Group C · Matchday 3
MoroccovsHaiti
2026-06-24·18:00 localPredictions finalised
The forecast
Match-outcome probability
- Morocco win67.1%
- Draw23.3%
- Haiti win9.6%
A clash of identities: Morocco's counter-attacker approach meets Haiti's balanced style in a fixture the model gives to Morocco at 79%.
Why the model says this
Favoring Morocco
- ·Morocco holds a significant Elo advantage with a delta of 290 points over Haiti, contributing to a 73.2% home win probability in the Elo model.
- ·Morocco is ranked significantly higher by FIFA at 11th globally, compared to Haiti's 84th position.
- ·The model projects Morocco to generate substantially more attacking threat, with an expected goals (xG) output of 2.19 compared to Haiti's 0.45.
- ·Multiple underlying models show strong favour for Morocco, with the DC model predicting a 77.1% home win and the HP model at 74.5%.
Favoring Haiti
- ·Haiti has shown some attacking capability in recent matches, scoring 7 goals in their last 6 fixtures.
- ·Haiti has kept 3 clean sheets in their last 6 matches, indicating periods of defensive resilience.
What the model can't fully price
- ·The model does not fully account for squad availability, with 3 projected starters across both teams carrying fitness doubts (2 for Morocco, 1 for Haiti).
- ·The model does not explicitly factor in the specific motivation for a 'Group C · Matchday 3' fixture, which could influence team approach and intensity.
Form check
Morocco
SteadyMorocco enters this match in strong form, remaining undefeated in their last six outings with four wins and two draws. Their defence has been particularly robust, conceding only 2 goals in this period.
Morocco has kept 4 clean sheets in their last 6 matches.
Haiti
SteadyHaiti's recent form is mixed, with three wins, one draw, and two losses in their last six fixtures. They have shown capability in attack, scoring 7 goals, but have also conceded 5 goals in the same period.
Haiti has scored 7 goals in their last 6 matches.
Analysis
How it plays out
Haiti's balanced setup will need to hold shape against Morocco's direct transition game. The risk for Haiti: getting caught between attacking and defending.
What decides it
Morocco will concede possession willingly and attack in transition. Their defensive block needs to hold without fouling in dangerous areas. Sofyan Amrabat's 6.2% scoring probability is the highest in this fixture. Containing that output is Haiti's primary defensive task.
Off the pitch
Morocco travel 7,048km, 3x Haiti's journey. Second-half fatigue is a real factor at that differential.
The angle
The model gives Haiti just 9.6% 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 (17.3%) · xG 2.3 - 0.4
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 2–017.3%
- 1–014.7%
- 3–013.2%
- 4–07.5%
- 2–17.4%
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
- 1–029.0%
- 0–026.1%
- 2–016.8%
- 1–16.7%
- 3–06.4%
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 goals93.0%
- More than 1.5 goals75.8%
- More than 2.5 goals51.0%
- More than 3.5 goals28.9%
- More than 4.5 goals13.9%
- More than 5.5 goals5.8%
- Both teams score31.8%
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
- Morocco clean sheetOpposing team scores zero65.1%
- Haiti clean sheetOpposing team scores zero10.2%
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
- Morocco by 4+15.4%
- Morocco by 3+32.1%
- Morocco by 2+55.8%
- Morocco by 1+79.3%
- Draw15.7%
- Haiti by 1+5.1%
- Haiti by 2+1.0%
- Haiti by 3+0.1%
- Haiti 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 51.0% · BTTS 31.8%
Game state through the match
- Morocco ahead79.7%
- Level14.9%
- Haiti ahead5.4%
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
- 0–1536.4%
- 15–3023.2%
- 30–4514.7%
- 45–609.4%
- 60–755.9%
- 75–903.8%
- No goal6.6%
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 → | HMorocco win | DDraw | AHaiti win |
|---|---|---|---|
| HMorocco ahead | 57.9% | 2.2% | 0.2% |
| DLevel | 20.1% | 10.8% | 2.3% |
| AHaiti ahead | 1.7% | 2.1% | 2.8% |
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
- Morocco trail at HT, avoid defeat at FT3.7%
- Haiti trail at HT, avoid defeat at FT2.4%
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: Amrabat (6.2%)
Match detail
Morocco
Model-rated key players: Sofyan Amrabat (MF) — P(scores) 6.2%; Youssef En-Nesyri (FW) — P(scores) 4.8%; Ayoub El Kaabi (FW) — P(scores) 3.5%.
