away
Algeria Argentina
11.1% win 24.6% draw 64.3% loss
Most likely 0–1 (16.9%) λ 0.56–1.70 Over 2.5 39% · BTTS 36%
🇩🇿
Algeria
0.3% Champion probability ±0.02 MC-SE
- Coach
- Vladimir Petković foreign · Bosnian
- Elo (model)
- 1,760 world 31st
- Squad value
- €282M
- Power → Reality
- 24th 25th −0.03 pp · neutral draw
§ 01
The forecast
How far Algeria 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
Algeria — stage progression
On the central forecast, Algeria more likely than not reaches the Round of 32 (70%). Champion probability is 0.3% ± 0.02 pts.
§ 02
The group & the path
Group J advancement odds, the bracket half Algeria 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 Algeria against that side. Full bracket & collision matrix →
§ 03
Match by match
Algeria'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%.
away
Algeria Jordan
58.5% win 26.1% draw 15.4% loss
Most likely 1–0 (14.3%) λ 1.65–0.73 Over 2.5 43% · BTTS 43%
home
Algeria Austria
30.3% win 31.1% draw 38.6% loss
Most likely 1–1 (14.4%) λ 1.02–1.18 Over 2.5 38% · BTTS 45%
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 Algeria. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where Algeria stands against the median of the 48-team field, metric by metric. The dot is Algeria; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
Algeria 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
Algeria on the decoupling axis
g = −0.20 ± 0.07: 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
26.1mean age
21mean caps
46%in a top-5 league
24distinct clubs
2largest club bloc
| # | Player | Pos | Club | League | Club min | Gls | Caps | NT gls |
|---|---|---|---|---|---|---|---|---|
| 1 | Melvin Mastil | GK | Stade Nyonnais | — | — no club data | — | 1 | 0 |
| 2 | Aïssa Mandi | DF | Lille | Ligue 1 +1.70z | 3,289 | 1 | 117 | 7 |
| 3 | Achref Abada | DF | USM Alger | — | — no club data | — | 7 | 1 |
| 4 | Mohamed Amine Tougai | DF | Espérance de Tunis | Ligue 1 −0.28z | 720 | 4 | 29 | 2 |
| 5 | Zineddine Belaïd | DF | JS Kabylie | Ligue 1 | 540 | 1 | 10 | 1 |
| 6 | Ramiz Zerrouki | MF | Twente | Eredivisie +0.74z | 2,846 | 3 | 52 | 3 |
| 7 | Riyad Mahrez (captain) | FW | Al-Ahli | Pro League −0.86z | 3,229 | 8 | 114 | 38 |
| 8 | Houssem Aouar | MF | Al-Ittihad | Pro League −0.86z | 3,084 | 15 | 20 | 6 |
| 9 | Amine Gouiri | FW | Marseille | Ligue 1 +1.70z | 1,702 | 11 | 22 | 8 |
| 10 | Farès Chaïbi | MF | Eintracht Frankfurt | Bundesliga +1.84z | 2,509 | 3 | 29 | 3 |
| 11 | Anis Hadj Moussa | FW | Feyenoord | Eredivisie +0.74z | 3,443 | 14 | 14 | 1 |
| 12 | Nadhir Benbouali | FW | Győri ETO | NB I | 1,240 | 7 | 3 | 1 |
| 13 | Jaouen Hadjam | DF | Young Boys | Super League −0.07z | 2,150 | 2 | 17 | 3 |
| 14 | Hicham Boudaoui | MF | Nice | Ligue 1 +1.70z | 2,170 | 1 | 32 | 0 |
| 15 | Rayan Aït-Nouri | DF | Manchester City | Premier League +2.21z | 2,022 | 0 | 28 | 0 |
| 16 | Oussama Benbot | GK | USM Alger | Ligue 1 | 924 | 0 | 2 | 0 |
| 17 | Rafik Belghali | DF | Hellas Verona | Serie A +1.70z | 1,926 | 2 | 12 | 1 |
| 18 | Mohamed Amoura | FW | VfL Wolfsburg | Bundesliga +1.84z | 2,065 | 8 | 45 | 19 |
| 19 | Nabil Bentaleb | MF | Lille | Ligue 1 +1.70z | 2,290 | 2 | 59 | 6 |
| 20 | Adil Boulbina | FW | Al-Duhail | Stars League −2.20z | 1,255 | 8 | 11 | 6 |
| 21 | Ramy Bensebaini | DF | Borussia Dortmund | Bundesliga +1.84z | 3,305 | 7 | 81 | 7 |
| 22 | Ibrahim Maza | MF | Bayer Leverkusen | Bundesliga +1.84z | 2,988 | 5 | 16 | 2 |
| 23 | Luca Zidane | GK | Granada | — | — no club data | — | 7 | 0 |
| 24 | Yacine Titraoui | MF | Charleroi | Jupiler Pro League −0.07z | 2,904 | 5 | 5 | 0 |
| 25 | Farès Ghedjemis | FW | Frosinone | Serie B +1.70z | 3,098 | 15 | 1 | 1 |
| 26 | Samir Chergui | DF | Paris FC | Ligue 1 +1.70z | 1,155 | 1 | 4 | 0 |
Source · Official squad announcements · API-Football (global club coverage). 3 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 88%.
§ 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
98,228
2.0 per 1,000 of home population
Host-language familiarity
Foreign
primary language Arabic
Climate adaptation gap
+1.2°C
home-vs-venue heat differential
Venue extremes
37°C
peak heat index · altitude up to 273 m
Travel
8h
max time-zone shift · nearest venue 6,207 km
Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records
§ 08
Elo trajectory
Algeria's long-run strength against the qualified-field median, 1963–2026.
Fig. D4 eloratings.net method · year-end values
Algeria — Elo since 1963
Algeria Qualified-field median
Algeria ends the series at 1873 Elo, the world’s 31st-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
88% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
88% 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 Algeria, 3 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →