Group H · Matchday 1
Saudi ArabiavsUruguay
2026-06-15·18:00 localPredictions finalised
The forecast
Match-outcome probability
- Saudi Arabia win9.1%
- Draw25.5%
- Uruguay win65.5%
A 324-point Elo gap frames this as a significant mismatch, yet the model still gives Saudi Arabia a 6% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Saudi Arabia
- ·Saudi Arabia has a balanced historical head-to-head record against Uruguay, with 1 win, 1 draw, and 1 loss in 3 matches.
- ·Saudi Arabia's style profile indicates a higher percentile for pressing intensity (73.8 percentile PPDA) compared to Uruguay (71.2 percentile PPDA).
Favoring Uruguay
- ·Uruguay possesses a substantial Elo rating advantage, with a delta of 324 points over Saudi Arabia.
- ·Uruguay is ranked significantly higher globally at 16th, compared to Saudi Arabia's 60th position.
- ·Uruguay's projected expected goals (xG) of 1.57 are considerably higher than Saudi Arabia's 0.51 xG.
- ·The ensemble model predicts a 59.5% win probability for Uruguay, with the ELO model component projecting an even higher 75.6%.
What the model can't fully price
- ·The model does not fully account for squad availability, specifically the 5 players across both teams currently carrying fitness doubts, 3 of whom are projected starters.
Form check
Saudi Arabia
DecliningSaudi Arabia's recent form has been inconsistent, marked by four losses in their last six matches, including a 0-4 defeat. While they secured two wins in the Arab Cup (2-1, 3-1), their most recent friendly results indicate a challenging period.
Conceded 11 goals in their last 6 matches.
Uruguay
SteadyUruguay's recent performances have been steady but not dominant, recording two wins and three draws in their last six matches. They have shown defensive resilience with two clean sheets, though a 1-5 friendly loss stands out as an anomaly.
Secured 3 draws in their last 6 matches.
Analysis
How it plays out
Both sides run a balanced system, so this becomes a test of who executes the same ideas better on the day.
What decides it
Darwin Núñez's 8.3% scoring probability is the highest in this fixture. Containing that output is Saudi Arabia's primary defensive task.
Off the pitch
No major off-pitch asymmetries. This one is decided by the football.
The angle
The model gives Saudi Arabia just 12.3% to win. Every World Cup produces group-stage upsets; the question is whether this fixture is one of them.
▸Goals & scorelines
Likeliest score 0–1 (20.8%) · xG 0.4 - 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
- 0–120.8%
- 0–216.2%
- 0–014.7%
- 1–110.1%
- 0–38.2%
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
- 0–037.9%
- 0–128.0%
- 0–210.8%
- 1–08.0%
- 1–16.7%
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 goals85.3%
- More than 1.5 goals58.9%
- More than 2.5 goals31.2%
- More than 3.5 goals13.6%
- More than 4.5 goals4.9%
- More than 5.5 goals1.5%
- Both teams score28.6%
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
- Saudi Arabia clean sheetOpposing team scores zero22.0%
- Uruguay clean sheetOpposing team scores zero64.0%
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
- Saudi Arabia by 4+<0.1%
- Saudi Arabia by 3+0.3%
- Saudi Arabia by 2+2.0%
- Saudi Arabia by 1+10.1%
- Draw26.5%
- Uruguay by 1+63.4%
- Uruguay by 2+34.6%
- Uruguay by 3+14.5%
- Uruguay by 4+4.9%
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.
▸How the match unfolds
Over 2.5 goals 31.2% · BTTS 28.6%
Game state through the match
- Saudi Arabia ahead10.7%
- Level25.3%
- Uruguay ahead64.0%
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
- 0–1527.9%
- 15–3020.1%
- 30–4514.5%
- 45–6010.5%
- 60–757.5%
- 75–905.4%
- No goal14.1%
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
| HT ↓ / FT → | HSaudi Arabia win | DDraw | AUruguay win |
|---|---|---|---|
| HSaudi Arabia ahead | 5.6% | 2.8% | 1.3% |
| DLevel | 4.6% | 19.8% | 20.5% |
| AUruguay ahead | 0.3% | 2.9% | 42.1% |
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
- Saudi Arabia trail at HT, avoid defeat at FT3.2%
- Uruguay trail at HT, avoid defeat at FT4.1%
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: Núñez (8.3%)
Match detail
Saudi Arabia
Model-rated key players: Abdullah Al-Hamdan (FW) — P(scores) 3.4%; Firas Al-Buraikan (FW) — P(scores) 3.4%; Saleh Al-Shehri (FW) — P(scores) 3.4%.
Saudi Arabia under Georgios Donis play a balanced game with 52% possession. Their likely shape is a 4-1-4-1, though they have also used 4-3-3. They apply moderate pressing intensity (PPDA 17.8). They are selective in their shooting (10.1 per 90).
Saudi Arabia will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. With Georgios Donis appointed relatively recently (161 days before kickoff), building tactical cohesion in limited preparation time is the immediate challenge.
Uruguay
Model-rated key players: Darwin Núñez (FW) — P(scores) 8.3%; Brian Rodríguez (FW) — P(scores) 4.1%; Rodrigo Aguirre (FW) — P(scores) 3.9%.
Uruguay under Marcelo Bielsa play a balanced game with 49% possession. Their likely shape is a 3-5-2, though they have also used 4-3-3 and 4-4-2. They apply moderate pressing intensity (PPDA 18.0) and move the ball forward quickly at 5.7 passes per attack.
Uruguay will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries.
Uruguay converts 15.4% from set-pieces (0.23 expected). Combined, the model expects 0.23 set-piece goals across the 90 minutes.
- P(Uruguay scores set-piece goal) 20.9%
- P(set-piece goal in match) 20.9%
Uruguay: Nahitan Nández on corners (5 corners), Lucas Torreira on free kicks (per fbref 2021 22)
If a penalty is awarded to Saudi Arabia, the model gives 70.0% conversion, 73.3% for Uruguay.
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
- 17.8
- Possession
- 52%
- Directness (yds/pass)
- 6.2
- Long balls/90
- 36
- Set-piece xG
- —
- PPDA
- 18.0
- Possession
- 49%
- Directness (yds/pass)
- 7.6
- Long balls/90
- 35
- Set-piece xG
- 15%
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
Saudi Arabia
- Firas Al-BuraikanStrikerCover: Abdullah Al-Salem · 0.050.51gap
- Abdullah Al-HamdanStrikerCover: Abdullah Al-Salem · 0.050.30gap
- Salem Al-DawsariWingerCover: Saleh Abu Al-Shamat · 0.030.29gap
Uruguay
- Nicolás de la CruzCentral midfieldNo natural backup0.53gap
- Darwin NúñezStrikerCover: Agustín Álvarez · 0.520.46gap
- Federico ValverdeCentral midfieldNo natural backup0.45gap
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)
- Abdullah Al-HamdanFW3.4%
- Firas Al-BuraikanFW3.4%
- Saleh Al-ShehriFW3.4%
- Darwin NúñezFW8.3%
- Brian RodríguezFW4.1%
- Rodrigo AguirreFW3.9%
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
Saudi Arabia
vs Cape Verde · avg 6.7
Uruguay
vs Spain · avg 8.5
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.
9Mohammed Al-Owais4'–92'Delivered an outstanding performance with multiple crucial saves, single-handedly securing a point for Saudi Arabia.
8saves▼
Delivered an outstanding performance with multiple crucial saves, single-handedly securing a point for Saudi Arabia.
Match timeline
7Abdulelah Al-Amri40'–43'Scored Saudi Arabia's only goal from a set piece but was also booked for a challenge.
2goals2headers1 yellow▼
Scored Saudi Arabia's only goal from a set piece but was also booked for a challenge.
Match timeline
6Al-ShabatRegistered a shot on goal but had no further notable impact on the match.
Registered a shot on goal but had no further notable impact on the match.
8Maxi AraújoScored the crucial equalizer and was a persistent threat in attack for Uruguay.
Scored the crucial equalizer and was a persistent threat in attack for Uruguay.
7Fernando Muslera37'–37'Made a crucial save to prevent Saudi Arabia from scoring earlier in the first half.
1saves▼
Made a crucial save to prevent Saudi Arabia from scoring earlier in the first half.
Match timeline
6Federico Valverde35'–90'Contributed to Uruguay's attack with a late shot but also committed a foul.
1shots1on target1fouls▼
Contributed to Uruguay's attack with a late shot but also committed a foul.
Match timeline
Match observations
- Saudi Arabia secured an early lead from a set-piece, showcasing their effectiveness in dead-ball situations.
- Uruguay dominated the second half, applying sustained pressure and creating numerous scoring opportunities.
- The Saudi Arabia goalkeeper was instrumental in earning a point for his team, making several impressive saves against Uruguay's relentless attacks.
▸Under the hood
Model-by-model comparison
Saudi Arabia vs Uruguay
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 6.0% | 22.0% | 72.0% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 10.4% | 26.6% | 63.0% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 11.5% | 26.6% | 62.0% |
| Bayesian stackingLearned-weight combination | — | 3.5% | 24.5% | 72.0% |
| Ensemble (published)Uniform average + isotonic calibration | — | 5.9% | 26.8% | 67.3% |
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(Saudi Arabia win)9.1%
- + Lineup contribution0.0pp
- + Style-matchup contribution+0.0pp
- Published P(Saudi Arabia win)9.1%
Decomposition of the published P(Saudi Arabia 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 |
|---|---|---|---|---|---|
| 15 Jun 2026 | FIFA World Cup | NMiami Gardens | 1–1 | D | — |
| 20 Jun 2018 | FIFA World Cup | NRostov-on-Don | 0–1 | L | 0.3–1.6 |
| 10 Oct 2014 | Friendly | HJeddah | 1–1 | D | — |
| 27 Mar 2002 | Friendly | HDammam | 3–2 | W | — |
Saudi Arabia vs Uruguay, every senior international meeting in the martj42 results dataset (score from Saudi Arabia's perspective; H/A/N = home/away/neutral; xG where the upstream dataset covers the match).
Latest news & match context
No recent headlines for Saudi Arabia or Uruguay.
- Stage:
- Group H · Matchday 1
- Date:
- 15 Jun
Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.
- 1.Squad availability: 1 carrying a fitness doubt across the two squads, 1 of them projected starters. The forecast does not adjust for who is missing: its lineup channel currently contributes zero, so this is context the probabilities do not include.
Saudi Arabia
Saudi Arabia come in at close to full strength.
Uruguay
Uruguay: 1 carrying a fitness doubt.
- DoubtGiorgian de Arrascaeta, the second-choice midfielder, is recovering from Muscle injury and is a fitness watch item; if unavailable the projected XI shifts.
Availability runs in Saudi Arabia's favour here: Uruguay are managing a fitness concern over Giorgian de Arrascaeta, while Saudi Arabia's projected XI looks intact.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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