Round of 32 · Match 6
Ivory CoastvsNorway
2026-06-30·12:00 local·AT&T Stadium · DallasPredictions finalised
Match signals
Factors that favour each side, from statistical models to group stage form and match conditions. Longer bars = stronger advantage.
Norway are strong favourites at 52% vs Ivory Coast's 21%. Most signals point the same way. Ivory Coast will need to outperform their rating.
📊What the Models Say
Rates teams by a single strength number updated after every match. Simpler but fast to react. It rates Norway at 69% to win vs Ivory Coast at 9%.
Simulates the goal-scoring process using attack and defence strength. The heaviest-weighted model. It rates Norway at 44% to win vs Ivory Coast at 26%.
Groups teams by confederation to share information. Helps for teams with fewer matches. It rates Norway at 46% to win vs Ivory Coast at 26%.
The published probability after calibration and adjustments. This is what the model says. It rates Norway at 52% to win vs Ivory Coast at 21%.
All 3 models agree: Norway is favoured. When models agree, the signal is stronger.
⚽Tournament Form
Norway collected 12 points (4W 0D 2L) vs Ivory Coast's 6 (2W 0D 2L). A stronger tournament record.
Norway averaged 2.17 goals per match vs Ivory Coast's 1.25. More firepower coming in.
Ivory Coast conceded just 1.0 goals/match vs Norway's 1.83. Tighter at the back.
Norway's goal difference of +2 is better than Ivory Coast's +1. They outperformed opponents by more.
📈Momentum
Norway's rating rose +30.1 during the tournament while Ivory Coast's moved +22.8. The tournament has been kinder to Norway.
Ivory Coast's players improved their form ratings during the tournament (+0.0029) vs Norway (-0.0001). Players trending upward.
🏆Team Quality
Norway is rated 1912 vs Ivory Coast's 1676 (gap: 236). That's a very large gap in historical team strength.
The model expects Norway to create 1.23 expected goals vs Ivory Coast's 0.90. More and better chances projected.
Norway's top 3 starters are harder to replace (avg VORP 0.63) than Ivory Coast's (0.40). More star power in key positions.
Similar levels of squad familiarity from club football.
🌍Match Conditions
Norway traveled 7,665km vs Ivory Coast's 9,699km. A shorter journey means less fatigue.
Ivory Coast face a 5h timezone shift vs Norway's 7h. Less jet lag disruption.
17 signals across 5 categories. Signal strength reflects how large the gap is between the two teams on each factor. Signals are descriptive, not prescriptive.
La previsione
Match-outcome probability
- Ivory Coast win29.2%
- Draw29.7%
- Norway win41.1%
A 236-point Elo gap frames this as a significant mismatch, yet the model still gives Ivory Coast a 21% probability of a result — enough to make this more than a formality.
▸Gol e punteggi
Likeliest score 1–1 (14.0%) · xG 0.9 - 1.2
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–114.0%
- 0–113.9%
- 0–012.8%
- 1–09.9%
- 0–29.0%
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
- 0–035.2%
- 0–120.6%
- 1–015.0%
- 1–110.1%
- 0–26.5%
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 goals87.2%
- More than 1.5 goals63.4%
- More than 2.5 goals35.7%
- More than 3.5 goals16.6%
- More than 4.5 goals6.5%
- More than 5.5 goals2.1%
- Both teams score42.7%
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
- Ivory Coast clean sheetOpposing team scores zero29.4%
- Norway clean sheetOpposing team scores zero40.7%
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
- Ivory Coast by 4+0.5%
- Ivory Coast by 3+2.4%
- Ivory Coast by 2+9.2%
- Ivory Coast by 1+26.3%
- Draw30.8%
- Norway by 1+42.9%
- Norway by 2+19.4%
- Norway by 3+6.6%
- Norway by 4+1.8%
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.
