WC 2026 · Forecasting Oxford Football Forecasting
🇬🇭

Ghana

CAF Group L
0.0% Champion probability ±0.00 MC-SE
Coach
Carlos Queiroz
Elo (model)
1,510 world 94th
Squad value
€291M
Power → Reality
39th 41st −0.00 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Ghana — stage progression

Round of 32: 35.98% (95% MC 35.68%–36.28%; MC-SE ±0.15 pts) Round of 32 reach 36.0% ±0.15 Round of 16: 6.73% (95% MC 6.57%–6.88%; MC-SE ±0.08 pts) Round of 16 reach 6.7% ±0.08 Quarter-final: 1.45% (95% MC 1.38%–1.53%; MC-SE ±0.04 pts) Quarter-final reach 1.5% ±0.04 Semi-final: 0.27% (95% MC 0.23%–0.30%; MC-SE ±0.02 pts) Semi-final reach 0.3% ±0.02 Final: 0.05% (95% MC 0.04%–0.06%; MC-SE ±0.01 pts) Final reach 0.1% ±0.01 Champion: 0.00% (95% MC 0.00%–0.01%; MC-SE ±0.00 pts) Champion reach 0.0% ±0.00

Ghana is most likely eliminated before the knockout rounds: 36% to clear the group. Champion probability 0.00%.

Source · Oxford Football Forecasting model
Group L Confed Advance (top 2) Reach R32
1🏴󠁧󠁢󠁥󠁮󠁧󠁿EnglandUEFA68.9%97.5%
2🇭🇷CroatiaUEFA47.0%90.3%
3🇬🇭GhanaCAF6.7%36.0%
4🇵🇦PanamaCONCACAF6.7%34.8%

Source · Oxford Football Forecasting model

Bracket position Half 1 · Quadrant 2

Earliest possible meetings

No collision rows recorded for this team.

Collision = the earliest round the bracket wiring could pit Ghana against that side. Full bracket & collision matrix →

Match 21 · 2026-06-17 · Toronto Stadium home
Ghana Panama
33% win 32.1% draw 34.9% loss
Most likely 1–1 (14.6%) λ 1.03–1.07 Over 2.5 35% · BTTS 43%
Match 45 · 2026-06-23 · Boston Stadium away
6.4% win 19.1% draw 74.5% loss
Most likely 0–2 (17.4%) λ 0.44–2.03 Over 2.5 45% · BTTS 32%
Match 68 · 2026-06-27 · Philadelphia Stadium away
Ghana Croatia
11.5% win 23.9% draw 64.6% loss
Most likely 0–1 (15.6%) λ 0.61–1.77 Over 2.5 43% · BTTS 39%
How a match forecast is built

Each pairing is scored by the ensemble (Dixon-Coles bivariate-Poisson, the Bayesian hierarchical model and the global LightGBM-Poisson, log-pooled), producing an 11×11 scoreline grid that is marginalised into win/draw/loss, expected goals (λ), over/under 2.5 and both-teams-to-score. These are the same distributions the tournament simulator consumes, oriented here to Ghana. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.

Fig. D2 Relative to the 48-team median

Ghana vs the field

Elo rating: 1510 vs field median 1780 (0.85× the field) Elo rating 1510 med 1780 Recent NT form: 1.33 ppg vs field median 1.87 ppg (0.71× the field) Recent NT form 1.33 ppg med 1.87 ppg Squad value: €291M vs field median €286M (1.02× the field) Squad value €291M med €286M Squad form (global): 0.187 vs field median 0.211 (0.89× the field) Squad form (global) 0.187 med 0.211 Fitness readiness: 0.689 vs field median 0.707 (0.97× the field) Fitness readiness 0.689 med 0.707 Familiarity / chemistry: 0.012 vs field median 0.015 (0.80× the field) Familiarity / chemistry 0.012 med 0.015 Experience (mean caps): 16 vs field median 25 (0.65× the field) Experience (mean caps) 16 med 25

Read each row as a multiple of the field median: dots to the right of the dashed line are above-field, to the left below. Raw values are labelled on the right so the comparison is transparent.

Source · Oxford Football Forecasting model

Fig. D3 Bayesian projection residual g

Ghana on the decoupling axis

aligned (0) ← record > squad price squad valued > record →

g = +0.34 ± 0.11: the squad is valued above its record — the transfer market rates this side above what its results have earned.

