Group C · Matchday 2
BrazilvsHaiti
2026-06-19·20:00 localPredictions finalised
The forecast
Match-outcome probability
- Brazil win86.9%
- Draw11.3%
- Haiti win1.8%
A clash of identities: Brazil's high-press approach meets Haiti's balanced style in a fixture the model gives to Brazil at 88%.
Why the model says this
Favoring Brazil
- ·Brazil holds a significant Elo rating advantage of 452 points over Haiti, indicating a substantial difference in team strength.
- ·Brazil is ranked 5th globally by FIFA, while Haiti is ranked 84th, a difference of 79 places.
- ·Brazil has won all 3 previous head-to-head encounters against Haiti, scoring 17 goals and conceding only 1 (7-1, 6-0, 4-0).
- ·Brazil's expected goals (xG) for this match are 3.95, significantly higher than Haiti's 0.47 xG, suggesting dominant offensive output.
Favoring Haiti
- ·Haiti has secured two wins in their last four competitive matches (FIFA World Cup qualification), with scores of 2-0 and 1-0.
- ·Haiti recently achieved a 1-1 draw in a friendly match in March 2026.
What the model can't fully price
- ·The model does not fully account for squad availability, with 3 players across both squads carrying fitness doubts, 2 of whom are projected starters.
- ·The model does not explicitly factor in the specific motivation for a 'Group C · Matchday 2' fixture, which can influence team approach and intensity.
- ·The model does not account for potential travel fatigue or venue-specific conditions, as venue information is not provided.
Form check
Brazil
SteadyBrazil's recent six-match run includes three wins, one draw, and two losses. They have shown attacking capability with a 3-1 win and a 5-0 win, but also suffered a 2-1 defeat and a 3-2 loss in the Kirin Cup.
Brazil has scored 14 goals in their last six matches.
Haiti
SteadyHaiti's last six matches feature three wins, one draw, and two losses. Their World Cup qualification campaign saw two clean sheet victories (2-0, 1-0), but they also experienced a 3-0 defeat in the same competition.
Haiti has kept two clean sheets in their last four competitive matches.
Analysis
How it plays out
Brazil press high and force the tempo. Haiti's balanced setup needs to absorb that pressure early and find the right moments to play forward. Brazil will expect to hold 58% possession. Haiti need their shape to stay compact without the ball and be clinical when they win it back.
What decides it
Brazil press high (PPDA 17.1). If the press doesn't win the ball early, the space behind their back line becomes exposed. Raphinha's 14.4% scoring probability is the highest in this fixture. Containing that output is Haiti's primary defensive task.
Off the pitch
Brazil travel 6,482km, 3x Haiti's journey. Second-half fatigue is a real factor at that differential.
The angle
The model gives Haiti just 2.9% to win. Every World Cup produces group-stage upsets; the question is whether this fixture is one of them.
▸Goals & scorelines
Likeliest score 3–0 (13.1%) · xG 3.9 - 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
- 3–013.1%
- 4–012.9%
- 5–010.1%
- 2–010.0%
- 6–06.6%
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
- 1–022.0%
- 2–021.9%
- 3–014.4%
- 0–011.6%
- 4–07.1%
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 goals98.6%
- More than 1.5 goals93.2%
- More than 2.5 goals80.8%
- More than 3.5 goals63.1%
- More than 4.5 goals43.8%
- More than 5.5 goals27.0%
- Both teams score33.8%
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
- Brazil clean sheetOpposing team scores zero65.7%
- Haiti clean sheetOpposing team scores zero2.0%
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
- Brazil by 4+47.3%
- Brazil by 3+66.8%
- Brazil by 2+83.6%
- Brazil by 1+94.0%
- Draw4.8%
- Haiti by 1+1.2%
- Haiti by 2+0.2%
- Haiti by 3+<0.1%
- Haiti by 4+0.0%
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 80.8% · BTTS 33.8%
Game state through the match
- Brazil ahead94.2%
- Level4.5%
- Haiti ahead1.4%
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
- 0–1551.6%
- 15–3025.0%
- 30–4512.1%
- 45–605.9%
- 60–752.8%
- 75–901.4%
- No goal1.3%
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 → | HBrazil win | DDraw | AHaiti win |
|---|---|---|---|
| HBrazil ahead | 79.0% | 0.9% | 0.1% |
| DLevel | 13.7% | 2.8% | 0.5% |
| AHaiti ahead | 1.5% | 0.8% | 0.7% |
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
- Brazil trail at HT, avoid defeat at FT2.3%
- Haiti trail at HT, avoid defeat at FT0.9%
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: Raphinha (14.4%)
Match detail
Brazil
Model-rated key players: Raphinha (FW) — P(scores) 14.4%; Gabriel Jesus (FW) — P(scores) 7.4%; Neymar (FW) — P(scores) 5.4%.
