability is the highest in this fixture. Containing that output is Iraq's primary defensive task.
Off the pitch
Ståle Solbakken (6 years in charge of Norway) vs Graham Arnold (1 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
The model gives Iraq just 13.1% to win. Every World Cup produces group-stage upsets; the question is whether this fixture is one of them.
▸Goals & scorelines
Likeliest score 0–1 (14.8%) · xG 0.6 - 1.9
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 0–114.8%
- 0–214.7%
- 1–19.9%
- 0–39.4%
- 1–28.9%
From the Dixon-Coles joint Poisson with the low-score correction. Scorelines are listed in probability order; this is a description of the model's distribution, not a recommendation.
Most likely half-time scorelines
- 0–028.9%
- 0–126.6%
- 0–213.0%
- 1–18.8%
- 1–08.1%
Same Dixon-Coles fit as the full-time list above, with rates halved to a 45-minute window and the low-score correction applied to that 1st-half block. The 0-0 row sits higher here than at full-time because fewer minutes have elapsed.
Goal totals
- More than 0.5 goals91.4%
- More than 1.5 goals72.3%
- More than 2.5 goals46.2%
- More than 3.5 goals24.7%
- More than 4.5 goals11.2%
- More than 5.5 goals4.3%
- Both teams score39.5%
Each row is the probability the match finishes with more than the listed number of goals. Both-teams-to-score is the probability each side scores at least once. All values are marginals of the Dixon-Coles joint goal grid that produces the scoreline list above — not market lines or any other operator construct.
Event-typed probabilities
- Iraq clean sheetOpposing team scores zero14.8%
- Norway clean sheetOpposing team scores zero54.4%
Derived from the same Dixon-Coles joint distribution as the scoreline list. These are descriptive event probabilities — see CLAUDE.md §3/§4 (formerly COMPLIANCE.md §4.2.7) for the framing the project uses.
Win-margin probability
- Iraq by 4+0.1%
- Iraq by 3+0.5%
- Iraq by 2+2.6%
- Iraq by 1+10.4%
- Draw21.7%
- Norway by 1+67.9%
- Norway by 2+42.3%
- Norway by 3+21.0%
- Norway by 4+8.6%
Each row is the probability the match ends with the listed margin or larger in that direction. Marginal of the Dixon-Coles joint goal grid; the “by 1+” rows plus the draw row sum to 1.
▸How the match unfolds
Over 2.5 goals 46.2% · BTTS 39.5%
Game state through the match
- Iraq ahead11.0%
- Level20.5%
- Norway ahead68.5%
Probability of each game state at minutes 0, 15, 30, 45, 60, 75, 90 — derived from two independent thinned-Poisson processes with the Dixon-Coles per-team rates. The three lines always sum to 1 at each minute. The right column shows the state at the match's closing minute.
When the first goal arrives
- 0–1534.3%
- 15–3022.5%
- 30–4514.8%
- 45–609.7%
- 60–756.4%
- 75–904.2%
- No goal8.0%
Probability the match's first goal arrives in each 15-minute window. Homogeneous Poisson with combined rate λ = λh + λa from the Dixon-Coles fit; the seven rows (six windows + no-goal tail) sum to 1.
Half-time / full-time grid
| HT ↓ / FT → | HIraq win | DDraw | ANorway win |
|---|---|---|---|
| HIraq ahead | 5.9% | 3.2% | 2.0% |
| DLevel | 4.4% | 14.2% | 19.6% |
| ANorway ahead | 0.5% | 3.3% | 46.9% |
Each cell is P(half-time result, full-time result). All nine cells sum to 1. Derived from a halved-λ Dixon-Coles fit for the first half plus an independent-Poisson second-half convolution.
Comeback probability
- Iraq trail at HT, avoid defeat at FT3.8%
- Norway trail at HT, avoid defeat at FT5.1%
Joint probability — P(side trailing at half-time AND avoiding defeat at full-time). NOT conditional on trailing at HT. Derived from the same half-time / full-time decomposition that produces the HT/FT grid above; a tied first half is neither a home nor an away comeback opportunity.
Cards
- Expected yellow cardsMean of the Poisson on total yellow cards.3.45
- Total yellows over 2.567.0%
- Total yellows over 3.545.3%
- Total yellows over 4.526.5%
- Any red cardP(at least one red card in the match).9.5%
Referee not yet assigned. Using the 2026 pool-mean per-match rate as a placeholder; the model picks up the referee's personal rate once the assignment is published. Total yellow cards modelled as a Poisson with mean equal to two team baselines plus the referee's deviation from the pool mean. Reds are modelled the same way, independently. See /docs/methodology/.
