Quarter-final · Match 2
SpainvsBelgium
2026-07-10·12:00 local·SoFi Stadium · Los AngelesPredictions finalised
Match signals
Factors that favour each side, from statistical models to group stage form and match conditions. Longer bars = stronger advantage.
Spain are strong favourites at 57% vs Belgium's 19%. Most signals point the same way. Belgium will need to outperform their rating.
📊What the Models Say
Rates teams by a single strength number updated after every match. Simpler but fast to react. It rates Spain at 73% to win vs Belgium at 5%.
Simulates the goal-scoring process using attack and defence strength. The heaviest-weighted model. It rates Spain at 53% to win vs Belgium at 21%.
Groups teams by confederation to share information. Helps for teams with fewer matches. It rates Spain at 49% to win vs Belgium at 25%.
The published probability after calibration and adjustments. This is what the model says. It rates Spain at 57% to win vs Belgium at 19%.
All 3 models agree: Spain is favoured. When models agree, the signal is stronger.
⚽Tournament Form
Spain collected 16 points (5W 1D 0L) vs Belgium's 11 (3W 2D 1L). A stronger tournament record.
Belgium averaged 2.33 goals per match vs Spain's 1.83. More firepower coming in.
Spain conceded just 0.17 goals/match vs Belgium's 1.17. Tighter at the back.
Spain's goal difference of +10 is better than Belgium's +7. They outperformed opponents by more.
📈Momentum
Belgium's rating rose +19.5 during the tournament while Spain's moved +10.6. The tournament has been kinder to Belgium.
Belgium's players improved their form ratings during the tournament (+0.0046) vs Spain (-0.0096). Players trending upward.
🏆Team Quality
Spain is rated 2165 vs Belgium's 1867 (gap: 298). That's a very large gap in historical team strength.
The model expects Spain to create 1.61 expected goals vs Belgium's 0.92. More and better chances projected.
Similar star-player quality in both squads.
Similar levels of squad familiarity from club football.
🌍Match Conditions
Similar travel distances for both teams.
16 signals across 5 categories. Signal strength reflects how large the gap is between the two teams on each factor. Signals are descriptive, not prescriptive.
The forecast
Match-outcome probability
- Spain win56.9%
- Draw23.9%
- Belgium win19.2%
A 298-point Elo gap frames this as a significant mismatch, yet the model still gives Belgium a 19% probability of a result — enough to make this more than a formality.
▸Goals & scorelines
Likeliest score 1–1 (12.5%) · xG 1.6 - 0.9
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–112.5%
- 1–012.1%
- 2–010.3%
- 2–19.5%
- 0–08.7%
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
- 0–028.8%
- 1–022.1%
- 0–112.4%
- 1–111.1%
- 2–09.1%
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 goals91.3%
- More than 1.5 goals72.6%
- More than 2.5 goals46.4%
- More than 3.5 goals24.9%
- More than 4.5 goals11.3%
- More than 5.5 goals4.4%
- Both teams score48.9%
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
- Spain clean sheetOpposing team scores zero39.8%
- Belgium clean sheetOpposing team scores zero20.0%
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
- Spain by 4+4.2%
- Spain by 3+12.1%
- Spain by 2+28.5%
- Spain by 1+52.8%
- Draw26.4%
- Belgium by 1+20.9%
- Belgium by 2+7.3%
- Belgium by 3+1.9%
- Belgium by 4+0.4%
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 46.4% · BTTS 48.9%
Game state through the match
- Spain ahead53.5%
- Level24.9%
- Belgium ahead21.6%
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
- 0–1534.4%
- 15–3022.6%
- 30–4514.8%
- 45–609.7%
- 60–756.4%
- 75–904.2%
- No goal8.0%
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 → | HSpain win | DDraw | ABelgium win |
|---|---|---|---|
| HSpain ahead | 34.5% | 4.5% | 1.1% |
| DLevel | 16.7% | 16.3% | 8.0% |
| ABelgium ahead | 2.1% | 4.4% | 12.3% |
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
- Spain trail at HT, avoid defeat at FT6.6%
- Belgium trail at HT, avoid defeat at FT5.6%
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.
- Spain54.3%
- Belgium45.7%
- Spain66.1%
- Belgium33.9%
- Spain42.7%
- Belgium57.3%
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: Spain conv 72.5%, save 25.0%; Belgium conv 71.4%, save 22.9%. Smoothed against the global prior with prior strength 20 — see /docs/methodology/.
▸Teams & players
Top scorer: Oyarzabal (10.8%)
Match detail
Spain
Model-rated key players: Mikel Oyarzabal (FW) — P(scores) 10.8%; Ferran Torres (FW) — P(scores) 5.3%; Lamine Yamal (FW) — P(scores) 4.8%.
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).
To succeed, Spain must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match.
Belgium
Model-rated key players: Kevin De Bruyne (MF) — P(scores) 6.7%; Loïs Openda (FW) — P(scores) 3.3%; Leandro Trossard (FW) — P(scores) 2.1%.
Belgium under Rudi Garcia play a balanced game, holding 54% of the ball — among the highest in the tournament field. Their likely shape is a other, though they have also used 4-2-3-1. They apply moderate pressing intensity (PPDA 23.1) and build patiently through midfield with 7.7 passes per attacking sequence.
Belgium will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. Managing minutes for Axel Witsel across what could be seven matches will test the coaching staff's rotation planning.
Spain's predicted XI averages 1,633 club minutes over the 2024-25 season (light load).
Spain coverage: 81.0% (9/11 XI matched against the FBref Big-5) · Belgium: 58.0% (10/11).
Spain historically converts 17.4% of xG from set-pieces, contributing 0.28 expected set-piece goals in this fixture. Belgium converts 14.6% from set-pieces (0.14 expected). Combined, the model expects 0.42 set-piece goals across the 90 minutes.
- P(Spain scores set-piece goal) 24.5%
- P(Belgium scores set-piece goal) 12.6%
- P(set-piece goal in match) 34.0%
Spain: Mikel Oyarzabal on corners (56 corners), Aleix García on free kicks (per fbref 2021 22) · Belgium: Kevin De Bruyne on corners (25 corners), Axel Witsel on free kicks (per fbref 2022 23)
If a penalty is awarded to Spain, the model gives 72.5% conversion, 71.4% for Belgium. If this match goes to a shootout, the symmetric (coin-toss averaged) win probability is 54.3% Spain / 45.7% Belgium.
Spain primary PK: Mikel Oyarzabal (4/5 in 2021-22, per fbref 2021 22) · Belgium primary PK: Kevin De Bruyne (2/3 in 2020-21, 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
Spain
- Dani OlmoAttacking midfieldNo natural backup0.51gap
- RodriDefensive midfieldCover: Martín Zubimendi · 0.390.27gap
- Ferran TorresStrikerCover: Borja Iglesias · 0.650.26gap
Belgium
- Youri TielemansCentral midfieldNo natural backup0.41gap
- Romelu LukakuStrikerNo natural backup0.37gap
- Zeno DebastCentre-backCover: Brandon Mechele · 0.560.32gap
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 level26 m
- Avg temperatureFive-year mean over the tournament window20.8 °C
- Avg humidity70%
- Heat stressShade WBGT ~22.5 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf stadium; natural grass is grown on a drainage-tray system over the turf under the translucent roof.
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)
- Mikel OyarzabalPKFW10.8%
- Ferran TorresFW5.3%
- Lamine YamalFW4.8%
- Kevin De BruynePKMF6.7%
- Loïs OpendaFW3.3%
- Leandro TrossardFW2.1%
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
Spain
vs Austria · avg 8.0
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.
Belgium
vs Senegal · avg 8.0
Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →
▸Under the hood
Model-by-model comparison
Spain vs Belgium
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 73.1% | 22.0% | 4.9% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 53.3% | 25.9% | 20.9% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 49.4% | 25.7% | 24.9% |
| Bayesian stackingLearned-weight combination | — | 65.9% | 24.5% | 9.7% |
| Ensemble (published)Uniform average + isotonic calibration | — | 56.9% | 23.9% | 19.2% |
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
- Folarin Balogun tells Donald Trump his World Cup red card intervention DID impact USA team before Belgium loss · Daily Mail — Football · 14 Jul
- France vs. Spain, 2026 World Cup semifinals: Match thread and discussion · Stars and Stripes FC · 14 Jul
- World Cup Watch Thread: Semi Finals | France vs Spain · Royal Blue Mersey · 14 Jul
- France v Spain - who would England rather face in the World Cup final? · Daily Mirror — Football · 14 Jul
- What color jerseys are France and Spain wearing today? World Cup kit reveal · Yahoo Sports Australia · 14 Jul
- Stage:
- Quarter-final · Match 2
- Date:
- 10 Jul
- Venue:
- SoFi Stadium, Los Angeles
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.
Spain and Belgium 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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