Brazil under Carlo Ancelotti play a high press game, holding 58% of the ball — among the highest in the tournament field. Their likely shape is a 4-2-3-1, though they have also used 4-3-3. They apply moderate pressing intensity (PPDA 17.1) and build patiently through midfield with 7.2 passes per attacking sequence. They generate a high volume of shots (16.5 per 90).
Brazil need their high press to force turnovers in dangerous areas — if opponents can play through the press, the space left behind is vulnerable. Physical conditioning and squad rotation will be critical to sustain pressing intensity across a long tournament.
Haiti
Model-rated key players: Dany Jean (FW) — P(scores) 2.2%; Don Deedson Louicius (FW) — P(scores) 2.1%; Duckens Nazon (FW) — P(scores) 2.1%.
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.
Brazil's predicted XI averages 1,628 club minutes over the 2024-25 season (light load).
Brazil coverage: 67.0% (10/11 XI matched against the FBref Big-5) · Haiti: 20.0% (2/11).
Brazil historically converts 10.8% of xG from set-pieces, contributing 0.43 expected set-piece goals in this fixture. Combined, the model expects 0.43 set-piece goals across the 90 minutes.
- P(Brazil scores set-piece goal) 34.8%
- P(set-piece goal in match) 34.8%
Brazil: Matheus Pereira on corners (84 corners) (per fbref 2020 21) · Haiti: Jean‐Ricner Bellegarde on corners (30 corners) (per fbref 2022 23)
If a penalty is awarded to Brazil, the model gives 72.0% conversion, 72.0% for Haiti.
Brazil primary PK: Raphinha (4/4 in 2021-22, 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
- 17.1
- Possession
- 58%
- Directness (yds/pass)
- 4.8
- Long balls/90
- 23
- Set-piece xG
- 11%
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
Brazil
- Bruno GuimarãesCentral midfieldNo natural backup0.67gap
- Lucas PaquetáAttacking midfieldNo natural backup0.45gap
- CasemiroDefensive midfieldCover: Fabinho · 0.440.42gap
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 level10 m
- Avg temperatureFive-year mean over the tournament window24.8 °C
- Avg humidity70%
- Heat stressShade WBGT ~26.5 °CLow heat stress
- Pitch surfacenatural grass
Natural-grass NFL stadium; FIFA-standard hybrid 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)
- Dany JeanFW2.2%
- Don Deedson LouiciusFW2.1%
- Duckens NazonFW2.1%
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
Brazil
vs Japan · avg 7.0
Haiti
vs Morocco · avg 6.7
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.
8Matheus CunhaScored the crucial opening goal with excellent anticipation and predatory instincts.
1goals▼
Scored the crucial opening goal with excellent anticipation and predatory instincts.
Match timeline
9Haiti GoalkeeperMade numerous crucial saves, including a double save, to prevent a much larger defeat for Haiti.
4saves▼
Made numerous crucial saves, including a double save, to prevent a much larger defeat for Haiti.
Match timeline
6Dominique SimonMade a crucial defensive clearance to prevent a goal for Haiti under significant pressure.
1blocks▼
Made a crucial defensive clearance to prevent a goal for Haiti under significant pressure.
Match timeline
Match observations
- Brazil secured a comfortable 3-0 victory over Haiti in a match where they largely dominated proceedings.
- The Brazilian side showcased their attacking prowess, creating numerous opportunities and converting three of them, though two other goals were ruled out for offside.
- Haiti's defence, particularly their goalkeeper, put in a resilient performance, making several crucial interventions to prevent a larger deficit.
▸Under the hood
Model-by-model comparison
Brazil vs Haiti
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 82.5% | 17.5% | 0.0% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 94.0% | 4.8% | 1.2% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 91.6% | 6.4% | 2.0% |
| Bayesian stackingLearned-weight combination | — | 100.0% | 0.0% | 0.0% |
| Ensemble (published)Uniform average + isotonic calibration | — | 87.6% | 12.1% | 0.4% |
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(Brazil win)86.9%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Brazil win)86.9%
Decomposition of the published P(Brazil 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 |
|---|---|---|---|---|---|
| 19 Jun 2026 | FIFA World Cup | NPhiladelphia | 3–0 | W | — |
| 8 Jun 2016 | Copa América | NOrlando | 7–1 | W | — |
| 18 Aug 2004 | Friendly | APort-au-Prince | 6–0 | W | — |
| 21 Apr 1974 | Friendly | HBrasília | 4–0 | W | — |
Brazil vs Haiti, every senior international meeting in the martj42 results dataset (score from Brazil's perspective; H/A/N = home/away/neutral).
Latest news & match context
- Brazil’s World Cup Collapse Revives Debate Over Faith and Soccer · Religion Unplugged · 14 Jul
- Stage:
- Group C · Matchday 2
- Date:
- 19 Jun
Brazil and Haiti 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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