WC 2026 · Forecasting Oxford Football Forecasting
🇫🇷

France

UEFA Group I
8.9% Champion probability ±0.09 MC-SE
Coach
Didier Deschamps home · French
Elo (model)
2,062 world 3rd
Squad value
€1609M
Power → Reality
4th 5th −0.15 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

France — stage progression

Round of 32: 95.20% (95% MC 95.07%–95.34%; MC-SE ±0.07 pts) Round of 32 reach 95.2% ±0.07 Round of 16: 68.15% (95% MC 67.86%–68.44%; MC-SE ±0.15 pts) Round of 16 reach 68.1% ±0.15 Quarter-final: 44.32% (95% MC 44.01%–44.63%; MC-SE ±0.16 pts) Quarter-final reach 44.3% ±0.16 Semi-final: 28.26% (95% MC 27.98%–28.54%; MC-SE ±0.14 pts) Semi-final reach 28.3% ±0.14 Final: 16.14% (95% MC 15.91%–16.37%; MC-SE ±0.12 pts) Final reach 16.1% ±0.12 Champion: 8.94% (95% MC 8.76%–9.12%; MC-SE ±0.09 pts) Champion reach 8.9% ±0.09

On the central forecast, France more likely than not reaches the Round of 16 (68%). Champion probability is 8.9% ± 0.09 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

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

Match 17 · 2026-06-16 · New York/New Jersey Stadium home
France Senegal
56.8% win 27.2% draw 16% loss
Most likely 1–0 (15.1%) λ 1.56–0.71 Over 2.5 40% · BTTS 41%
Match 42 · 2026-06-22 · Philadelphia Stadium home
France Iraq
78.2% win 16.6% draw 5.2% loss
Most likely 2–0 (17.2%) λ 2.24–0.44 Over 2.5 50% · BTTS 32%
Match 61 · 2026-06-26 · Boston Stadium away
France Norway
45.9% win 29.6% draw 24.5% loss
Most likely 1–1 (13.8%) λ 1.37–0.93 Over 2.5 40% · BTTS 46%
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 France. 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

France vs the field

Elo rating: 2062 vs field median 1780 (1.16× the field) Elo rating 2062 med 1780 Recent NT form: 2.13 ppg vs field median 1.87 ppg (1.14× the field) Recent NT form 2.13 ppg med 1.87 ppg Squad value: €1609M vs field median €286M (5.63× the field) Squad value €1609M med €286M Squad form (global): 0.384 vs field median 0.211 (1.82× the field) Squad form (global) 0.384 med 0.211 Fitness readiness: 0.886 vs field median 0.707 (1.25× the field) Fitness readiness 0.886 med 0.707 Familiarity / chemistry: 0.043 vs field median 0.015 (2.80× the field) Familiarity / chemistry 0.043 med 0.015 Experience (mean caps): 28 vs field median 25 (1.12× the field) Experience (mean caps) 28 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

France on the decoupling axis

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

g = +0.24 ± 0.09: 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
25.6mean age
28mean caps
85%in a top-5 league
18distinct clubs
5largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Brice SambaGKRennesLigue 1 +1.70z3,060040
2Malo GustoDFChelseaPremier League +2.21z4,3143100
3Lucas DigneDFAston VillaPremier League +2.21z3,0280570
4Dayot UpamecanoDFBayern MunichBundesliga +1.84z3,6462372
5Jules KoundéDFBarcelonaLa Liga +2.13z3,6273470
6Manu KonéMFRomaSerie A +1.70z2,7652130
7Ousmane DembéléFWParis Saint-GermainLigue 1 +1.70z2,52025587
8Aurélien TchouaméniMFReal MadridLa Liga +2.13z4,4752453
9Marcus ThuramFWInter MilanSerie A +1.70z3,07719343
10Kylian Mbappé (captain)FWReal MadridLa Liga +2.13z3,755439756
11Michael OliseFWBayern MunichBundesliga +1.84z4,38725164
12Bradley BarcolaFWParis Saint-GermainLigue 1 +1.70z3,25813193
13N'Golo KantéMFFenerbahçeSüper Lig +0.49z1,4302682
14Adrien RabiotMFMilanno club data587
15Ibrahima KonatéDFLiverpoolPremier League +2.21z4,3762280
16Mike MaignanGKMilanno club data390
17William SalibaDFArsenalPremier League +2.21z4,2541310
18Warren Zaïre-EmeryMFParis Saint-GermainLigue 1 +1.70z4,3433111
19Théo HernandezDFAl-HilalPro League −0.86z3,4867432
20Désiré DouéFWParis Saint-GermainLigue 1 +1.70z2,9541462
21Lucas HernandezDFParis Saint-GermainLigue 1 +1.70z2,1720420
22Jean-Philippe MatetaFWCrystal PalacePremier League +2.21z3,4051642
23Robin RisserGKLensLigue 1 +1.70z3,420000
24Rayan CherkiMFManchester CityPremier League +2.21z3,0881062
25Maghnes AklioucheMFMonacoLigue 1 +1.70z3,515781
26Maxence LacroixDFCrystal PalacePremier League +2.21z4,848330

Source · Official squad announcements · API-Football (global club coverage). 2 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 92%.

Diaspora in the hosts

286,620

4.0 per 1,000 of home population

Host-language familiarity

Shared

primary language French · spoken in a host

Climate adaptation gap

+2.7°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 5,560 km

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

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

France — Elo since 1950

2123 world #3
France Qualified-field median

France ends the series at 2123 Elo, the world’s 3rd-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.

92% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
92% 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 France, 2 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →