WC 2026 · Forecasting Oxford Football Forecasting
🇨🇮

Côte d'Ivoire

CAF Group E
0.2% Champion probability ±0.02 MC-SE
Coach
Emerse Faé
Elo (model)
1,695
Squad value
€397M
Power → Reality
27th 28th −0.00 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Côte d'Ivoire — stage progression

Round of 32: 80.16% (95% MC 79.91%–80.40%; MC-SE ±0.13 pts) Round of 32 reach 80.2% ±0.13 Round of 16: 31.80% (95% MC 31.52%–32.09%; MC-SE ±0.15 pts) Round of 16 reach 31.8% ±0.15 Quarter-final: 10.84% (95% MC 10.65%–11.03%; MC-SE ±0.10 pts) Quarter-final reach 10.8% ±0.10 Semi-final: 3.22% (95% MC 3.11%–3.33%; MC-SE ±0.06 pts) Semi-final reach 3.2% ±0.06 Final: 0.85% (95% MC 0.79%–0.90%; MC-SE ±0.03 pts) Final reach 0.8% ±0.03 Champion: 0.23% (95% MC 0.20%–0.26%; MC-SE ±0.02 pts) Champion reach 0.2% ±0.02

On the central forecast, Côte d'Ivoire more likely than not reaches the Round of 32 (80%). Champion probability is 0.2% ± 0.02 pts.

Source · Oxford Football Forecasting model
Group E Confed Advance (top 2) Reach R32
1🇩🇪GermanyUEFA62.5%98.1%
2🇪🇨EcuadorCONMEBOL50.7%93.3%
3🇨🇮Côte d'IvoireCAF31.8%80.2%
4🇨🇼CuraçaoCONCACAF0.4%5.2%

Source · Oxford Football Forecasting model

Bracket position Half 0 · Quadrant 0

Earliest possible meetings

No collision rows recorded for this team.

Collision = the earliest round the bracket wiring could pit Côte d'Ivoire against that side. Full bracket & collision matrix →

Match 9 · 2026-06-14 · Philadelphia Stadium home
Côte d'Ivoire Ecuador
21.5% win 33.6% draw 44.9% loss
Most likely 0–0 (17.2%) λ 0.70–1.13 Over 2.5 28% · BTTS 35%
Match 33 · 2026-06-20 · Toronto Stadium away
Côte d'Ivoire Germany
17.7% win 26.2% draw 56.1% loss
Most likely 0–1 (13.0%) λ 0.82–1.65 Over 2.5 45% · BTTS 46%
Match 55 · 2026-06-25 · Philadelphia Stadium away
Côte d'Ivoire Curaçao
74% win 18.7% draw 7.3% loss
Most likely 2–0 (15.9%) λ 2.13–0.53 Over 2.5 49% · BTTS 37%
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 Côte d'Ivoire. 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

Côte d'Ivoire vs the field

Elo rating: 1695 vs field median 1780 (0.95× the field) Elo rating 1695 med 1780 Recent NT form: 2.20 ppg vs field median 1.87 ppg (1.18× the field) Recent NT form 2.20 ppg med 1.87 ppg Squad value: €397M vs field median €286M (1.39× the field) Squad value €397M med €286M Squad form (global): 0.221 vs field median 0.211 (1.05× the field) Squad form (global) 0.221 med 0.211 Fitness readiness: 0.695 vs field median 0.707 (0.98× the field) Fitness readiness 0.695 med 0.707 Familiarity / chemistry: 0.003 vs field median 0.015 (0.20× the field) Familiarity / chemistry 0.003 med 0.015 Experience (mean caps): 21 vs field median 25 (0.84× the field) Experience (mean caps) 21 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

Côte d'Ivoire on the decoupling axis

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

g = +0.24 ± 0.05: 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
25.3mean age
21mean caps
69%in a top-5 league
25distinct clubs
2largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Yahia FofanaGKÇaykur RizesporSüper Lig +0.49z2,4300350
2Ousmane DiomandeDFSporting CPPrimeira Liga +1.14z2,1571151
3Ghislain KonanDFGil VicentePrimeira Liga +1.14z2,6610540
4Jean Michaël SeriMFMaribor1. SNL00654
5Wilfried SingoDFGalatasaraySüper Lig +0.49z1,5252341
6Seko FofanaMFPortoPrimeira Liga +1.14z7053327
7Odilon KossounouDFAtalantaSerie A +1.70z1,8951350
8Franck Kessié (captain)MFAl-AhliPro League −0.86z3,043810315
9Ange-Yoan BonnyFWInter MilanSerie A +1.70z1,723710
10Simon AdingraFWMonacoLigue 1 +1.70z9993295
11Yan DiomandeFWRB LeipzigBundesliga +1.84z2,71713103
12Elye WahiFWNiceLigue 1 +1.70z1,328920
13Christopher OpériDFİstanbul BaşakşehirSüper Lig +0.49z2,2642120
14Oumar DiakitéFWCercle BruggeJupiler Pro League −0.07z8580296
15Amad DialloFWManchester UnitedPremier League +2.21z2,4262196
16Mohamed KonéGKCharleroiJupiler Pro League −0.07z1,710000
17Guéla DouéDFStrasbourgLigue 1 +1.70z2,8192203
18Ibrahim SangaréMFNottingham ForestPremier League +2.21z2,84125812
19Nicolas PépéFWVillarrealLa Liga +2.13z3,27385512
20Emmanuel AgbadouDFBeşiktaşSüper Lig +0.49z1,2701202
21Evan NdickaDFRomaSerie A +1.70z6,1736280
22Evann GuessandFWCrystal PalacePremier League +2.21z6772214
23Alban LafontGKPanathinaikosSuper League 1 +0.03z2,873040
24Bazoumana TouréFWTSG HoffenheimBundesliga +1.84z2,524562
25Parfait GuiagonMFCharleroiJupiler Pro League −0.07z2,555950
26Christ Inao OulaïMFTrabzonsporSüper Lig +0.49z2,239290

Source · Official squad announcements · API-Football (global club coverage). Every player’s club season matched in API-Football.

Diaspora in the hosts

54,094

2.0 per 1,000 of home population

Host-language familiarity

Shared

primary language French · spoken in a host

Climate adaptation gap

+0.7°C

home-vs-venue heat differential

Venue extremes

35°C

peak heat index · altitude up to 81 m

Travel

5h

max time-zone shift · nearest venue 7,529 km

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

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

Côte d'Ivoire — Elo since 1960

1815 world #38
Côte d'Ivoire Qualified-field median

Côte d'Ivoire ends the series at 1815 Elo, the world’s 38th-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.

96% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
100% 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). Full validation, calibration & conformal coverage →