Group B · Matchday 2
CanadavsQatar
2026-06-18·15:00 localPredictions finalised
The forecast
Match-outcome probability
- Canada win64.3%
- Draw23.1%
- Qatar win12.7%
A 359-point Elo gap frames this as a significant mismatch, yet the model still gives Qatar a 9% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Canada
- ·The ensemble model projects Canada with a 58.1% win probability, significantly higher than Qatar's 15.3%.
- ·Canada's expected goals (xG) are 1.88, compared to Qatar's 0.87, indicating a stronger offensive projection.
- ·Canada holds a FIFA ranking of 27, substantially higher than Qatar's 51st position.
- ·The Elo model indicates a significant strength advantage for Canada, with a delta of 359 points over Qatar.
Favoring Qatar
- ·The HP model assigns Qatar a 17.6% win probability, the highest among the individual models.
- ·The DC model projects Qatar with a 16.2% win probability.
What the model can't fully price
- ·Two players across both squads are carrying a fitness doubt, with one projected starter. The model's lineup channel currently contributes zero, meaning these potential absences are not factored into the probabilities.
- ·Canada has had 6 days of rest since their last match, one day more than Qatar's 5 days. Rest and recovery differentials are not inputs for the model.
Form check
Canada
SteadyCanada's recent form shows a mixed bag of results, with one win, four draws, and one loss in their last six matches. They have struggled for consistent goal-scoring in some fixtures, with three 0-0 draws.
Four draws in their last six matches
Qatar
DecliningQatar's recent performances have been challenging, with three losses, two draws, and only one win in their last six outings. They have conceded 6 goals in their last 3 matches.
Three losses in their last four matches
Analysis
How it plays out
Qatar defend deep and give Canada the ball. The question is whether Canada's pragmatic approach generates enough final-third creativity to break through. Canada's aggressive press (PPDA 20.6) against Qatar's deeper build-up (PPDA 35.0) creates a clear territory question: can Canada force errors high up, or will Qatar play through the press and find space behind it?
What decides it
Canada adjust shape to the opponent. That flexibility is an asset, but it takes longer to settle into a game. Qatar defend deep and limit space. Set pieces and individual errors become the most likely routes to goal. Jonathan David's 12.2% scoring probability is the highest in this fixture. Containing that output is Qatar's primary defensive task.
Off the pitch
Qatar travel 11,697km while Canada are essentially at home. That journey shows up in second-half intensity.
The angle
Likely the last World Cup for Hassan Al-Haydos. Tournament experience at this level is hard to quantify but hard to replace.
▸Goals & scorelines
Likeliest score 1–0 (12.9%) · xG 1.9 - 0.8
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–012.9%
- 2–012.7%
- 1–110.8%
- 2–19.6%
- 3–08.0%
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
- 0–027.4%
- 1–024.7%
- 2–011.9%
- 1–110.0%
- 0–19.5%
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 goals92.2%
- More than 1.5 goals74.5%
- More than 2.5 goals49.0%
- More than 3.5 goals27.1%
- More than 4.5 goals12.7%
- More than 5.5 goals5.2%
- Both teams score45.4%
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
- Canada clean sheetOpposing team scores zero47.2%
- Qatar clean sheetOpposing team scores zero15.2%
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
- Canada by 4+7.5%
- Canada by 3+18.8%
- Canada by 2+38.7%
- Canada by 1+63.6%
- Draw22.8%
- Qatar by 1+13.6%
- Qatar by 2+4.0%
- Qatar by 3+0.9%
- Qatar 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 49.0% · BTTS 45.4%
Game state through the match
- Canada ahead64.3%
- Level21.5%
- Qatar ahead14.2%
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
- 0–1535.5%
- 15–3022.9%
- 30–4514.8%
- 45–609.5%
- 60–756.1%
- 75–904.0%
- No goal7.2%
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 → | HCanada win | DDraw | AQatar win |
|---|---|---|---|
| HCanada ahead | 43.4% | 3.8% | 0.7% |
| DLevel | 18.6% | 14.2% | 5.5% |
| AQatar ahead | 2.2% | 3.7% | 7.8% |
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
- Canada trail at HT, avoid defeat at FT5.9%
- Qatar trail at HT, avoid defeat at FT4.5%
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: David (12.2%)
Match detail
Canada
Model-rated key players: Jonathan David (FW) — P(scores) 12.2%; Lucas Cavallini (FW) — P(scores) 4.7%; Cyle Larin (FW) — P(scores) 3.0%.
Canada under Jesse Marsch play a pragmatic game with 49% possession. Their likely shape is a 4-4-2, though they have also used 3-4-3. They apply moderate pressing intensity (PPDA 20.6). They favour high-quality chances (xG/shot 0.156, among the best in the field).
Canada play a pragmatic, results-oriented game that adapts shape to the opposition. Tactical flexibility is their strength. The risk is inconsistency — without a default identity, a poor result can cascade if the team struggles to find a Plan B.
Qatar
Model-rated key players: Akram Afif (FW) — P(scores) 5.3%; Almoez Ali (FW) — P(scores) 5.3%; Ismaeel Mohammad (FW) — P(scores) 5.3%.