▸Teams & players
Top scorer: Haaland (13.2%)
Match detail
Iraq
Model-rated key players: Aymen Hussein (FW) — P(scores) 2.1%; Mohanad Ali (FW) — P(scores) 2.1%; Ali Al-Hamadi (FW) — P(scores) 1.1%.
Limited recent tournament data is available for Iraq's tactical profile. Early indicators suggest a balanced approach.
Iraq will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. Managing minutes for Jalal Hassan across what could be seven matches will test the coaching staff's rotation planning.
Norway
Model-rated key players: Erling Haaland (FW) — P(scores) 13.2%; Alexander Sørloth (FW) — P(scores) 5.7%; Erling Braut Haaland (FW) — P(scores) 3.1%.
Limited recent tournament data is available for Norway's tactical profile. Early indicators suggest a balanced approach.
Norway will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries.
Norway converts 13.6% from set-pieces (0.26 expected). Combined, the model expects 0.26 set-piece goals across the 90 minutes.
- P(Norway scores set-piece goal) 22.9%
- P(set-piece goal in match) 22.9%
Norway: Martin Ødegaard on free kicks (per fbref 2022 23)
If a penalty is awarded to Iraq, the model gives 71.4% conversion, 72.0% for Norway.
Norway primary PK: Erling Haaland (2/2 in 2022-23, per fbref 2022 23).
Derived from the model's per-fixture forecast joint and supporting reference data (predicted squads, set-piece xG share, PK posteriors, club minutes). See /docs/methodology/ for the full methodology.
Tactical forecast
Partial coverage from FotMob match stats (recent qualifiers and friendlies): possession and shot volume only. Press and build-up metrics are not available for this side.
- PPDA
- —
- Possession
- 48%
- Directness (yds/pass)
- —
- Long balls/90
- —
- Set-piece xG
- —
Partial coverage from FotMob match stats (recent qualifiers and friendlies): possession and shot volume only. Press and build-up metrics are not available for this side.
- PPDA
- —
- Possession
- 56%
- Directness (yds/pass)
- —
- Long balls/90
- —
- Set-piece xG
- 14%
Style profile per side from StatsBomb open-data aggregation across recent international tournaments (Euro 2020/2024, Copa America 2024, AFCON 2023, World Cup 2018/2022). The tactical-fingerprint badge maps each team’s observed style vector into one of eight canonical archetypes via a rule-based classifier; teams with fewer than three matches of qualifying coverage carry an “insufficient-data” label rather than being forced into a default. Sides outside the StatsBomb-open corpus use FotMob team match stats from recent qualifiers and friendlies instead (possession and shot volume only), marked as partial coverage. PPDA = passes the side allows per defensive action (lower = more intense press). Formation distributions are not yet produced — that head of the §2.7 classifier is pending its own data pull. See /docs/methodology/.
Squad depth
Most irreplaceable starters
Iraq
- Ali Al-HamadiStrikerCover: Ali Yousif · 0.050.36gap
- Aymen HusseinStrikerCover: Ali Yousif · 0.050.14gap
- Mohanad AliStrikerCover: Ali Yousif · 0.050.12gap
Norway
- Erling HaalandStrikerNo natural backup0.75gap
- Alexander SørlothStrikerNo natural backup0.62gap
- Martin ØdegaardAttacking midfieldCover: Thelo Aasgaard · 0.310.51gap
Gap = how far a side's rating at the position falls from the starter to his likely in-squad replacement (named under each name). Larger = harder to replace. Descriptive metric, does not feed the published probabilities. Methodology →
Match conditions
- AltitudeNear sea level67 m
- Avg temperatureFive-year mean over the tournament window21.8 °C
- Avg humidity76%
- Heat stressShade WBGT ~24.1 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Artificial-turf NFL stadium laying a temporary natural-grass pitch for the tournament.
Heat stress is a shade Wet-Bulb Globe Temperature proxy from the venue's climatology mean temperature and humidity; FIFA mandates cooling breaks at WBGT 32 °C. Evening kickoff (local time). These are long-window averages, not a match-day forecast, and they are not inputs to the forecast.
Top scorers · P(scores in this match)
- Aymen HusseinFW2.1%
- Mohanad AliFW2.1%
- Ali Al-HamadiFW1.1%
- Erling HaalandPKFW13.2%
- Alexander SørlothFW5.7%
- Erling Braut HaalandFW3.1%
Per-player scoring rate from Model #5 (`p_score_per_match`). Reflects each player's npxG/90, expected minutes, team xG share, and the average opposing-team defence. See /docs/methodology/.
