Quarter-final · Match 3
NorwayvsEngland
2026-07-11·17:00 local·Hard Rock Stadium · MiamiPredictions finalised
Match signals
Factors that favour each side, from statistical models to group stage form and match conditions. Longer bars = stronger advantage.
England are dominant at 60% vs Norway's 15%. Quality, form, and model estimates all point the same way. An upset here would be a major story.
📊What the Models Say
Rates teams by a single strength number updated after every match. Simpler but fast to react. It rates England at 54% to win vs Norway at 24%.
Simulates the goal-scoring process using attack and defence strength. The heaviest-weighted model. It rates England at 55% to win vs Norway at 18%.
Groups teams by confederation to share information. Helps for teams with fewer matches. It rates England at 54% to win vs Norway at 20%.
The published probability after calibration and adjustments. This is what the model says. It rates England at 60% to win vs Norway at 15%.
All 3 models agree: England is favoured. When models agree, the signal is stronger.
⚽Tournament Form
England collected 16 points (5W 1D 0L) vs Norway's 12 (4W 0D 2L). A stronger tournament record.
Similar attacking output: Norway 2.17 goals/match, England 2.17.
England conceded just 1.0 goals/match vs Norway's 1.83. Tighter at the back.
England's goal difference of +7 is better than Norway's +2. They outperformed opponents by more.
📈Momentum
Both teams' ratings moved similarly during the tournament (Norway +30.1, England +33.4).
Norway's players improved their form ratings during the tournament (-0.0001) vs England (-0.0068). Players trending upward.
🏆Team Quality
England is rated 2020 vs Norway's 1912 (gap: 108). That's a significant gap in historical team strength.
The model expects England to create 1.50 expected goals vs Norway's 0.75. More and better chances projected.
Norway's top 3 starters are harder to replace (avg VORP 0.63) than England's (0.26). More star power in key positions.
England's starters play together at club level more often (0.049 cohesion) than Norway's (0.000). More shared understanding on the pitch.
🌍Match Conditions
Similar travel distances for both teams.
England face a 5h timezone shift vs Norway's 6h. 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.
El pronóstico
Match-outcome probability
- Norway win15.2%
- Draw24.9%
- England win59.9%
The model rates England as favourites at 60%, with Norway projected at 15% to win.
▸Goles y marcadores
Likeliest score 0–1 (15.0%) · xG 0.8 - 1.5
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 0–115.0%
- 1–112.6%
- 0–211.8%
- 0–011.2%
- 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
- 0–033.0%
- 0–123.8%
- 1–011.7%
- 1–19.7%
- 0–29.1%
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 goals88.8%
- More than 1.5 goals66.6%
- More than 2.5 goals39.2%
- More than 3.5 goals19.1%
- More than 4.5 goals7.8%
- More than 5.5 goals2.8%
- Both teams score41.9%
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
- Norway clean sheetOpposing team scores zero22.3%
- England clean sheetOpposing team scores zero47.1%
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
- Norway by 4+0.2%
- Norway by 3+1.2%
- Norway by 2+5.5%
- Norway by 1+18.1%
- Draw27.6%
- England by 1+54.3%
- England by 2+28.5%
- England by 3+11.6%
- England by 4+3.8%
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.
▸Cómo se desarrolla el partido
Over 2.5 goals 39.2% · BTTS 41.9%
Game state through the match
- Norway ahead18.8%
- Level26.2%
- England ahead55.0%
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
- 0–1531.3%
- 15–3021.5%
- 30–4514.8%
- 45–6010.2%
- 60–757.0%
- 75–904.8%
- No goal10.5%
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 → | HNorway win | DDraw | AEngland win |
|---|---|---|---|
| HNorway ahead | 10.5% | 4.0% | 1.8% |
| DLevel | 7.3% | 18.3% | 17.7% |
| AEngland ahead | 0.8% | 4.1% | 35.4% |
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
- Norway trail at HT, avoid defeat at FT4.9%
- England trail at HT, avoid defeat at FT5.8%
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.
- Norway51.3%
- England48.7%
- Norway63.2%
- England36.8%
- Norway39.3%
- England60.7%
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: Norway conv 72.0%, save 20.0%; England conv 68.6%, save 22.9%. Smoothed against the global prior with prior strength 20 — see /docs/methodology/.
▸Equipos y jugadores
Top scorer: Haaland (9.3%)
Match detail
Norway
Model-rated key players: Erling Haaland (FW) — P(scores) 9.3%; Alexander Sørloth (FW) — P(scores) 2.4%; Erling Braut Haaland (FW) — P(scores) 1.3%.
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.
