Group L · Matchday 1
GhanavsPanama
2026-06-17·19:00 localPredictions finalised
The forecast
Match-outcome probability
- Ghana win40.7%
- Draw29.4%
- Panama win29.9%
A 234-point Elo gap frames this as a significant mismatch, yet the model still gives Panama a 30% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Ghana
- ·Ghana's expected goals (xG) of 1.01 slightly exceed Panama's 0.97.
- ·The DC model assigns Ghana a 34.7% win probability, higher than Panama's 32.5%.
- ·The HP model gives Ghana a 38.7% chance of winning, compared to Panama's 31.1%.
Favoring Panama
- ·Panama holds a significantly higher FIFA ranking at 30th, compared to Ghana's 72nd.
- ·Panama is favoured by an ELO gap of 234 points.
- ·The ELO model predicts a 68.4% win probability for Panama, against 9.6% for Ghana.
- ·Panama's recent form shows 3 wins, 2 draws, and 1 loss in their last six matches, while Ghana has 2 wins and 4 losses.
What the model can't fully price
- ·The model does not account for the fitness doubt of one player across both squads, as its lineup channel currently contributes zero to the forecast.
- ·The venue information is not specified, which means the model cannot fully account for potential home advantage or neutral ground conditions.
Form check
Ghana
DecliningGhana's recent form shows a concerning trend, with four consecutive losses in friendly matches, conceding 10 goals and scoring only 2. Their last two competitive matches were wins in World Cup qualification.
4 consecutive losses in recent friendlies
Panama
SteadyPanama enters the match in reasonable form, having secured three wins and two draws in their last six fixtures. Their only loss was a narrow 1-0 defeat in a friendly.
3 wins and 2 draws in their last six matches
Analysis
How it plays out
Neither side wants sustained possession. Ghana's counter attacker and Panama's transition approach could produce a cagey contest decided by set pieces and moments.
What decides it
Ghana will concede possession willingly and attack in transition. Their defensive block needs to hold without fouling in dangerous areas. Panama will concede possession willingly and attack in transition. Their defensive block needs to hold without fouling in dangerous areas. Jordan Ayew carries the marginally higher scoring probability (12.2% vs 6.9%).
Off the pitch
Ghana travel 8,466km, 2x Panama's journey. Second-half fatigue is a real factor at that differential. Thomas Christiansen (6 years in charge of Panama) vs Carlos Queiroz (0 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
Likely the last World Cup for Jordan Ayew. Tournament experience at this level is hard to quantify but hard to replace.
▸Goals & scorelines
Likeliest score 1–1 (14.2%) · xG 1.1 - 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
- 1–114.2%
- 0–014.0%
- 1–013.7%
- 0–111.4%
- 2–08.0%
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
- 0–036.9%
- 1–019.4%
- 0–116.3%
- 1–19.8%
- 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 goals86.0%
- More than 1.5 goals61.0%
- More than 2.5 goals33.1%
- More than 3.5 goals14.8%
- More than 4.5 goals5.5%
- More than 5.5 goals1.8%
- Both teams score41.2%
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
- Ghana clean sheetOpposing team scores zero39.5%
- Panama clean sheetOpposing team scores zero33.3%
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
- Ghana by 4+1.2%
- Ghana by 3+5.0%
- Ghana by 2+16.0%
- Ghana by 1+38.4%
- Draw32.1%
- Panama by 1+29.5%
- Panama by 2+10.7%
- Panama by 3+2.9%
- Panama by 4+0.6%
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 33.1% · BTTS 41.2%
Game state through the match
- Ghana ahead39.2%
- Level30.4%
- Panama ahead30.3%
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
- 0–1528.7%
- 15–3020.4%
- 30–4514.6%
- 45–6010.4%
- 60–757.4%
- 75–905.3%
- No goal13.2%
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 → | HGhana win | DDraw | APanama win |
|---|---|---|---|
| HGhana ahead | 23.6% | 4.5% | 1.1% |
| DLevel | 14.1% | 21.8% | 11.4% |
| APanama ahead | 1.4% | 4.5% | 17.7% |
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
- Ghana trail at HT, avoid defeat at FT5.8%
- Panama 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.
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: Ayew (12.2%)
Match detail
Ghana
Model-rated key players: Jordan Ayew (FW) — P(scores) 12.2%; Antoine Semenyo (FW) — P(scores) 5.0%; Joseph Paintsil (FW) — P(scores) 4.5%.
Ghana under Carlos Queiroz play a counter attacker game, with just 43% possession — among the lowest in the field. Their likely shape is a 4-2-3-1, though they have also used 5-3-2. They apply moderate pressing intensity (PPDA 20.2) and move the ball forward quickly at 5.3 passes per attack. They are selective in their shooting (7.5 per 90) and favour high-quality chances (xG/shot 0.140, among the best in the field).
Ghana 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. Managing minutes for Jordan Ayew across what could be seven matches will test the coaching staff's rotation planning.
Panama
Model-rated key players: Alfredo Stephens (FW) — P(scores) 6.9%; José Fajardo (FW) — P(scores) 4.7%; Ismael Díaz (FW) — P(scores) 4.3%.
Panama under Thomas Christiansen play a transition heavy game with 46% possession. They apply moderate pressing intensity (PPDA 21.2). They are selective in their shooting (10.0 per 90).
Panama 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. Managing minutes for Eric Davis across what could be seven matches will test the coaching staff's rotation planning.