Source · Oxford Football Forecasting model
What g means — and its limits

g is the residual from regressing a team’s current squad market value on its history-based strength in the Bayesian hierarchical model. Positive g means the squad is valued above what the team’s record predicts; negative means the record outruns the squad’s price (the side achieves more than its market value implies). Regressed on out-of-sample success the slope is positive — squad-rich sides go a touch further — but not statistically significant at n = 3 tournaments, so treat a single team’s g as a descriptive read, not a hard prediction. The full decoupling essay →

26players
24.0mean age
16mean caps
58%in a top-5 league
23distinct clubs
3largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Lawrence Ati-ZigiGKSt. GallenSuper League −0.07z5,8700290
2Alidu SeiduDFRennesLigue 1 +1.70z1,4070241
3Caleb YirenkyiMFNordsjællandSuperliga −0.53z2,6142111
4Jonas AdjeteyDFVfL WolfsburgBundesliga +1.84z1370100
5Thomas ParteyMFVillarrealLa Liga +2.13z1,33205715
6Abdul MuminDFRayo VallecanoLa Liga +2.13z78050
7Abdul FatawuFWLeicester CityChampionship +2.21z3,7269283
8Kwasi SiboMFOviedoLa Liga +2.13z1,809080
9Jordan Ayew (captain)FWLeicester CityChampionship +2.21z2,388612034
10Brandon Thomas-AsanteFWCoventry CityChampionship +2.21z1,9911381
11Antoine SemenyoMFManchester CityPremier League +2.21z2,02211343
12Joseph AnangGKSt Patrick's Athleticno club data10
13Christopher Bonsu BaahFWAl-QadsiahPro League −0.86z2,679390
14Gideon MensahDFAuxerreLigue 1 +1.70z2,4230400
15Elisha OwusuMFAuxerreLigue 1 +1.70z2,3530200
16Benjamin AsareGKHearts of Oakno club data110
17Abdul Rahman BabaDFPAOKSuper League 1 +0.03z2,7483511
18Jerome OpokuDFİstanbul BaşakşehirSüper Lig +0.49z2,8841111
19Iñaki WilliamsFWAthletic BilbaoLa Liga +2.13z2,8884262
20Augustine BoakyeMFSaint-ÉtienneLigue 2 +1.70z2,746500
21Kojo Peprah OppongDFNiceLigue 1 +1.70z3,464140
22Kamaldeen SulemanaFWAtalantaSerie A +1.70z1,3603281
23Derrick LuckassenDFPafos1. Division −0.31z1,350110
24Ernest NuamahFWLyonLigue 1 +1.70z470184
25Prince Kwabena AduFWViktoria Plzeňno club data50
26Marvin SenayaDFAuxerreLigue 1 +1.70z1,874120

Source · Official squad announcements · API-Football (global club coverage). 3 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 88%.

Diaspora in the hosts

225,756

7.0 per 1,000 of home population

Host-language familiarity

Shared

primary language English · spoken in a host

Climate adaptation gap

−1.0°C

home-vs-venue heat differential

Venue extremes

35°C

peak heat index · altitude up to 83 m

Travel

5h

max time-zone shift · nearest venue 8,035 km

Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records

Fig. D4 eloratings.net method · year-end values

Ghana — Elo since 1950

1625 world #94
Ghana Qualified-field median

Ghana ends the series at 1625 Elo, the world’s 94th-ranked side — below the qualified-field median.

Source · eloratings.net
Which Elo is this?

This line is the public eloratings.net series (year-end ratings), which terminates exactly at the current rating and world rank shown on the marker. It is a different number from the Elo shown in the header band (a panel-normalised rating used inside the forecast); the two are ~0.99 correlated but on different scales. We keep them distinct rather than blend them.

88% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
88% Fitness-readiness coverage Where below 100%, part of the fitness signal is imputed by the de-biasing layer.
n = 3 Out-of-sample tournaments The model is validated on three held-out World Cups; it matches the market, it does not beat it.

Validated on n=3 held-out tournaments; coverage below 1.0 means part of this squad's club-form/fitness is imputed (the global de-biasing layer). For Ghana, 3 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →