Austria
- Coach
- Ralf Rangnick foreign · German
- Elo (model)
- 1,830 world 27th
- Squad value
- €281M
- Power → Reality
- 20th 20th −0.03 pp · neutral draw
§ 01
The forecast
How far Austria 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
Austria — stage progression
On the central forecast, Austria more likely than not reaches the Round of 32 (77%). Champion probability is 0.5% ± 0.02 pts.
§ 02
The group & the path
Group J advancement odds, the bracket half Austria 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
Earliest possible meetings
No collision rows recorded for this team.
Collision = the earliest round the bracket wiring could pit Austria against that side. Full bracket & collision matrix →
§ 03
Match by match
Austria'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 Austria. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where Austria stands against the median of the 48-team field, metric by metric. The dot is Austria; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
Austria 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
Austria on the decoupling axis
g = +0.31 ± 0.06: 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 25 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 | Alexander Schlager | GK | Red Bull Salzburg | Bundesliga +0.26z | 3,825 | 0 | 26 | 0 |
| 2 | David Affengruber | DF | Elche | La Liga +2.13z | 3,051 | 1 | 1 | 0 |
| 3 | Kevin Danso | DF | Tottenham Hotspur | Premier League +2.21z | 2,363 | 0 | 32 | 0 |
| 4 | Xaver Schlager | MF | RB Leipzig | Bundesliga +1.84z | 2,067 | 3 | 51 | 4 |
| 5 | Stefan Posch | DF | Mainz 05 | Bundesliga +1.84z | 1,755 | 2 | 52 | 5 |
| 6 | Nicolas Seiwald | MF | RB Leipzig | Bundesliga +1.84z | 3,052 | 0 | 47 | 1 |
| 7 | Marko Arnautović | FW | Red Star Belgrade | — | — no club data | — | 133 | 47 |
| 8 | David Alaba (captain) | DF | Real Madrid | Copa del Rey +2.13z | 244 | 0 | 113 | 15 |
| 9 | Marcel Sabitzer | MF | Borussia Dortmund | Bundesliga +1.84z | 2,807 | 1 | 98 | 26 |
| 10 | Florian Grillitsch | MF | Braga | Primeira Liga +1.14z | 1,804 | 3 | 58 | 1 |
| 11 | Michael Gregoritsch | FW | FC Augsburg | Bundesliga +1.84z | 927 | 6 | 75 | 24 |
| 12 | Florian Wiegele | GK | Viktoria Plzeň | Czech Liga +0.20z | 2,446 | 0 | 1 | 0 |
| 13 | Patrick Pentz | GK | Brøndby | Superliga −0.53z | 3,358 | 0 | 18 | 0 |
| 14 | Saša Kalajdžić | FW | LASK | Bundesliga +0.26z | 1,323 | 6 | 22 | 4 |
| 15 | Philipp Lienhart | DF | SC Freiburg | Bundesliga +1.84z | 2,454 | 2 | 41 | 3 |
| 16 | Phillipp Mwene | DF | Mainz 05 | Bundesliga +1.84z | 2,780 | 1 | 30 | 0 |
| 17 | Carney Chukwuemeka | MF | Borussia Dortmund | Bundesliga +1.84z | 1,257 | 3 | 3 | 1 |
| 18 | Romano Schmid | MF | Werder Bremen | Bundesliga +1.84z | 3,078 | 4 | 34 | 3 |
| 20 | Konrad Laimer | MF | Bayern Munich | Bundesliga +1.84z | 3,656 | 3 | 57 | 7 |
| 21 | Patrick Wimmer | FW | VfL Wolfsburg | Bundesliga +1.84z | 1,978 | 5 | 30 | 1 |
| 22 | Alexander Prass | MF | TSG Hoffenheim | Bundesliga +1.84z | 1,438 | 3 | 19 | 0 |
| 23 | Marco Friedl | DF | Werder Bremen | Bundesliga +1.84z | 2,639 | 1 | 11 | 0 |
| 24 | Paul Wanner | MF | PSV Eindhoven | Eredivisie +0.74z | 2,265 | 5 | 3 | 0 |
| 25 | Michael Svoboda | DF | Venezia | Serie B +1.70z | 2,552 | 3 | 4 | 0 |
| 26 | Alessandro Schöpf | MF | Wolfsberger AC | Bundesliga +0.26z | 2,155 | 5 | 35 | 6 |
Source · Official squad announcements · API-Football (global club coverage). 1 of 25 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 96%.
§ 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
56,767
6.0 per 1,000 of home population
Host-language familiarity
Foreign
primary language German
Climate adaptation gap
+5.0°C
home-vs-venue heat differential
Venue extremes
45°C
peak heat index · altitude up to 273 m
Travel
9h
max time-zone shift · nearest venue 6,523 km
Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records
§ 08
Elo trajectory
Austria's long-run strength against the qualified-field median, 1950–2026.
Fig. D4 eloratings.net method · year-end values
Austria — Elo since 1950
Austria ends the series at 1890 Elo, the world’s 27th-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
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 Austria, 1 of 25 players are shown as “— no club data”. Full validation, calibration & conformal coverage →