Round of 16 · Match 5

PortugalvsSpain

2026-07-06·14:00 local·AT&T Stadium · DallasPredictions finalised

Snapshot · 2026-07-09Model 1.0.0Final prediction · locked 6 Jul, 16:02 UTCPortugal·Spain·
Full time · forecast gradedPortugal 0 1 SpainThe 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.

PortugalSignal balanceSpain
22%78%

Spain are the clear favourites (49% to Portugal's 26%), and 10 of the wider signals confirm it. A clear probability gap, though draws (25%) keep this from being one-sided.

📊What the Models Say

5 Spain
15%Elo Rating Model63%
StrongStrong

Rates teams by a single strength number updated after every match. Simpler but fast to react. It rates Spain at 63% to win vs Portugal at 15%.

27%Dixon-Coles Model44%
ModerateModerate

Simulates the goal-scoring process using attack and defence strength. The heaviest-weighted model. It rates Spain at 44% to win vs Portugal at 27%.

29%Hierarchical Poisson43%
SlightSlight

Groups teams by confederation to share information. Helps for teams with fewer matches. It rates Spain at 43% to win vs Portugal at 29%.

26%Final Ensemble49%
ModerateModerate

The published probability after calibration and adjustments. This is what the model says. It rates Spain at 49% to win vs Portugal at 26%.

0/3Model Agreement3/3
StrongStrong

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

Tournament Form

3 Spain
8pts (2W 2D 1L)Tournament Record13pts (4W 1D 0L)
StrongStrong

Spain collected 13 points (4W 1D 0L) vs Portugal's 8 (2W 2D 1L). A stronger tournament record.

1.6/matchGoals Scored1.8/match
Even

Similar attacking output: Portugal 1.6 goals/match, Spain 1.8.

0.6 conceded/matchDefence0.0 conceded/match
SlightSlight

Spain conceded just 0.0 goals/match vs Portugal's 0.6. Tighter at the back.

+5Goal Difference+9
ModerateModerate

Spain's goal difference of +9 is better than Portugal's +5. They outperformed opponents by more.

📈Momentum

1 Portugal
+0.7Tournament Rating Change+4.8
Even

Both teams' ratings moved similarly during the tournament (Portugal +0.7, Spain +4.8).

-0.0019Player Form Trend-0.0096
ModerateModerate

Portugal's players improved their form ratings during the tournament (-0.0019) vs Spain (-0.0096). Players trending upward.

🏆Team Quality

2 Portugal2 Spain
1984Overall Strength (Elo)2165
ModerateModerate

Spain is rated 2165 vs Portugal's 1984 (gap: 181). That's a significant gap in historical team strength.

0.94 xGExpected Chance Creation1.26 xG
ModerateModerate

The model expects Spain to create 1.26 expected goals vs Portugal's 0.94. More and better chances projected.

0.43Star Power0.34
SlightSlight

Portugal's top 3 starters are harder to replace (avg VORP 0.43) than Spain's (0.34). More star power in key positions.

0.050Squad Familiarity0.018
ModerateModerate

Portugal's starters play together at club level more often (0.050 cohesion) than Spain's (0.018). More shared understanding on the pitch.

🌍Match Conditions

1 Portugal
7,688kmTravel Distance7,975km
Even

Similar travel distances for both teams.

6h shiftTimezone Shift7h shift
SlightSlight

Portugal face a 6h timezone shift vs Spain'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.

比赛预测

Match-outcome probability

  • Portugal win
    25.4%
  • Draw
    25.0%
  • Spain win
    49.6%

The model rates Spain as favourites at 49%, with Portugal projected at 26% to win.

Likeliest score1–113.9%
First goal0-15'30.7%
Both teams score44.5%
Over 2.5 goals37.7%
Top scorerOyarzabal10.4%
Expected goals0.9 - 1.3
Loading pitch visualisation...