▸Come si sviluppa la partita
Over 2.5 goals 35.7% · BTTS 42.7%
Game state through the match
- Ivory Coast ahead27.1%
- Level29.2%
- Norway ahead43.7%
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
- 0–1529.8%
- 15–3020.9%
- 30–4514.7%
- 45–6010.3%
- 60–757.2%
- 75–905.1%
- No goal12.0%
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 → | HIvory Coast win | DDraw | ANorway win |
|---|---|---|---|
| HIvory Coast ahead | 15.6% | 4.5% | 1.6% |
| DLevel | 10.2% | 20.6% | 15.2% |
| ANorway ahead | 1.1% | 4.5% | 26.8% |
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
- Ivory Coast trail at HT, avoid defeat at FT5.6%
- Norway trail at HT, avoid defeat at FT6.0%
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.
PK shootout simulator
If the match ends level after extra time, the model estimates the shootout outcome from each team's Bayesian-smoothed conversion / save rate (Model #15). The bracket simulator uses the symmetric (averaged) ordering; the two what-if scenarios below show how the win probabilities shift when conditioning on which team kicks first.
- Ivory Coast60.8%
- Norway39.2%
- Ivory Coast72.8%
- Norway27.2%
- Ivory Coast49.1%
- Norway50.9%
First-kicker advantage
The first kicker's per-kick conversion rate is scaled by ×1.050 (about +5.0%), stacked on the Markov chain's structural asymmetry. Real World Cup shootouts use a coin toss for kicker order, so on average the order is 50/50 — the symmetric path above is the relevant number for a single fixture. The ordering-conditioned probabilities are a descriptive what-if scenario.
Literature: first kickers win ≈ 60% historically (Apesteguia & Palacios-Huerta, American Economic Review 2010; Vandebroek et al. 2016).
Per-team posteriors: Ivory Coast conv 73.3%, save 26.7%; Norway conv 72.0%, save 20.0%. Smoothed against the global prior with prior strength 20 — see /docs/methodology/.
▸Squadre e giocatori
Top scorer: Haaland (11.9%)
Match detail
Ivory Coast
Model-rated key players: Franck Kessié (MF) — P(scores) 8.1%; Simon Adingra (FW) — P(scores) 1.5%; Jérémie Boga (FW) — P(scores) 1.4%.
Ivory Coast under Emerse Faé play a possession dominant game, holding 58% of the ball — among the highest in the tournament field. They press intensely (PPDA 13.7, 2nd in the field).
To succeed, Ivory Coast must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match.
Norway
Model-rated key players: Erling Haaland (FW) — P(scores) 11.9%; Alexander Sørloth (FW) — P(scores) 4.6%; Erling Braut Haaland (FW) — P(scores) 2.5%.
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.
Ivory Coast's predicted XI averages 1,658 club minutes over the 2024-25 season (light load).
Ivory Coast coverage: 60.0% (7/11 XI matched against the FBref Big-5) · Norway: 46.0% (7/11).
Ivory Coast historically converts 16.5% of xG from set-pieces, contributing 0.15 expected set-piece goals in this fixture. Norway converts 13.6% from set-pieces (0.17 expected). Combined, the model expects 0.32 set-piece goals across the 90 minutes.
- P(Ivory Coast scores set-piece goal) 13.8%
- P(Norway scores set-piece goal) 15.4%
- P(set-piece goal in match) 27.1%
Ivory Coast: Nicolas Pépé on corners (13 corners), Ibrahim Sangaré on free kicks (per fbref 2022 23) · Norway: Martin Ødegaard on free kicks (per fbref 2022 23)
If a penalty is awarded to Ivory Coast, the model gives 73.3% conversion, 72.0% for Norway. If this match goes to a shootout, the symmetric (coin-toss averaged) win probability is 60.8% Ivory Coast / 39.2% Norway.
Ivory Coast primary PK: Franck Kessié (2/3 in 2021-22, per fbref 2022 23) · 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.