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.
Haiti
Model-rated key players: Dany Jean (FW) — P(scores) 2.5%; Don Deedson Louicius (FW) — P(scores) 2.4%; Duckens Nazon (FW) — P(scores) 2.4%.
Limited recent tournament data is available for Haiti's tactical profile. Early indicators suggest a balanced approach.
Haiti will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries.
Morocco historically converts 11.8% of xG from set-pieces, contributing 0.27 expected set-piece goals in this fixture. Combined, the model expects 0.27 set-piece goals across the 90 minutes.
- P(Morocco scores set-piece goal) 23.7%
- P(set-piece goal in match) 23.7%
Morocco: Mounir Chouiar on corners (26 corners), Sofyan Amrabat on free kicks (per fbref 2020 21) · Haiti: Jean‐Ricner Bellegarde on corners (30 corners) (per fbref 2022 23)
If a penalty is awarded to Morocco, the model gives 74.3% conversion, 72.0% for Haiti.
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.
Tactical forecast
- PPDA
- 22.2
- Possession
- 46%
- Directness (yds/pass)
- 6.6
- Long balls/90
- 34
- Set-piece xG
- 12%
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
- 49%
- 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
Morocco
- Nayef AguerdCentre-backCover: Chadi Riad · 0.000.85gap
- Issa DiopCentre-backCover: Chadi Riad · 0.000.85gap
- Ayoub El KaabiStrikerNo natural backup0.33gap
Haiti
- Jean-Kévin DuverneCentre-backCover: Keeto Thermoncy · 0.000.82gap
- Jean‐Ricner BellegardeCentral midfieldNo natural backup0.63gap
- Hannes DelcroixCentre-backCover: Keeto Thermoncy · 0.000.61gap
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 level320 m
- Avg temperatureFive-year mean over the tournament window25.7 °C
- Avg humidity73%
- Heat stressShade WBGT ~27.9 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf stadium converting to 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. 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)
- Sofyan AmrabatPKMF6.2%
- Youssef En-NesyriFW4.8%
- Ayoub El KaabiFW3.5%
- Dany JeanFW2.5%
- Don Deedson LouiciusFW2.4%
- Duckens NazonFW2.4%
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
Morocco
vs Netherlands · avg 7.0
Haiti
vs Brazil · avg 7.5
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.
8Achraf Hakimi19'–19'Netted a crucial equalizer for Morocco and created danger with a well-taken free-kick.
2goals▼
Netted a crucial equalizer for Morocco and created danger with a well-taken free-kick.
Match timeline
8SabiriConverted the decisive fifth penalty in the shootout, securing victory for Morocco under pressure.
Converted the decisive fifth penalty in the shootout, securing victory for Morocco under pressure.
8Joseph8'–8'Scored Haiti's opening goal with a clinical finish, giving his team an early lead.
2goals▼
Scored Haiti's opening goal with a clinical finish, giving his team an early lead.
Match timeline
6Jean-Kévin DuverneWas part of the defensive line under pressure during a Moroccan goal, but the team conceded.
▼
Was part of the defensive line under pressure during a Moroccan goal, but the team conceded.
Match timeline
6Wilson IsidorShowed forward intent by participating in an offensive move for Haiti without a direct goal contribution.
▼
Showed forward intent by participating in an offensive move for Haiti without a direct goal contribution.
Match timeline
Match observations
- The Netherlands opened the scoring, but Morocco responded with a powerful header to level the score.
- The game proceeded to a penalty shootout after remaining deadlocked.
- The shootout was tense, featuring several misses and saves, ultimately decided by Morocco's fifth successful attempt.
▸Under the hood
Model-by-model comparison
Morocco vs Haiti
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 77.3% | 22.0% | 0.7% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 79.4% | 15.6% | 5.0% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 76.3% | 16.8% | 6.9% |
| Bayesian stackingLearned-weight combination | — | 86.9% | 13.1% | 0.0% |
| Ensemble (published)Uniform average + isotonic calibration | — | 79.3% | 18.3% | 2.4% |
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(Morocco win)74.6%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Morocco win)74.6%
Decomposition of the published P(Morocco 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.
Latest news & match context
No recent headlines for Morocco or Haiti.
- Stage:
- Group C · Matchday 3
- Date:
- 24 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.
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.
Haiti
Haiti come in at close to full strength.
Availability runs in Haiti's favour here: Morocco are managing a fitness concern over Nayef Aguerd, while Haiti'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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