Recent match form
Last match player ratings
Iraq
vs Senegal · avg 4.5
Norway
vs Ivory Coast · avg 7.4
Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →
Video analysis: player performance
Per-player ratings and event breakdowns from official highlights analysis. Tap a player to see their full match timeline.
6Unnamed Player #18Showcased attacking intent but lacked decisive contributions to the scoreline.
Showcased attacking intent but lacked decisive contributions to the scoreline.
6Unnamed Player #15Displayed emotion and intensity but without specific impactful actions in the match.
Displayed emotion and intensity but without specific impactful actions in the match.
9Erling Haaland29'–43'Scored two crucial goals, leading Norway's attack and securing a dominant victory.
2goals▼
Scored two crucial goals, leading Norway's attack and securing a dominant victory.
Match timeline
Match observations
- The match concluded with Norway securing a dominant 4-1 victory over Iraq.
- The game featured a lively atmosphere, with both sets of supporters passionately engaging in chants and celebrations throughout the contest.
- Norway's attacking prowess was evident, particularly through the performance of their star striker, Erling Haaland.
▸Under the hood
Model-by-model comparison
Iraq vs Norway
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 0.7% | 22.0% | 77.3% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 10.5% | 21.5% | 68.0% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 11.2% | 21.3% | 67.5% |
| Bayesian stackingLearned-weight combination | — | 2.2% | 17.5% | 80.3% |
| Ensemble (published)Uniform average + isotonic calibration | — | 5.3% | 21.8% | 72.9% |
How each model works
- Elo
- Each team carries a single strength rating updated after every match by a margin-aware K-factor. Match probabilities come from the logistic function of the rating gap. Elo is fast-adapting but coarse — it sees only who won and by how much, not how the goals were scored.
- Dixon-Coles
- A Poisson regression on team-level attack and defence parameters, fitted via maximum likelihood with an exponential time-decay weighting. The Dixon-Coles correction adjusts the four low-score cells (0-0, 1-0, 0-1, 1-1) where independent Poisson underestimates dependence. Produces full scoreline distributions, not just H/D/A.
- Hierarchical Poisson
- A Bayesian Poisson model fitted via MCMC (PyMC) with hierarchical priors that pool attack and defence parameters within confederations. Shrinks small-sample teams toward their confederation mean — helpful for nations with few recent competitive fixtures. Slower to fit but better-calibrated on the tails.
- Bayesian stacking
- Optimises simplex weights (w_elo, w_dc, w_hp) to maximise the leave-one-out log-score across a walk-forward backtest (Yao et al. 2018). The result is a weighted average of the three component models' probabilities, then isotonic-calibrated. Adds no extra features — just learns which component to trust more from historical accuracy.
- Ensemble (published)
- Equal-weight average of all three component models, followed by per-class isotonic regression calibration fitted on 24 months of walk-forward out-of-fold predictions. This is the probability published on the site. The uniform mean is deliberately simple — it avoids overfitting to the stacking weights' training window.
Three independent component models feed two combination strategies. The uniform ensemble is the published probability; Bayesian stacking uses learned weights. Amber bars flag >5pp divergence from the published number. Full methodology
Probability decomposition (transparency surface)
- Baseline ensemble — P(Iraq win)13.3%
- + Lineup contribution0.0pp
- + Style-matchup contribution+0.0pp
- Published P(Iraq win)13.3%
Decomposition of the published P(Iraq win) into the calibrated- baseline plus contributions from the §2.3 expected-XI lineup delta and the §2.7 style-matchup interaction. The §2.7 roadmap is explicit that style effects are second-order to team strength — single-digit-percentage P(win) shifts on extreme style matchups, near-zero on balanced ones. We surface the decomposition for transparency even when the contributions are small; the baseline carries the prediction. Methodology: /docs/methodology.
For this fixture both contributions round to under 0.05pp — the fitted style-matchup pair effect is in the small-magnitude regime the model expects to dominate.
Head-to-head history
| Date | Competition | Venue | Score | Result | xG |
|---|---|---|---|---|---|
| 16 Jun 2026 | FIFA World Cup | NFoxborough | 1–4 | L | — |
Iraq vs Norway, every senior international meeting in the martj42 results dataset (score from Iraq's perspective; H/A/N = home/away/neutral).
Latest news & match context
- Erling Haaland brings $750 stuffed raccoon back to Norway after World Cup exit · The Independent — Football · 14 Jul
- Stage:
- Group I · Matchday 1
- Date:
- 16 Jun
Iraq and Norway both come in at close to full strength, so the forecast rests on baseline team strength rather than late team-news swings.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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