away
Jordan Austria
14.1% win 24.5% draw 61.4% loss
Most likely 0–1 (13.8%) λ 0.72–1.76 Over 2.5 45% · BTTS 44%
🇯🇴
Jordan
0.0% Champion probability ±0.00 MC-SE
- Coach
- Jamal Sellami foreign · Moroccan
- Elo (model)
- 1,680 world 48th
- Squad value
- €16M
- Power → Reality
- 42nd 48th −0.00 pp · neutral draw
§ 01
The forecast
How far Jordan 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
Jordan — stage progression
Jordan is most likely eliminated before the knockout rounds: 20% to clear the group. Champion probability 0.00%.
§ 02
The group & the path
Group J advancement odds, the bracket half Jordan sits in, and the earliest round they could meet each leading side.
| Group J | Confed | Advance (top 2) | Reach R32 | |
|---|---|---|---|---|
| 1 | 🇦🇷Argentina | CONMEBOL | 69.3% | 98.3% |
| 2 | 🇦🇹Austria | UEFA | 28.6% | 76.7% |
| 3 | 🇩🇿Algeria | CAF | 24.6% | 70.1% |
| 4 | 🇯🇴Jordan | AFC | 3.5% | 20.4% |
Source · Oxford Football Forecasting model
Bracket position Half 1 · Quadrant 3
Earliest possible meetings
No collision rows recorded for this team.
Collision = the earliest round the bracket wiring could pit Jordan against that side. Full bracket & collision matrix →
§ 03
Match by match
Jordan'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%.
home
Jordan Algeria
15.4% win 26.1% draw 58.5% loss
Most likely 0–1 (14.3%) λ 0.73–1.65 Over 2.5 43% · BTTS 43%
home
Jordan Argentina
3.2% win 12% draw 84.8% loss
Most likely 0–2 (16.8%) λ 0.39–2.65 Over 2.5 59% · BTTS 31%
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 Jordan. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where Jordan stands against the median of the 48-team field, metric by metric. The dot is Jordan; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
Jordan 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
Jordan on the decoupling axis
g = −0.12 ± 0.08: 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.
26players
25.7mean age
26mean caps
0%in a top-5 league
15distinct clubs
6largest club bloc
| # | Player | Pos | Club | League | Club min | Gls | Caps | NT gls |
|---|---|---|---|---|---|---|---|---|
| 1 | Yazeed Abulaila | GK | Al-Hussein | — | — no club data | — | 76 | 0 |
| 2 | Mohammad Abu Hashish | DF | Al-Karma | — | — no club data | — | 56 | 1 |
| 3 | Abdallah Nasib | DF | Al-Zawraa | — | — no club data | — | 65 | 3 |
| 4 | Husam Abu Dahab | DF | Al-Faisaly | — | — no club data | — | 18 | 0 |
| 5 | Yazan Al-Arab | DF | FC Seoul | K League 1 | 2,162 | 0 | 80 | 3 |
| 6 | Amer Jamous | MF | Al-Zawraa | — | — no club data | — | 19 | 1 |
| 7 | Mohammad Abu Zrayq | FW | Raja Casablanca | — | — no club data | — | 41 | 5 |
| 8 | Noor Al-Rawabdeh | MF | Selangor | AFC Champions League Two −0.05z | 315 | 0 | 68 | 3 |
| 9 | Ali Olwan | FW | Al-Sailiya | Stars League −2.20z | 126 | 0 | 66 | 29 |
| 10 | Musa Al-Taamari | FW | Rennes | — | — no club data | — | 92 | 24 |
| 11 | Odeh Al-Fakhouri | FW | Pyramids | Premier League −0.83z | 422 | 2 | 10 | 1 |
| 12 | Nour Bani Attiah | GK | Al-Faisaly | — | — no club data | — | 5 | 0 |
| 13 | Mahmoud Al-Mardi | FW | Al-Hussein | AFC Champions League Two −0.05z | 84 | 0 | 89 | 9 |
| 14 | Rajaei Ayed | MF | Al-Hussein | AFC Champions League Two −0.05z | 180 | 0 | 72 | 0 |
| 15 | Ibrahim Sadeh | MF | Al-Karma | — | — no club data | — | 57 | 3 |
| 16 | Mo Abualnadi | DF | Selangor | AFC Champions League Two −0.05z | 385 | 0 | 18 | 0 |
| 17 | Salim Obaid | DF | Al-Hussein | AFC Champions League Two −0.05z | 168 | 0 | 11 | 0 |
| 18 | Yazan Al-Naimat | FW | Al-Arabi | Stars League −2.20z | 40 | 2 | 70 | 26 |
| 19 | Saed Al-Rosan | DF | Al-Hussein | AFC Champions League Two −0.05z | 180 | 0 | 21 | 2 |
| 20 | Mohannad Abu Taha | MF | Al-Quwa Al-Jawiya | — | — no club data | — | 29 | 1 |
| 21 | Nizar Al-Rashdan | MF | Qatar SC | — | — no club data | — | 47 | 4 |
| 22 | Abdallah Al-Fakhouri | GK | Al-Wehdat | — | — no club data | — | 11 | 0 |
| 23 | Ihsan Haddad (captain) | DF | Al-Hussein | — | — no club data | — | 92 | 2 |
| 24 | Ali Azaizeh | FW | Al-Shabab | Pro League −0.86z | 638 | 2 | 4 | 0 |
| 25 | Mohammad Al-Dawoud | MF | Al-Wehdat | — | — no club data | — | 13 | 1 |
| 26 | Anas Badawi | DF | Al-Faisaly | — | — no club data | — | 1 | 0 |
Source · Official squad announcements · API-Football (global club coverage). 15 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 42%.
§ 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
105,564
9.0 per 1,000 of home population
Host-language familiarity
Foreign
primary language Arabic
Climate adaptation gap
−7.0°C
home-vs-venue heat differential
Venue extremes
45°C
peak heat index · altitude up to 177 m
Travel
10h
max time-zone shift · nearest venue 8,928 km
Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records
§ 08
Elo trajectory
Jordan's long-run strength against the qualified-field median, 1963–2026.
Fig. D4 eloratings.net method · year-end values
Jordan — Elo since 1963
Jordan Qualified-field median
Jordan ends the series at 1776 Elo, the world’s 48th-ranked side — below 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
42% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
42% 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 Jordan, 15 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →