Quarter-final · Match 1
FrancevsMorocco
2026-07-09·16:00 local·Gillette Stadium · BostonPredictions finalised
Match signals
Factors that favour each side, from statistical models to group stage form and match conditions. Longer bars = stronger advantage.
France are strong favourites at 50% vs Morocco's 20%. Most signals point the same way. Morocco 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 France at 70% to win vs Morocco at 8%.
Simulates the goal-scoring process using attack and defence strength. The heaviest-weighted model. It rates France at 41% to win vs Morocco at 24%.
Groups teams by confederation to share information. Helps for teams with fewer matches. It rates France at 44% to win vs Morocco at 24%.
The published probability after calibration and adjustments. This is what the model says. It rates France at 50% to win vs Morocco at 20%.
All 3 models agree: France is favoured. When models agree, the signal is stronger.
⚽Tournament Form
France collected 18 points (6W 0D 0L) vs Morocco's 11 (3W 2D 1L). A stronger tournament record.
France averaged 2.67 goals per match vs Morocco's 1.67. More firepower coming in.
France conceded just 0.33 goals/match vs Morocco's 1.0. Tighter at the back.
France's goal difference of +14 is better than Morocco's +4. They outperformed opponents by more.
📈Momentum
Both teams' ratings moved similarly during the tournament (France +41.4, Morocco +44.8).
Morocco's players improved their form ratings during the tournament (+0.0000) vs France (-0.0074). Players trending upward.
🏆Team Quality
France is rated 2081 vs Morocco's 1822 (gap: 259). That's a very large gap in historical team strength.
The model expects France to create 1.02 expected goals vs Morocco's 0.72. More and better chances projected.
Morocco's top 3 starters are harder to replace (avg VORP 0.67) than France's (0.30). More star power in key positions.
France's starters play together at club level more often (0.052 cohesion) than Morocco's (0.000). More shared understanding on the pitch.
🌍Match Conditions
Similar travel distances for both teams.
Morocco face a 5h timezone shift vs France'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.
Die Prognose
Match-outcome probability
- France win49.9%
- Draw30.5%
- Morocco win19.6%
A 259-point Elo gap frames this as a significant mismatch, yet the model still gives Morocco a 20% probability of a result — enough to make this more than a formality.
▸Tore & Spielstände
Likeliest score 0–0 (18.3%) · xG 1.0 - 0.7
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 0–018.3%
- 1–017.1%
- 1–113.7%
- 0–111.8%
- 2–09.1%
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
- 0–042.3%
- 1–020.9%
- 0–114.6%
- 1–18.2%
- 2–05.5%
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 goals81.7%
- More than 1.5 goals52.8%
- More than 2.5 goals25.5%
- More than 3.5 goals10.0%
- More than 4.5 goals3.3%
- More than 5.5 goals0.9%
- Both teams score33.7%
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
- France clean sheetOpposing team scores zero48.5%
- Morocco clean sheetOpposing team scores zero36.0%
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
- France by 4+1.1%
- France by 3+4.9%
- France by 2+16.4%
- France by 1+41.0%
- Draw34.5%
- Morocco by 1+24.5%
- Morocco by 2+7.4%
- Morocco by 3+1.6%
- Morocco by 4+0.3%
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.
▸Wie das Spiel sich entwickelt
Over 2.5 goals 25.5% · BTTS 33.7%
Game state through the match
- France ahead41.8%
- Level33.0%
- Morocco ahead25.3%
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
- 0–1525.2%
- 15–3018.9%
- 30–4514.1%
- 45–6010.5%
- 60–757.9%
- 75–905.9%
- No goal17.5%
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
| HT ↓ / FT → | HFrance win | DDraw | AMorocco win |
|---|---|---|---|
| HFrance ahead | 25.1% | 4.0% | 0.8% |
| DLevel | 15.4% | 25.4% | 10.1% |
| AMorocco ahead | 1.1% | 3.9% | 14.3% |
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
- France trail at HT, avoid defeat at FT5.1%
- Morocco trail at HT, avoid defeat at FT4.7%
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.
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.
- France46.9%
- Morocco53.1%
- France58.9%
- Morocco41.1%
- France34.9%
- Morocco65.1%
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: France conv 73.3%, save 24.4%; Morocco conv 74.3%, save 25.7%. Smoothed against the global prior with prior strength 20 — see /docs/methodology/.
▸Teams & Spieler
Top scorer: Thuram (8.7%)
Match detail
France
Model-rated key players: Marcus Thuram (FW) — P(scores) 8.7%; Kylian Mbappé (FW) — P(scores) 4.3%; Bradley Barcola (FW) — P(scores) 3.5%.
France under Didier Deschamps play a balanced game with 51% possession. Their likely shape is a 4-2-3-1, though they have also used 4-3-3. They sit deeper and pick their moments to press (PPDA 26.1) and build patiently through midfield with 7.5 passes per attacking sequence.
France will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. Managing the fitness of Kylian Mbappé could prove decisive — their availability transforms the team's ceiling.
Morocco
Model-rated key players: Sofyan Amrabat (MF) — P(scores) 6.1%; Youssef En-Nesyri (FW) — P(scores) 2.9%; Ayoub El Kaabi (FW) — P(scores) 2.1%.
Morocco under Mohamed Ouahbi play a counter attacker game with 46% possession. Their likely shape is a 4-3-3, though they have also used other. They apply moderate pressing intensity (PPDA 22.2).
Morocco rely on defensive discipline and quick transitions — absorbing pressure and converting turnovers into attacking chances. Concentration and defensive organisation for full 90-minute stretches will determine whether the approach holds against top opposition. With Mohamed Ouahbi appointed relatively recently (161 days before kickoff), building tactical cohesion in limited preparation time is the immediate challenge.
France's predicted XI averages 2,336 club minutes over the 2024-25 season (moderate load).
France coverage: 92.0% (11/11 XI matched against the FBref Big-5) · Morocco: 32.0% (7/11).
France historically converts 16.4% of xG from set-pieces, contributing 0.17 expected set-piece goals in this fixture. Morocco converts 11.8% from set-pieces (0.09 expected). Combined, the model expects 0.25 set-piece goals across the 90 minutes.
- P(France scores set-piece goal) 15.5%
- P(Morocco scores set-piece goal) 8.2%
- P(set-piece goal in match) 22.4%
France: Florian Thauvin on corners (70 corners) (per fbref 2020 21) · Morocco: Mounir Chouiar on corners (26 corners), Sofyan Amrabat on free kicks (per fbref 2020 21)
If a penalty is awarded to France, the model gives 73.3% conversion, 74.3% for Morocco. If this match goes to a shootout, the symmetric (coin-toss averaged) win probability is 46.9% France / 53.1% Morocco.
France primary PK: Marcus Thuram (4/5 in 2018-19, per fbref 2020 21) · Morocco primary PK: Sofyan Amrabat (1/1 in 2019-20, per fbref 2020 21).
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
France
- N'Golo KantéDefensive midfieldNo natural backup0.43gap
- Aurélien TchouaméniDefensive midfieldNo natural backup0.26gap
- Kylian MbappéStrikerCover: Jean-Philippe Mateta · 0.770.21gap
Morocco
- Nayef AguerdCentre-backCover: Chadi Riad · 0.000.85gap
- Issa DiopCentre-backCover: Chadi Riad · 0.000.85gap
- Ayoub El KaabiStrikerNo natural backup0.33gap
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 level67 m
- Avg temperatureFive-year mean over the tournament window21.8 °C
- Avg humidity76%
- Heat stressShade WBGT ~24.1 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Artificial-turf NFL 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)
- Marcus ThuramPKFW8.7%
- Kylian MbappéFW4.3%
- Bradley BarcolaFW3.5%
- Sofyan AmrabatPKMF6.1%
- Youssef En-NesyriFW2.9%
- Ayoub El KaabiFW2.1%
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
France
vs Sweden · avg 7.6
Morocco
vs Netherlands · avg 7.0
Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →
▸Hinter den Kulissen
Model-by-model comparison
France vs Morocco
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 70.4% | 22.0% | 7.6% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 41.2% | 34.4% | 24.4% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 44.5% | 31.4% | 24.1% |
| Bayesian stackingLearned-weight combination | — | 53.8% | 35.7% | 10.5% |
| Ensemble (published)Uniform average + isotonic calibration | — | 49.9% | 30.5% | 19.5% |
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
- 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
- How and where to watch Spain vs. France 2026 World Cup match: TV channel, streaming options · The New York Times · 14 Jul
- Stage:
- Quarter-final · Match 1
- Date:
- 9 Jul
- Venue:
- Gillette Stadium, Boston
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.
- 2.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.
France
France come in at close to full strength.
Morocco
Morocco: 1 carrying a fitness doubt.
- DoubtNayef Aguerd, the third-choice defender, is recovering from Groin injury and is a fitness watch item; if unavailable the projected XI shifts.
Availability runs in France's favour here: Morocco are managing a fitness concern over Nayef Aguerd, while France'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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