Ghana historically converts 5.2% of xG from set-pieces, contributing 0.06 expected set-piece goals in this fixture. Combined, the model expects 0.06 set-piece goals across the 90 minutes.
- P(Ghana scores set-piece goal) 5.5%
- P(set-piece goal in match) 5.5%
Ghana: Salis Abdul Samed on free kicks (per fbref 2022 23)
If a penalty is awarded to Ghana, the model gives 72.5% conversion, 72.0% for Panama.
Ghana primary PK: Jordan Ayew (1/1 in 2017-18, 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.
Tactical forecast
- PPDA
- 20.2
- Possession
- 43%
- Directness (yds/pass)
- 6.6
- Long balls/90
- 32
- Set-piece xG
- 5%
- PPDA
- 21.2
- Possession
- 46%
- Directness (yds/pass)
- 7.3
- Long balls/90
- 37
- 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
Ghana
- Lawrence Ati-ZigiGoalkeeperCover: Joseph Anang · 0.210.41gap
- Thomas ParteyDefensive midfieldCover: Elisha Owusu · 0.270.37gap
- Gideon MensahFull-backCover: Marvin Senaya · 0.260.29gap
Panama
- Adalberto CarrasquillaCentral midfieldNo natural backup0.30gap
- José Luis RodríguezWingerCover: César Yanis · 0.070.28gap
- Ismael DíazWingerCover: César Yanis · 0.070.25gap
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 level78 m
- Avg temperatureFive-year mean over the tournament window21.2 °C
- Avg humidity71%
- Heat stressShade WBGT ~22.9 °CLow heat stress
- Pitch surfacenatural grass
Natural-grass football stadium.
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)
- Jordan AyewPKFW12.2%
- Antoine SemenyoFW5.0%
- Joseph PaintsilFW4.5%
- Alfredo StephensFW6.9%
- José FajardoFW4.7%
- Ismael DíazFW4.3%
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
Ghana
vs Colombia · avg 6.3
Worked well: Their goalkeeper, Ati-Zigi, delivered an outstanding performance, making crucial saves that prevented Colombia from extending their lead. The defence showed moments of resilience under pressure.
Struggled: Ghana struggled to create sustained attacking pressure and lacked a decisive final pass or shot. They were often caught out by Colombia's swift counter-attacks and lost possession in dangerous areas.
Panama
vs England · 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.
9Lawrence Ati-Zigi1'–90'His numerous crucial saves throughout the match, especially early on and at the very end, were instrumental in securing the clean sheet and the victory for Ghana.
5saves▼
His numerous crucial saves throughout the match, especially early on and at the very end, were instrumental in securing the clean sheet and the victory for Ghana.
Match timeline
8Antoine Semenyo61'–90'Semenyo scored the decisive late goal with a composed finish after a good dribble, proving to be Ghana's key attacking threat.
1goals2shots2on target▼
Semenyo scored the decisive late goal with a composed finish after a good dribble, proving to be Ghana's key attacking threat.
Match timeline
7Jordan Ayew61'–61'Ayew provided an attacking presence for Ghana, having a shot on target and showing good predatory instincts in the box.
1shots1on target▼
Ayew provided an attacking presence for Ghana, having a shot on target and showing good predatory instincts in the box.
Match timeline
6Cristian Martínez1'–59'Martínez was a persistent attacking threat for Panama, creating several chances and having multiple shots, but ultimately lacked the precision to convert.
4shots3on target▼
Martínez was a persistent attacking threat for Panama, creating several chances and having multiple shots, but ultimately lacked the precision to convert.
Match timeline
5Jiovany Ramos66'–66'Ramos had a moment of individual attacking endeavor with a shot from outside the box, but it failed to hit the target.
1shots▼
Ramos had a moment of individual attacking endeavor with a shot from outside the box, but it failed to hit the target.
Match timeline
5Ismael Díaz90'–90'Díaz had a final effort on target in injury time that was saved, almost snatching a late equalizer for Panama.
1shots1on target▼
Díaz had a final effort on target in injury time that was saved, almost snatching a late equalizer for Panama.
Match timeline
▸Under the hood
Model-by-model comparison
Ghana vs Panama
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 19.3% | 22.0% | 58.7% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 38.5% | 32.1% | 29.5% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 39.6% | 30.9% | 29.4% |
| Bayesian stackingLearned-weight combination | — | 30.1% | 33.3% | 36.5% |
| Ensemble (published)Uniform average + isotonic calibration | — | 40.7% | 29.4% | 29.9% |
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(Ghana win)26.3%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Ghana win)26.3%
Decomposition of the published P(Ghana 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 |
|---|---|---|---|---|---|
| 17 Jun 2026 | FIFA World Cup | NToronto | 1–0 | W | — |
Ghana vs Panama, every senior international meeting in the martj42 results dataset (score from Ghana's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for Ghana or Panama.
- Stage:
- Group L · Matchday 1
- Date:
- 17 Jun
Ghana and Panama 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/.
Standard Pass
This match is a free preview
You're seeing the model's full forecast for this fixture for free. Unlock the same depth: probabilities, expected goals, scoreline distributions, and per-player scoring, for all 104 matches with a Standard Pass, valid through the tournament.
Every forecast graded against the real result, scored on 987 matches since 2014. See the scorecard.
24h money-back, no questions asked·No subscription, no auto-renewal·Access through 31 Dec 2026. See refund policy.