进球与比分

Likeliest score 1–1 (13.9%) · xG 0.9 - 1.3

Expected goals

Portugal
0.94
Spain
1.26

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

Most likely scorelines

  • 1–1
    13.9%
  • 0–1
    13.1%
  • 0–0
    11.9%
  • 1–0
    9.6%
  • 0–2
    8.8%

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
    33.9%
  • 0–1
    20.3%
  • 1–0
    15.1%
  • 1–1
    10.5%
  • 0–2
    6.6%

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
    88.1%
  • More than 1.5 goals
    65.3%
  • More than 2.5 goals
    37.7%
  • More than 3.5 goals
    18.1%
  • More than 4.5 goals
    7.2%
  • More than 5.5 goals
    2.5%
  • Both teams score
    44.5%

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

  • Portugal clean sheetOpposing team scores zero28.4%
  • Spain clean sheetOpposing team scores zero39.0%

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

  • Portugal by 4+
    0.6%
  • Portugal by 3+
    2.6%
  • Portugal by 2+
    9.8%
  • Portugal by 1+
    27.0%
  • Draw
    30.3%
  • Spain by 1+
    42.8%
  • Spain by 2+
    19.6%
  • Spain by 3+
    6.8%
  • Spain by 4+
    1.9%

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.

比赛如何展开

Over 2.5 goals 37.7% · BTTS 44.5%

Game state through the match

0%25%50%75%100%0'15'30'45'60'75'90'
  • Portugal ahead27.8%
  • Level28.7%
  • Spain ahead43.6%

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
    30.7%
  • 15–30
    21.3%
  • 30–45
    14.7%
  • 45–60
    10.2%
  • 60–75
    7.1%
  • 75–90
    4.9%
  • No goal
    11.1%

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 →HPortugal winDDrawASpain win
HPortugal ahead16.1%4.5%1.7%
DLevel10.3%19.8%15.0%
ASpain ahead1.2%4.6%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

  • Portugal trail at HT, avoid defeat at FT
    5.8%
  • Spain trail at HT, avoid defeat at FT
    6.2%

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)
  • Portugal
    56.3%
  • Spain
    43.7%
If Portugal kicks first
  • Portugal
    67.3%
  • Spain
    32.7%
If Spain kicks first
  • Portugal
    45.4%
  • Spain
    54.6%
Expected paired rounds
4.8
Decided in regulation 5 kicks
74.0%

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: Portugal conv 73.3%, save 28.9%Spain conv 72.5%, save 25.0%. Smoothed against the global prior with prior strength 20 — see /docs/methodology/.

球队与球员

Top scorer: Oyarzabal (10.4%)

Match detail

Portugal

Model-rated key players: Cristiano Ronaldo (FW) — P(scores) 9.5%; Gonçalo Ramos (FW) — P(scores) 2.8%; João Félix (FW) — P(scores) 2.6%.

How they play

Portugal under Roberto Martínez play a possession dominant game, holding 59% of the ball — among the highest in the tournament field. Their likely shape is a 4-3-3. They apply moderate pressing intensity (PPDA 21.6) and build patiently through midfield with 7.9 passes per attacking sequence. They generate a high volume of shots (13.5 per 90).

What they must execute

To succeed, Portugal must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match. Managing minutes for Cristiano Ronaldo across what could be seven matches will test the coaching staff's rotation planning.

Storylines
Last dance: Cristiano Ronaldo41 at kickoff with 226 caps — probably his final World Cup.
Top scorer: Gonçalo RamosModel's top anytime-scorer for the team — 30% probability of scoring at least once, rank #6 of all players.
Scoring form: Averaged 2.63 xG per match across 15 recent internationals — #3 of 35 in the field for attacking output.

Spain

Model-rated key players: Mikel Oyarzabal (FW) — P(scores) 10.4%; Ferran Torres (FW) — P(scores) 4.8%; Lamine Yamal (FW) — P(scores) 4.3%.

How they play

Spain under Luis de la Fuente play a possession dominant game, holding 68% of the ball — among the highest in the tournament field. Their likely shape is a 4-3-3. They press intensely (PPDA 15.7, top quartile (4th of 40)) and build patiently through midfield with 10.0 passes per attacking sequence. They generate a high volume of shots (15.3 per 90).

What they must execute

To succeed, Spain must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match.

