WC 2026 · Forecasting Oxford Football Forecasting
🇸🇳

Senegal

CAF Group I
0.5% Champion probability ±0.02 MC-SE
Coach
Pape Thiaw home · Senegalese
Elo (model)
1,867 world 22nd
Squad value
€360M
Power → Reality
22nd 22nd −0.07 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Senegal — stage progression

Round of 32: 69.28% (95% MC 68.99%–69.56%; MC-SE ±0.15 pts) Round of 32 reach 69.3% ±0.15 Round of 16: 30.07% (95% MC 29.79%–30.36%; MC-SE ±0.15 pts) Round of 16 reach 30.1% ±0.15 Quarter-final: 12.46% (95% MC 12.26%–12.67%; MC-SE ±0.10 pts) Quarter-final reach 12.5% ±0.10 Semi-final: 4.45% (95% MC 4.33%–4.58%; MC-SE ±0.07 pts) Semi-final reach 4.5% ±0.07 Final: 1.45% (95% MC 1.37%–1.52%; MC-SE ±0.04 pts) Final reach 1.4% ±0.04 Champion: 0.45% (95% MC 0.41%–0.49%; MC-SE ±0.02 pts) Champion reach 0.5% ±0.02

On the central forecast, Senegal more likely than not reaches the Round of 32 (69%). Champion probability is 0.5% ± 0.02 pts.

Source · Oxford Football Forecasting model
Group I Confed Advance (top 2) Reach R32
1🇫🇷FranceUEFA68.1%95.2%
2🇳🇴NorwayUEFA50.8%87.7%
3🇸🇳SenegalCAF30.1%69.3%
4🇮🇶IraqAFC3.6%17.6%

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 Senegal against that side. Full bracket & collision matrix →

Match 17 · 2026-06-16 · New York/New Jersey Stadium away
Senegal France
16% win 27.2% draw 56.8% loss
Most likely 0–1 (15.1%) λ 0.71–1.56 Over 2.5 40% · BTTS 41%
Match 41 · 2026-06-23 · New York/New Jersey Stadium away
Senegal Norway
24.5% win 29.8% draw 45.7% loss
Most likely 1–1 (13.8%) λ 0.92–1.35 Over 2.5 40% · BTTS 46%
Match 62 · 2026-06-26 · Toronto Stadium home
Senegal Iraq
57.8% win 27.6% draw 14.6% loss
Most likely 1–0 (16.7%) λ 1.52–0.64 Over 2.5 37% · BTTS 38%
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 Senegal. 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

Senegal vs the field

Elo rating: 1867 vs field median 1780 (1.05× the field) Elo rating 1867 med 1780 Recent NT form: 2.27 ppg vs field median 1.87 ppg (1.21× the field) Recent NT form 2.27 ppg med 1.87 ppg Squad value: €360M vs field median €286M (1.26× the field) Squad value €360M med €286M Squad form (global): 0.218 vs field median 0.211 (1.03× the field) Squad form (global) 0.218 med 0.211 Fitness readiness: 0.712 vs field median 0.707 (1.01× the field) Fitness readiness 0.712 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): 20 vs field median 25 (0.82× the field) Experience (mean caps) 20 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

Senegal on the decoupling axis

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

g = −0.26 ± 0.06: the record outruns the squad price — the team has achieved more than its comparatively modest squad value would predict.

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
23.7mean age
20mean caps
77%in a top-5 league
22distinct clubs
2largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Yehvann DioufGKNiceLigue 1 +1.70z3,690020
2Mamadou SarrDFChelseaFA Cup +2.21z300070
3Kalidou Koulibaly (captain)DFAl-HilalPro League −0.86z5,07451022
4Abdoulaye SeckDFMaccabi HaifaLigat Ha'al −0.55z1,6742224
5Idrissa GueyeMFEvertonPremier League +2.21z2,10121307
6Pathé CissMFRayo VallecanoLa Liga +2.13z3,0562290
7Assane DiaoFWComoSerie A +1.70z1,163250
8Lamine CamaraMFMonacoLigue 1 +1.70z2,5473327
9Bamba DiengFWLorientLigue 1 +1.70z7603222
10Sadio ManéFWAl-NassrPro League −0.86z4,6572112755
11Nicolas JacksonFWBayern MunichBundesliga +1.84z1,35811328
12Cherif NdiayeFWSamsunsporSüper Lig +0.49z1,7479184
13Iliman NdiayeFWEvertonPremier League +2.21z2,8666394
14Ismail JakobsDFGalatasaraySüper Lig +0.49z2,1810290
15Krépin DiattaDFMonacoLigue 1 +1.70z1,2500602
16Édouard MendyGKAl-AhliPro League −0.86z3,2100560
17Pape Matar SarrMFTottenham HotspurPremier League +2.21z2,2332394
18Ismaïla SarrFWCrystal PalacePremier League +2.21z3,617218219
19Moussa NiakhatéDFLyonLigue 1 +1.70z3,7671300
20Ibrahim MbayeFWParis Saint-GermainLigue 1 +1.70z1,1483103
21Habib DiarraMFSunderlandPremier League +2.21z1,5743204
22Bara Sapoko NdiayeMFBayern MunichBundesliga +1.84z148010
23Mory DiawGKLe HavreLigue 1 +1.70z2,655050
24Antoine MendyDFNiceLigue 1 +1.70z3,321260
25El Hadji Malick DioufDFWest Ham UnitedPremier League +2.21z3,0080191
26Pape GueyeMFVillarrealLa Liga +2.13z2,9165415

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

Diaspora in the hosts

69,618

4.0 per 1,000 of home population

Host-language familiarity

Shared

primary language French · spoken in a host

Climate adaptation gap

+0.9°C

home-vs-venue heat differential

Venue extremes

31°C

peak heat index · altitude up to 81 m

Travel

4h

max time-zone shift · nearest venue 5,940 km

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

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

Senegal — Elo since 1961

1920 world #22
Senegal Qualified-field median

Senegal ends the series at 1920 Elo, the world’s 22nd-ranked side — above 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.

100% 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 →