England
Model-rated key players: Harry Kane (FW) — P(scores) 3.8%; Marcus Rashford (FW) — P(scores) 5.9%; Ollie Watkins (FW) — P(scores) 1.8%.
England under Thomas Tuchel play a balanced game, holding 55% of the ball — among the highest in the tournament field. Their likely shape is a 4-1-4-1, though they have also used 4-3-3. They apply moderate pressing intensity (PPDA 23.5) and build patiently through midfield with 8.5 passes per attacking sequence. They favour high-quality chances (xG/shot 0.142, among the best in the field).
England will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. Managing the fitness of Tino Livramento could prove decisive — their availability transforms the team's ceiling.
England's predicted XI averages 2,119 club minutes over the 2024-25 season (moderate load).
Norway coverage: 46.0% (7/11 XI matched against the FBref Big-5) · England: 79.0% (11/11).
Norway historically converts 13.6% of xG from set-pieces, contributing 0.10 expected set-piece goals in this fixture. England converts 15.2% from set-pieces (0.23 expected). Combined, the model expects 0.33 set-piece goals across the 90 minutes.
- P(Norway scores set-piece goal) 9.8%
- P(England scores set-piece goal) 20.4%
- P(set-piece goal in match) 28.2%
Norway: Martin Ødegaard on free kicks (per fbref 2022 23) · England: Trent Alexander-Arnold on corners (32 corners), Eberechi Eze on free kicks (per fbref 2022 23)
If a penalty is awarded to Norway, the model gives 72.0% conversion, 68.6% for England. If this match goes to a shootout, the symmetric (coin-toss averaged) win probability is 51.3% Norway / 48.7% England.
Norway primary PK: Erling Haaland (2/2 in 2022-23, per fbref 2022 23) · England primary PK: Marcus Rashford (6/8 in 2019-20, 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
Norway
- Erling HaalandStrikerNo natural backup0.75gap
- Alexander SørlothStrikerNo natural backup0.62gap
- Martin ØdegaardAttacking midfieldCover: Thelo Aasgaard · 0.310.51gap
England
- Marc GuéhiCentre-backCover: Jarell Quansah · 0.650.32gap
- Jude BellinghamAttacking midfieldCover: Morgan Rogers · 0.720.27gap
- Marcus RashfordWingerCover: Anthony Gordon · 0.620.19gap
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 level3 m
- Avg temperatureFive-year mean over the tournament window27.0 °C
- Avg humidity82%
- Heat stressShade WBGT ~30.7 °CHigh heat stress
- Pitch surfacenatural grass
Already plays on natural Bermudagrass; no turf conversion needed.
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)
- Erling HaalandPKFW9.3%
- Alexander SørlothFW2.4%
- Erling Braut HaalandFW1.3%
- Harry KaneFW3.8%
- Marcus RashfordPKFW5.9%
- Ollie WatkinsFW1.8%
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
Norway
vs Ivory Coast · avg 7.4
England
vs DR Congo · avg 7.5
Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →
▸Entre bastidores
Model-by-model comparison
Norway vs England
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 23.5% | 22.0% | 54.4% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 18.1% | 26.5% | 55.4% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 20.2% | 25.9% | 53.9% |
| Bayesian stackingLearned-weight combination | — | 12.9% | 23.5% | 63.6% |
| Ensemble (published)Uniform average + isotonic calibration | — | 15.0% | 24.9% | 60.1% |
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
- France v Spain - who would England rather face in the World Cup final? · Daily Mirror — Football · 14 Jul
- England captain Harry Kane hits out at ITV interviewer Gabriel Clarke 'trying to create division' between Thomas Tuchel and Jude Bellingham with his questioning at the World Cup · Daily Mail — Football · 14 Jul
- England World Cup semi-final and final? A reminder of where and when! · Sky Sports — Football · 14 Jul
- England vs. Argentina ticket prices: How much do World Cup semifinal tickets cost? · USA Today · 14 Jul
- World Cup 2026: American Ismail Elfath to referee England v Argentina · BBC · 14 Jul
- Stage:
- Quarter-final · Match 3
- Date:
- 11 Jul
- Venue:
- Hard Rock Stadium, Miami
a 27°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.Squad availability: 1 carrying a fitness doubt across the two squads. The forecast does not adjust for who is missing: its lineup channel currently contributes zero, so this is context the probabilities do not include.
Norway
Norway come in at close to full strength.
England
England: 1 carrying a fitness doubt.
- DoubtTino Livramento (defender) is carrying Knee injury — a depth-level fitness watch item.
Both projected XIs look intact; the fitness concerns are at squad-depth level rather than among first-choice starters.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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