Group K · Matchday 2
PortugalvsUzbekistan
2026-06-23·12:00 localPredictions finalised
The forecast
Match-outcome probability
- Portugal win62.2%
- Draw25.1%
- Uzbekistan win12.6%
A clash of identities: Portugal's possession-dominant approach meets Uzbekistan's balanced style in a fixture the model gives to Portugal at 78%.
Why the model says this
Favoring Portugal
- ·Portugal holds a significant Elo advantage of 257 points over Uzbekistan.
- ·Portugal is ranked 6th globally by FIFA, substantially higher than Uzbekistan at 50th.
- ·The model projects Portugal to generate 1.92 expected goals (xG) compared to Uzbekistan's 0.54 xG, indicating a strong offensive advantage.
- ·All underlying models (Elo, DC, HP, Stacking) show a strong preference for a Portugal win, with probabilities ranging from 69.6% to 72.1% before ensemble averaging.
Favoring Uzbekistan
- ·Uzbekistan has conceded 3 goals in their last six matches, a lower total than Portugal's 5 goals conceded over the same period.
- ·They have secured two 0-0 draws in their last six fixtures, indicating an ability to hold opponents scoreless.
- ·Uzbekistan recorded a 3-1 victory in the FIFA Series, demonstrating their capacity to score multiple goals.
What the model can't fully price
- ·The model does not fully account for squad availability; 4 players across both squads are carrying fitness doubts, with 1 projected starter among them, which is not factored into the probabilities.
Form check
Portugal
SteadyPortugal's recent form shows 3 wins, 2 draws, and 1 loss in their last six matches, including a dominant 9-1 victory in World Cup qualification. They have scored 14 goals and conceded 5 in this period.
14 goals scored in last 6 matches
Uzbekistan
SteadyUzbekistan's recent run includes 2 wins, 3 draws, and 1 loss from their last six fixtures. They have shown defensive resilience with two 0-0 draws and conceded only 3 goals in this period.
3 goals conceded in last 6 matches
Analysis
How it plays out
Portugal will dominate the ball. Whether Uzbekistan can stay organised through long spells without it determines if Portugal's possession converts to chances. Portugal will expect to hold 59% possession. Uzbekistan need their shape to stay compact without the ball and be clinical when they win it back.
What decides it
Portugal's possession game (59% avg) requires patience in the final third and quick ball recovery when they lose it. Cristiano Ronaldo's 11.1% scoring probability is the highest in this fixture. Containing that output is Uzbekistan's primary defensive task.
Off the pitch
No major off-pitch asymmetries. This one is decided by the football.
The angle
The model gives Uzbekistan just 12.6% to win. Every World Cup produces group-stage upsets; the question is whether this fixture is one of them.
▸Goals & scorelines
Likeliest score 2–0 (16.9%) · xG 2.2 - 0.5
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 2–016.9%
- 1–015.3%
- 3–012.1%
- 2–17.9%
- 1–17.7%
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
- 1–028.7%
- 0–027.4%
- 2–015.7%
- 1–17.2%
- 0–15.9%
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 goals92.3%
- More than 1.5 goals74.1%
- More than 2.5 goals48.6%
- More than 3.5 goals26.8%
- More than 4.5 goals12.5%
- More than 5.5 goals5.0%
- Both teams score33.3%
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 zero62.8%
- Uzbekistan clean sheetOpposing team scores zero11.6%
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+12.9%
- Portugal by 3+28.4%
- Portugal by 2+51.7%
- Portugal by 1+76.3%
- Draw17.5%
- Uzbekistan by 1+6.2%
- Uzbekistan by 2+1.3%
- Uzbekistan by 3+0.2%
- Uzbekistan by 4+<0.1%
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 48.6% · BTTS 33.3%
Game state through the match
- Portugal ahead76.7%
- Level16.6%
- Uzbekistan ahead6.7%
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
- 0–1535.4%
- 15–3022.9%
- 30–4514.8%
- 45–609.6%
- 60–756.2%
- 75–904.0%
- No goal7.3%
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 → | HPortugal win | DDraw | AUzbekistan win |
|---|---|---|---|
| HPortugal ahead | 54.7% | 2.4% | 0.3% |
| DLevel | 20.3% | 12.0% | 2.8% |
| AUzbekistan ahead | 1.7% | 2.3% | 3.5% |
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 FT4.1%
- Uzbekistan trail at HT, avoid defeat at FT2.7%
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: Ronaldo (11.1%)
Match detail
Portugal
Model-rated key players: Cristiano Ronaldo (FW) — P(scores) 11.1%; Gonçalo Ramos (FW) — P(scores) 4.3%; João Félix (FW) — P(scores) 4.0%.
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).
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.
