France
- Coach
- Didier Deschamps home · French
- Elo (model)
- 2,062 world 3rd
- Squad value
- €1609M
- Power → Reality
- 4th 5th −0.15 pp · neutral draw
§ 01
The forecast
How far France goes — the survival probability at each stage of the bracket, from the tournament Monte-Carlo simulation. Each bar carries its ±1.96·MC-SE interval.
Fig. D1 Fixture-aware · 100k sims
France — stage progression
On the central forecast, France more likely than not reaches the Round of 16 (68%). Champion probability is 8.9% ± 0.09 pts.
§ 02
The group & the path
Group I advancement odds, the bracket half France sits in, and the earliest round they could meet each leading side.
| Group I | Confed | Advance (top 2) | Reach R32 | |
|---|---|---|---|---|
| 1 | 🇫🇷France | UEFA | 68.1% | 95.2% |
| 2 | 🇳🇴Norway | UEFA | 50.8% | 87.7% |
| 3 | 🇸🇳Senegal | CAF | 30.1% | 69.3% |
| 4 | 🇮🇶Iraq | AFC | 3.6% | 17.6% |
Source · Oxford Football Forecasting model
Earliest possible meetings
Collision = the earliest round the bracket wiring could pit France against that side. Full bracket & collision matrix →
§ 03
Match by match
France's three group fixtures, each with the predicted win / draw / loss split and the single most-likely scoreline. Probabilities are this team's own orientation; they sum to 100%.
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.
§ 04
Strength profile
Where France stands against the median of the 48-team field, metric by metric. The dot is France; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
France vs the field
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.
§ 05
History vs squad
The decoupling residual g — whether the squad’s market value sits above, or below, what the team’s record predicts.
Fig. D3 Bayesian projection residual g
France on the decoupling axis
g = +0.24 ± 0.09: the squad is valued above its record — the transfer market rates this side above what its results have earned.
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 →
§ 06
The squad
All 26 selected players — club, league (with relative strength), club-season minutes and goals, caps. Sort any column.
| # | Player | Pos | Club | League | Club min | Gls | Caps | NT gls |
|---|---|---|---|---|---|---|---|---|
| 1 | Brice Samba | GK | Rennes | Ligue 1 +1.70z | 3,060 | 0 | 4 | 0 |
| 2 | Malo Gusto | DF | Chelsea | Premier League +2.21z | 4,314 | 3 | 10 | 0 |
| 3 | Lucas Digne | DF | Aston Villa | Premier League +2.21z | 3,028 | 0 | 57 | 0 |
| 4 | Dayot Upamecano | DF | Bayern Munich | Bundesliga +1.84z | 3,646 | 2 | 37 | 2 |
| 5 | Jules Koundé | DF | Barcelona | La Liga +2.13z | 3,627 | 3 | 47 | 0 |
| 6 | Manu Koné | MF | Roma | Serie A +1.70z | 2,765 | 2 | 13 | 0 |
| 7 | Ousmane Dembélé | FW | Paris Saint-Germain | Ligue 1 +1.70z | 2,520 | 25 | 58 | 7 |
| 8 | Aurélien Tchouaméni | MF | Real Madrid | La Liga +2.13z | 4,475 | 2 | 45 | 3 |
| 9 | Marcus Thuram | FW | Inter Milan | Serie A +1.70z | 3,077 | 19 | 34 | 3 |
| 10 | Kylian Mbappé (captain) | FW | Real Madrid | La Liga +2.13z | 3,755 | 43 | 97 | 56 |
| 11 | Michael Olise | FW | Bayern Munich | Bundesliga +1.84z | 4,387 | 25 | 16 | 4 |
| 12 | Bradley Barcola | FW | Paris Saint-Germain | Ligue 1 +1.70z | 3,258 | 13 | 19 | 3 |
| 13 | N'Golo Kanté | MF | Fenerbahçe | Süper Lig +0.49z | 1,430 | 2 | 68 | 2 |
| 14 | Adrien Rabiot | MF | Milan | — | — no club data | — | 58 | 7 |
| 15 | Ibrahima Konaté | DF | Liverpool | Premier League +2.21z | 4,376 | 2 | 28 | 0 |
| 16 | Mike Maignan | GK | Milan | — | — no club data | — | 39 | 0 |
| 17 | William Saliba | DF | Arsenal | Premier League +2.21z | 4,254 | 1 | 31 | 0 |
| 18 | Warren Zaïre-Emery | MF | Paris Saint-Germain | Ligue 1 +1.70z | 4,343 | 3 | 11 | 1 |
| 19 | Théo Hernandez | DF | Al-Hilal | Pro League −0.86z | 3,486 | 7 | 43 | 2 |
| 20 | Désiré Doué | FW | Paris Saint-Germain | Ligue 1 +1.70z | 2,954 | 14 | 6 | 2 |
| 21 | Lucas Hernandez | DF | Paris Saint-Germain | Ligue 1 +1.70z | 2,172 | 0 | 42 | 0 |
| 22 | Jean-Philippe Mateta | FW | Crystal Palace | Premier League +2.21z | 3,405 | 16 | 4 | 2 |
| 23 | Robin Risser | GK | Lens | Ligue 1 +1.70z | 3,420 | 0 | 0 | 0 |
| 24 | Rayan Cherki | MF | Manchester City | Premier League +2.21z | 3,088 | 10 | 6 | 2 |
| 25 | Maghnes Akliouche | MF | Monaco | Ligue 1 +1.70z | 3,515 | 7 | 8 | 1 |
| 26 | Maxence Lacroix | DF | Crystal Palace | Premier League +2.21z | 4,848 | 3 | 3 | 0 |
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%.
§ 07
Tournament context
The host-nation environment this team meets — diaspora support, climate and altitude exposure at their venues, language familiarity.
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
§ 08
Elo trajectory
France's long-run strength against the qualified-field median, 1950–2026.
Fig. D4 eloratings.net method · year-end values
France — Elo since 1950
France ends the series at 2123 Elo, the world’s 3rd-ranked side — above the qualified-field median.
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.
§ 09
Data coverage
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 →