Quarter-final · Match 3

NorwayvsEngland

2026-07-11·17:00 local·Hard Rock Stadium · MiamiPredictions finalised

Snapshot · 2026-07-14Model 1.0.0Final prediction · locked 11 Jul, 19:43 UTCNorway·England·
Full time · forecast gradedNorway 1 2 EnglandThe locked pre-match forecast has been graded against this result.See the calibration recap →

Match signals

Factors that favour each side, from statistical models to group stage form and match conditions. Longer bars = stronger advantage.

NorwaySignal balanceEngland
15%85%

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

5 England
24%Elo Rating Model54%
StrongStrong

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%.

18%Dixon-Coles Model55%
StrongStrong

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%.

20%Hierarchical Poisson54%
StrongStrong

Groups teams by confederation to share information. Helps for teams with fewer matches. It rates England at 54% to win vs Norway at 20%.

15%Final Ensemble60%
StrongStrong

The published probability after calibration and adjustments. This is what the model says. It rates England at 60% to win vs Norway at 15%.

0/3Model Agreement3/3
StrongStrong

All 3 models agree: England is favoured. When models agree, the signal is stronger.

Tournament Form

3 England
12pts (4W 0D 2L)Tournament Record16pts (5W 1D 0L)
ModerateModerate

England collected 16 points (5W 1D 0L) vs Norway's 12 (4W 0D 2L). A stronger tournament record.

2.17/matchGoals Scored2.17/match
Even

Similar attacking output: Norway 2.17 goals/match, England 2.17.

1.83 conceded/matchDefence1.0 conceded/match
ModerateModerate

England conceded just 1.0 goals/match vs Norway's 1.83. Tighter at the back.

+2Goal Difference+7
ModerateModerate

England's goal difference of +7 is better than Norway's +2. They outperformed opponents by more.

📈Momentum

1 Norway
+30.1Tournament Rating Change+33.4
Even

Both teams' ratings moved similarly during the tournament (Norway +30.1, England +33.4).

-0.0001Player Form Trend-0.0068
ModerateModerate

Norway's players improved their form ratings during the tournament (-0.0001) vs England (-0.0068). Players trending upward.

🏆Team Quality

1 Norway3 England
1912Overall Strength (Elo)2020
ModerateModerate

England is rated 2020 vs Norway's 1912 (gap: 108). That's a significant gap in historical team strength.

0.75 xGExpected Chance Creation1.50 xG
ModerateModerate

The model expects England to create 1.50 expected goals vs Norway's 0.75. More and better chances projected.

0.63Star Power0.26
StrongStrong

Norway's top 3 starters are harder to replace (avg VORP 0.63) than England's (0.26). More star power in key positions.

0.000Squad Familiarity0.049
ModerateModerate

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

1 England
7,449kmTravel Distance7,028km
Even

Similar travel distances for both teams.

6h shiftTimezone Shift5h shift
SlightSlight

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.

예측

Match-outcome probability

  • Norway win
    15.2%
  • Draw
    24.9%
  • England win
    59.9%

The model rates England as favourites at 60%, with Norway projected at 15% to win.

Rank checkFIFA ranks Norway #29 in the world; the model ranks them #16 in this tournament field, 13 places higher than the FIFA list suggests. All 48 compared →
Likeliest score0–115.0%
First goal0-15'31.3%
Both teams score41.9%
Over 2.5 goals39.2%
Top scorerHaaland9.3%
Expected goals0.8 - 1.5
Loading pitch visualisation...

골 및 스코어라인

Likeliest score 0–1 (15.0%) · xG 0.8 - 1.5

Expected goals

Norway
0.75
England
1.50

Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.

Most likely scorelines

  • 0–1
    15.0%
  • 1–1
    12.6%
  • 0–2
    11.8%
  • 0–0
    11.2%
  • 1–2
    8.9%

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

  • 0–0
    33.0%
  • 0–1
    23.8%
  • 1–0
    11.7%
  • 1–1
    9.7%
  • 0–2
    9.1%

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 goals
    88.8%
  • More than 1.5 goals
    66.6%
  • More than 2.5 goals
    39.2%
  • More than 3.5 goals
    19.1%
  • More than 4.5 goals
    7.8%
  • More than 5.5 goals
    2.8%
  • Both teams score
    41.9%

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%
  • Draw
    27.6%
  • 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.

경기 전개 양상

Over 2.5 goals 39.2% · BTTS 41.9%

Game state through the match

0%25%50%75%100%0'15'30'45'60'75'90'
  • 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

  • 0–15
    31.3%
  • 15–30
    21.5%
  • 30–45
    14.8%
  • 45–60
    10.2%
  • 60–75
    7.0%
  • 75–90
    4.8%
  • No goal
    10.5%

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

Joint probability of half-time and full-time results
HT ↓ / FT →HNorway winDDrawAEngland win
HNorway ahead10.5%4.0%1.8%
DLevel7.3%18.3%17.7%
AEngland ahead0.8%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 FT
    4.9%
  • England trail at HT, avoid defeat at FT
    5.8%

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.

Symmetric (averaged over both orderings — used by the bracket simulator)
  • Norway
    51.3%
  • England
    48.7%
If Norway kicks first
  • Norway
    63.2%
  • England
    36.8%
If England kicks first
  • Norway
    39.3%
  • England
    60.7%
Expected paired rounds
4.8
Decided in regulation 5 kicks
73.1%

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/.

팀 및 선수

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%.

How they play

Limited recent tournament data is available for Norway's tactical profile. Early indicators suggest a balanced approach.

What they must execute

Norway will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries.

Storylines
Form trend: Gained 88 international Elo points over the last 12 months — current rating 1964.
Scoring form: Averaged 3.36 xG per match across 8 recent internationals — #1 of 35 in the field for attacking output.
Top scorer: Alexander SørlothModel's top anytime-scorer for the team — 35% probability of scoring at least once, rank #1 of all players.

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%.

How they play

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).

What they must execute

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.

Storylines
Out injured: Tino LivramentoThigh problems, no expected return. Composite 0.94 — would have been a likely starter.
Defensive form: Conceded only 0.44 xG per match across 11 recent internationals — #2 of 35 in the field for defensive solidity.
Top scorer: Harry KaneModel's top anytime-scorer for the team — 32% probability of scoring at least once, rank #2 of all players.
Workload going in

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).

Set-piece outlook

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)

Penalty outlook

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

  1. Erling HaalandStrikerNo natural backup0.75gap
  2. Alexander SørlothStrikerNo natural backup0.62gap
  3. Martin ØdegaardAttacking midfieldCover: Thelo Aasgaard · 0.310.51gap

England

  1. Marc GuéhiCentre-backCover: Jarell Quansah · 0.650.32gap
  2. Jude BellinghamAttacking midfieldCover: Morgan Rogers · 0.720.27gap
  3. 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)

Norway
England

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

8
Erling HaalandST
ATK
DEF
PAS
8
Norway GoalkeeperGK
ATK
DEF
PAS
7
Torbjørn Heggem
ATK
DEF
PAS
7
Martin ØdegaardAM
ATK
DEF
PAS
7
Oscar Bobb
ATK
DEF
PAS

England

vs DR Congo · avg 7.5

9
Harry KaneST
ATK
DEF
PAS
9
Noni MaduekeRW
ATK
DEF
PAS
8
Anthony GordonLW
ATK
DEF
PAS
8
England GKGK
ATK
DEF
PAS
7
Jude BellinghamCM
ATK
DEF
PAS
7
Bukayo SakaRW
ATK
DEF
PAS
6
Marcus RashfordLW
ATK
DEF
PAS
6
Ezri KonsaCB
ATK
DEF
PAS

Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →

분석 내부

Model-by-model comparison

Norway vs England

Moderate (5.4%)
ModelWeightHomeDrawAway
EloRating-based strength estimate32%
23.5%
22.0%
54.4%
Dixon-ColesGoal-process model with low-score correction63%
18.1%
26.5%
55.4%
Hierarchical PoissonBayesian model with confederation pooling6%
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%
Home spread: 5.4%
Draw spread: 4.5%
Away spread: 1.5%
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

Match conditions
Stage:
Quarter-final · Match 3
Date:
11 Jul
Venue:
Hard Rock Stadium, Miami

a 27°C kickoff modestly suppresses expected scoring at this venue.

Beyond the model

Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.

  1. 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. 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.
Availability

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.
What it means

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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