Squad depth
Most irreplaceable starters
Ivory Coast
- Oumar DiakitéStrikerCover: Elye Wahi · 0.000.67gap
- Ibrahim SangaréDefensive midfieldNo natural backup0.30gap
- Ousmane DiomandeCentre-backCover: Emmanuel Agbadou · 0.730.23gap
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 level168 m
- Avg temperatureFive-year mean over the tournament window29.4 °C
- Avg humidity63%
- Heat stressShade WBGT ~30.8 °CHigh heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf stadium; a temporary natural-grass pitch on a sand root-zone is laid over the turf 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. Afternoon 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)
- Franck KessiéPKMF8.1%
- Simon AdingraFW1.5%
- Jérémie BogaFW1.4%
- Erling HaalandPKFW11.9%
- Alexander SørlothFW4.6%
- Erling Braut HaalandFW2.5%
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
Ivory Coast
vs Curaçao · avg 7.8
Norway
vs France · avg 6.3
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.
8Amad Diallo60'–110'Came on as a substitute and scored a brilliant solo equalizer, completely changing the dynamic of the match.
2goals▼
Came on as a substitute and scored a brilliant solo equalizer, completely changing the dynamic of the match.
Match timeline
7Yahia Fofana6'–128'Made numerous important saves throughout the match, denying Norway on multiple occasions despite conceding two goals.
6saves▼
Made numerous important saves throughout the match, denying Norway on multiple occasions despite conceding two goals.
Match timeline
8Erling Haaland58'–132'Scored the decisive winning goal for Norway with a clinical finish, securing their victory despite being isolated for much of the first half.
2goals2shots1on target▼
Scored the decisive winning goal for Norway with a clinical finish, securing their victory despite being isolated for much of the first half.
Match timeline
8Norway Goalkeeper51'–142'Made several important saves throughout the match, including a crucial one in added time to preserve the lead.
3saves▼
Made several important saves throughout the match, including a crucial one in added time to preserve the lead.
Match timeline
7Torbjørn Heggem57'–57'Made a vital goal-line block that prevented Ivory Coast from scoring, preserving Norway's lead.
1blocks▼
Made a vital goal-line block that prevented Ivory Coast from scoring, preserving Norway's lead.
Match timeline
7Martin Ødegaard39'–39'Provided the assist for Norway's opening goal, demonstrating his playmaking ability.
▼
Provided the assist for Norway's opening goal, demonstrating his playmaking ability.
Match timeline
7Oscar Bobb86'–86'Delivered a crucial pass that directly led to Haaland's winning goal, showcasing his impact from the bench.
▼
Delivered a crucial pass that directly led to Haaland's winning goal, showcasing his impact from the bench.
Match timeline
Match observations
- The match was an exciting, end-to-end contest with both teams creating numerous scoring opportunities.
- Norway took an early lead, but Ivory Coast fought back with a moment of individual brilliance to equalize.
- Both goalkeepers were called upon to make several important stops throughout the game.
▸Dietro le quinte
Model-by-model comparison
Ivory Coast vs Norway
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 8.7% | 22.0% | 69.3% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 26.4% | 29.7% | 43.9% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 25.7% | 28.3% | 46.0% |
| Bayesian stackingLearned-weight combination | — | 15.3% | 27.4% | 57.3% |
| Ensemble (published)Uniform average + isotonic calibration | — | 21.4% | 26.5% | 52.1% |
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
Latest news & match context
- Erling Haaland brings $750 stuffed raccoon back to Norway after World Cup exit · The Independent — Football · 14 Jul
- Stage:
- Round of 32 · Match 6
- Date:
- 30 Jun
- Venue:
- AT&T Stadium, Dallas
a 29°C kickoff modestly suppresses expected scoring at this venue.
Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.
- 1.Elimination stakes: A one-off elimination tie. Motivation, risk appetite and game management under tournament pressure are not model inputs; the forecast rests on team strength and the style matchup.
- 2.Rest differential: Ivory Coast have had 5 days since their previous match versus 4 for Norway. Rest and recovery are not model inputs.
Ivory Coast
Ivory Coast come in at close to full strength.
Norway
Norway come in at close to full strength.
Ivory Coast 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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