Uzbekistan
Model-rated key players: Eldor Shomurodov (FW) — P(scores) 5.5%; Abbosbek Fayzullaev (FW) — P(scores) 4.0%; Dostonbek Khamdamov (FW) — P(scores) 4.0%.
Limited recent tournament data is available for Uzbekistan's tactical profile. Early indicators suggest a balanced approach.
Uzbekistan will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries.
Portugal's predicted XI averages 2,098 club minutes over the 2024-25 season (moderate load).
Portugal coverage: 78.0% (9/11 XI matched against the FBref Big-5) · Uzbekistan: 4.0% (1/11).
Portugal historically converts 17.0% of xG from set-pieces, contributing 0.36 expected set-piece goals in this fixture. Combined, the model expects 0.36 set-piece goals across the 90 minutes.
- P(Portugal scores set-piece goal) 30.6%
- P(set-piece goal in match) 30.6%
Portugal: Pedro Neto on corners (20 corners), Rúben Neves on free kicks (per fbref 2022 23)
If a penalty is awarded to Portugal, the model gives 73.3% conversion, 76.0% for Uzbekistan.
Portugal primary PK: Cristiano Ronaldo (3/3 in 2021-22, 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
- PPDA
- 21.6
- Possession
- 59%
- Directness (yds/pass)
- 4.5
- Long balls/90
- 30
- Set-piece xG
- 17%
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
- 44%
- Directness (yds/pass)
- —
- Long balls/90
- —
- Set-piece xG
- —
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
Portugal
- Bruno FernandesAttacking midfieldCover: Francisco Trincão · 0.400.56gap
- Diogo CostaGoalkeeperCover: Rui Silva · 0.500.50gap
- Bernardo SilvaAttacking midfieldCover: Francisco Trincão · 0.400.24gap
Uzbekistan
- Abdukodir KhusanovCentre-backCover: Umar Eshmurodov · 0.280.53gap
- Eldor ShomurodovStrikerNo natural backup0.22gap
- Odiljon HamrobekovDefensive midfieldCover: Abdulla Abdullaev · 0.310.03gap
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 level13 m
- Avg temperatureFive-year mean over the tournament window28.4 °C
- Avg humidity78%
- Heat stressShade WBGT ~31.8 °CHigh heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf 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. 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)
- Cristiano RonaldoPKFW11.1%
- Gonçalo RamosFW4.3%
- João FélixFW4.0%
- Eldor ShomurodovFW5.5%
- Abbosbek FayzullaevFW4.0%
- Dostonbek KhamdamovFW4.0%
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
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.
Uzbekistan
vs DR Congo · avg 6.8
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.
9RonaldoScored multiple goals, demonstrating clinical finishing and leading the attack for Portugal.
1goals▼
Scored multiple goals, demonstrating clinical finishing and leading the attack for Portugal.
Match timeline
3NemotovConceded multiple goals and scored an unfortunate own goal under immense pressure from the opposition.
Conceded multiple goals and scored an unfortunate own goal under immense pressure from the opposition.
Match observations
- The match was dominated by Portugal from the outset, showcasing high pressing and relentless attacking pressure.
- Portugal's clinical finishing allowed them to build a significant lead early and maintain control throughout the game.
- Uzbekistan attempted quick counter-attacks but found it difficult to penetrate Portugal's solid defence.
▸Under the hood
Model-by-model comparison
Portugal vs Uzbekistan
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 73.7% | 22.0% | 4.3% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 76.1% | 17.5% | 6.4% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 74.8% | 17.7% | 7.5% |
| Bayesian stackingLearned-weight combination | — | 84.1% | 15.9% | 0.0% |
| Ensemble (published)Uniform average + isotonic calibration | — | 78.1% | 19.0% | 2.9% |
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(Portugal win)65.2%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Portugal win)65.2%
Decomposition of the published P(Portugal 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 |
|---|---|---|---|---|---|
| 23 Jun 2026 | FIFA World Cup | NHouston | 5–0 | W | — |
Portugal vs Uzbekistan, every senior international meeting in the martj42 results dataset (score from Portugal's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for Portugal or Uzbekistan.
- Stage:
- Group K · Matchday 2
- Date:
- 23 Jun
Portugal
Portugal come in at close to full strength.
Uzbekistan
Uzbekistan come in at close to full strength.
Portugal and Uzbekistan 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/.
Standard Pass
This match is a free preview
You're seeing the model's full forecast for this fixture for free. Unlock the same depth: probabilities, expected goals, scoreline distributions, and per-player scoring, for all 104 matches with a Standard Pass, valid through the tournament.
Every forecast graded against the real result, scored on 987 matches since 2014. See the scorecard.
24h money-back, no questions asked·No subscription, no auto-renewal·Access through 31 Dec 2026. See refund policy.