Qatar under Julen Lopetegui play a low block game, with just 43% possession — among the lowest in the field. Their likely shape is a 5-3-2, though they have also used other. They sit deeper and pick their moments to press (PPDA 35.0). They are selective in their shooting (6.2 per 90).
Qatar will look to stay compact and frustrate opponents, limiting space and hitting on the break. Set-piece proficiency — both attacking and defending — becomes critical when open-play chances are limited by design. Managing minutes for Hassan Al-Haydos across what could be seven matches will test the coaching staff's rotation planning.
Canada historically converts 13.0% of xG from set-pieces, contributing 0.24 expected set-piece goals in this fixture. Combined, the model expects 0.24 set-piece goals across the 90 minutes.
- P(Canada scores set-piece goal) 21.7%
- P(set-piece goal in match) 21.7%
Canada: Junior Hoilett on corners (25 corners) (per fbref 2018 19) · Qatar: Guilherme on corners (14 corners) (per fbref 2017 18)
If a penalty is awarded to Canada, the model gives 72.0% conversion, 72.0% for Qatar.
Canada primary PK: Jonathan David (2/3 in 2022-23, per fbref 2018 19).
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
- 20.6
- Possession
- 49%
- Directness (yds/pass)
- 6.8
- Long balls/90
- 31
- Set-piece xG
- 13%
- PPDA
- 35.0
- Possession
- 43%
- Directness (yds/pass)
- 5.9
- Long balls/90
- 38
- 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
Canada
- Alistair Johnston TRPFull-backNo natural backup0.51gap
- Jonathan DavidStrikerCover: Daniel Jebbison · 0.370.31gap
- Dayne St. ClairGoalkeeperCover: Owen Goodman · 0.330.31gap
Qatar
- Almoez AliStrikerCover: Ahmed Alaaeldin · 0.130.36gap
- Lucas MendesCentre-backCover: Al-Hashmi Al-Hussain · 0.020.26gap
- Meshaal BarshamGoalkeeperCover: Salah Zakaria · 0.300.17gap
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 window17.4 °C
- Avg humidity73%
- Heat stressShade WBGT ~19.5 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Artificial-turf stadium with a retractable roof; a temporary natural-grass pitch is laid over the turf 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)
- Jonathan DavidPKFW12.2%
- Lucas CavalliniFW4.7%
- Cyle LarinFW3.0%
- Akram AfifFW5.3%
- Almoez AliFW5.3%
- Ismaeel MohammadFW5.3%
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
Canada
vs South Africa · avg 6.0
Qatar
vs Bosnia and Herzegovina · avg 7.0
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.
9Jonathan David110'–146'Scored two goals with clinical finishing and intelligent movement, significantly contributing to Canada's dominant victory.
2goals▼
Scored two goals with clinical finishing and intelligent movement, significantly contributing to Canada's dominant victory.
Match timeline
8Cyle Larin16'–110'Scored the opening goal and was involved in another, demonstrating strong attacking instincts and predatory finishing.
1goals1shots1on target▼
Scored the opening goal and was involved in another, demonstrating strong attacking instincts and predatory finishing.
Match timeline
8Tajon Buchanan29'–43'Scored a goal and his decisive run directly led to a red card for the opposition, significantly impacting the match.
1goals1fouls won▼
Scored a goal and his decisive run directly led to a red card for the opposition, significantly impacting the match.
Match timeline
7Jacob Shaffelburg132'–132'Scored a goal from close range, capitalizing on a cross and contributing to Canada's dominant scoreline.
1goals▼
Scored a goal from close range, capitalizing on a cross and contributing to Canada's dominant scoreline.
Match timeline
2Homam Ahmed49'–49'Received a red card for a foul, which severely hampered Qatar's defensive efforts and morale for the majority of the match.
1 red▼
Received a red card for a foul, which severely hampered Qatar's defensive efforts and morale for the majority of the match.
Match timeline
Match observations
- Canada delivered a commanding performance, securing a comprehensive victory with multiple goals.
- The match was characterised by Canada's relentless attack and Qatar's struggles to contain them, exacerbated by a red card.
- The home crowd was in full voice, celebrating each goal as Canada dominated proceedings.
▸Under the hood
Model-by-model comparison
Canada vs Qatar
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 76.2% | 22.0% | 1.8% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 62.8% | 22.9% | 14.3% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 61.1% | 23.1% | 15.8% |
| Bayesian stackingLearned-weight combination | — | 76.1% | 20.2% | 3.7% |
| Ensemble (published)Uniform average + isotonic calibration | — | 67.9% | 22.9% | 9.2% |
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
Probability decomposition (transparency surface)
- Baseline ensemble — P(Canada win)64.3%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Canada win)64.3%
Decomposition of the published P(Canada 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 |
|---|---|---|---|---|---|
| 18 Jun 2026 | FIFA World Cup | HVancouver | 6–0 | W | — |
| 23 Sep 2022 | Friendly | NVienna | 2–0 | W | — |
Canada vs Qatar, every senior international meeting in the martj42 results dataset (score from Canada's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for Canada or Qatar.
- Stage:
- Group B · Matchday 2
- Date:
- 18 Jun
Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.
- 1.Rest differential: Canada have had 6 days since their previous match versus 5 for Qatar. Rest and recovery are not model inputs.
Canada and Qatar 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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