Senegal
- Coach
- Pape Thiaw home · Senegalese
- Elo (model)
- 1,867 world 22nd
- Squad value
- €360M
- Power → Reality
- 22nd 22nd −0.07 pp · neutral draw
§ 01
The forecast
How far Senegal 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
Senegal — stage progression
On the central forecast, Senegal more likely than not reaches the Round of 32 (69%). Champion probability is 0.5% ± 0.02 pts.
§ 02
The group & the path
Group I advancement odds, the bracket half Senegal 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
No collision rows recorded for this team.
Collision = the earliest round the bracket wiring could pit Senegal against that side. Full bracket & collision matrix →
§ 03
Match by match
Senegal'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 Senegal. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where Senegal stands against the median of the 48-team field, metric by metric. The dot is Senegal; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
Senegal 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
Senegal on the decoupling axis
g = −0.26 ± 0.06: the record outruns the squad price — the team has achieved more than its comparatively modest squad value would predict.
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 | Yehvann Diouf | GK | Nice | Ligue 1 +1.70z | 3,690 | 0 | 2 | 0 |
| 2 | Mamadou Sarr | DF | Chelsea | FA Cup +2.21z | 300 | 0 | 7 | 0 |
| 3 | Kalidou Koulibaly (captain) | DF | Al-Hilal | Pro League −0.86z | 5,074 | 5 | 102 | 2 |
| 4 | Abdoulaye Seck | DF | Maccabi Haifa | Ligat Ha'al −0.55z | 1,674 | 2 | 22 | 4 |
| 5 | Idrissa Gueye | MF | Everton | Premier League +2.21z | 2,101 | 2 | 130 | 7 |
| 6 | Pathé Ciss | MF | Rayo Vallecano | La Liga +2.13z | 3,056 | 2 | 29 | 0 |
| 7 | Assane Diao | FW | Como | Serie A +1.70z | 1,163 | 2 | 5 | 0 |
| 8 | Lamine Camara | MF | Monaco | Ligue 1 +1.70z | 2,547 | 3 | 32 | 7 |
| 9 | Bamba Dieng | FW | Lorient | Ligue 1 +1.70z | 760 | 3 | 22 | 2 |
| 10 | Sadio Mané | FW | Al-Nassr | Pro League −0.86z | 4,657 | 21 | 127 | 55 |
| 11 | Nicolas Jackson | FW | Bayern Munich | Bundesliga +1.84z | 1,358 | 11 | 32 | 8 |
| 12 | Cherif Ndiaye | FW | Samsunspor | Süper Lig +0.49z | 1,747 | 9 | 18 | 4 |
| 13 | Iliman Ndiaye | FW | Everton | Premier League +2.21z | 2,866 | 6 | 39 | 4 |
| 14 | Ismail Jakobs | DF | Galatasaray | Süper Lig +0.49z | 2,181 | 0 | 29 | 0 |
| 15 | Krépin Diatta | DF | Monaco | Ligue 1 +1.70z | 1,250 | 0 | 60 | 2 |
| 16 | Édouard Mendy | GK | Al-Ahli | Pro League −0.86z | 3,210 | 0 | 56 | 0 |
| 17 | Pape Matar Sarr | MF | Tottenham Hotspur | Premier League +2.21z | 2,233 | 2 | 39 | 4 |
| 18 | Ismaïla Sarr | FW | Crystal Palace | Premier League +2.21z | 3,617 | 21 | 82 | 19 |
| 19 | Moussa Niakhaté | DF | Lyon | Ligue 1 +1.70z | 3,767 | 1 | 30 | 0 |
| 20 | Ibrahim Mbaye | FW | Paris Saint-Germain | Ligue 1 +1.70z | 1,148 | 3 | 10 | 3 |
| 21 | Habib Diarra | MF | Sunderland | Premier League +2.21z | 1,574 | 3 | 20 | 4 |
| 22 | Bara Sapoko Ndiaye | MF | Bayern Munich | Bundesliga +1.84z | 148 | 0 | 1 | 0 |
| 23 | Mory Diaw | GK | Le Havre | Ligue 1 +1.70z | 2,655 | 0 | 5 | 0 |
| 24 | Antoine Mendy | DF | Nice | Ligue 1 +1.70z | 3,321 | 2 | 6 | 0 |
| 25 | El Hadji Malick Diouf | DF | West Ham United | Premier League +2.21z | 3,008 | 0 | 19 | 1 |
| 26 | Pape Gueye | MF | Villarreal | La Liga +2.13z | 2,916 | 5 | 41 | 5 |
Source · Official squad announcements · API-Football (global club coverage). Every player’s club season matched in API-Football.
§ 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
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
§ 08
Elo trajectory
Senegal's long-run strength against the qualified-field median, 1961–2026.
Fig. D4 eloratings.net method · year-end values
Senegal — Elo since 1961
Senegal ends the series at 1920 Elo, the world’s 22nd-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). Full validation, calibration & conformal coverage →