Storylines
Club core: 8 of 26 predicted-squad players play their club football for Barcelona — a single-club spine on the international side.
Club xG: Squad averages 1.85 xG per match across club football last season — #3 of 20 in the field for attacking pedigree from each player's domestic side (23 of 26 players matched to a known club).
Teen starter: Lamine Yamal18 at kickoff — 25 caps — projected on the bench, the squad's youngest pick.
Workload going in

Portugal's predicted XI averages 2,098 club minutes over the 2024-25 season (moderate load). Spain's predicted XI averages 1,633 club minutes over the 2024-25 season (light load).

Portugal coverage: 78.0% (9/11 XI matched against the FBref Big-5) · Spain: 81.0% (9/11).

Set-piece outlook

Portugal historically converts 17.0% of xG from set-pieces, contributing 0.16 expected set-piece goals in this fixture. Spain converts 17.4% from set-pieces (0.22 expected). Combined, the model expects 0.38 set-piece goals across the 90 minutes.

  • P(Portugal scores set-piece goal) 14.8%
  • P(Spain scores set-piece goal) 19.7%
  • P(set-piece goal in match) 31.6%

Portugal: Pedro Neto on corners (20 corners), Rúben Neves on free kicks (per fbref 2022 23) · Spain: Mikel Oyarzabal on corners (56 corners), Aleix García on free kicks (per fbref 2021 22)

Penalty outlook

If a penalty is awarded to Portugal, the model gives 73.3% conversion, 72.5% for Spain. If this match goes to a shootout, the symmetric (coin-toss averaged) win probability is 56.3% Portugal / 43.7% Spain.

Portugal primary PK: Cristiano Ronaldo (3/3 in 2021-22, per fbref 2022 23) · Spain primary PK: Mikel Oyarzabal (4/5 in 2021-22, per fbref 2021 22).

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

Portugal

  1. Bruno FernandesAttacking midfieldCover: Francisco Trincão · 0.400.56gap
  2. Diogo CostaGoalkeeperCover: Rui Silva · 0.500.50gap
  3. Bernardo SilvaAttacking midfieldCover: Francisco Trincão · 0.400.24gap

Spain

  1. Dani OlmoAttacking midfieldNo natural backup0.51gap
  2. RodriDefensive midfieldCover: Martín Zubimendi · 0.390.27gap
  3. Ferran TorresStrikerCover: Borja Iglesias · 0.650.26gap

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)

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

Portugal

vs Croatia · avg 9.0

9
RonaldoST
ATK
DEF
PAS

Worked well: Their ability to create chances and their resilience in coming back from a deficit were notable strengths.

Struggled: They struggled with the offside trap at times, leading to a disallowed goal.

Spain

vs Austria · avg 8.0

8
Mikel OyarzabalAM
ATK
DEF
PAS
8
Dani OlmoAM
ATK
DEF
PAS
8
Pedro PorroRB
ATK
DEF
PAS
8
Marc CucurellaLB
ATK
DEF
PAS

Worked well: Their ability to create a high volume of chances, combined with effective finishing for three goals, proved decisive. The full-backs' forward runs were particularly impactful.

Struggled: Spain could have been more efficient with their finishing, as several clear-cut opportunities, including shots hitting the woodwork, were not converted.

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

模型细节

Model-by-model comparison

Portugal vs Spain

High disagreement (20.0%)
ModelWeightHomeDrawAway
EloRating-based strength estimate32%
14.6%
22.0%
63.4%
Dixon-ColesGoal-process model with low-score correction63%
27.1%
29.2%
43.7%
Hierarchical PoissonBayesian model with confederation pooling6%
29.0%
27.6%
43.4%
Bayesian stackingLearned-weight combination
20.1%
27.1%
52.8%
Ensemble (published)Uniform average + isotonic calibration
25.9%
25.0%
49.2%
Home spread: 14.4%
Draw spread: 7.2%
Away spread: 20.0%
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:
Round of 16 · Match 5
Date:
6 Jul
Venue:
AT&T Stadium, Dallas

a 29°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.
Availability

Portugal

Portugal come in at close to full strength.

Spain

Spain come in at close to full strength.

What it means

Portugal and Spain 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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