WC 2026 · Forecasting Oxford Football Forecasting
🇦🇹

Austria

UEFA Group J
0.5% Champion probability ±0.02 MC-SE
Coach
Ralf Rangnick foreign · German
Elo (model)
1,830 world 27th
Squad value
€281M
Power → Reality
20th 20th −0.03 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Austria — stage progression

Round of 32: 76.66% (95% MC 76.39%–76.92%; MC-SE ±0.13 pts) Round of 32 reach 76.7% ±0.13 Round of 16: 28.58% (95% MC 28.30%–28.86%; MC-SE ±0.14 pts) Round of 16 reach 28.6% ±0.14 Quarter-final: 12.78% (95% MC 12.57%–12.98%; MC-SE ±0.11 pts) Quarter-final reach 12.8% ±0.11 Semi-final: 5.05% (95% MC 4.92%–5.19%; MC-SE ±0.07 pts) Semi-final reach 5.1% ±0.07 Final: 1.87% (95% MC 1.79%–1.96%; MC-SE ±0.04 pts) Final reach 1.9% ±0.04 Champion: 0.55% (95% MC 0.50%–0.59%; MC-SE ±0.02 pts) Champion reach 0.5% ±0.02

On the central forecast, Austria more likely than not reaches the Round of 32 (77%). Champion probability is 0.5% ± 0.02 pts.

Source · Oxford Football Forecasting model
Group J Confed Advance (top 2) Reach R32
1🇦🇷ArgentinaCONMEBOL69.3%98.3%
2🇦🇹AustriaUEFA28.6%76.7%
3🇩🇿AlgeriaCAF24.6%70.1%
4🇯🇴JordanAFC3.5%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 Austria against that side. Full bracket & collision matrix →

Match 20 · 2026-06-17 · San Francisco Bay Area Stadium home
Austria Jordan
61.4% win 24.5% draw 14.1% loss
Most likely 1–0 (13.8%) λ 1.76–0.72 Over 2.5 45% · BTTS 44%
Match 43 · 2026-06-22 · Dallas Stadium away
Austria Argentina
12.5% win 26.1% draw 61.4% loss
Most likely 0–1 (17.0%) λ 0.59–1.61 Over 2.5 38% · BTTS 36%
Match 69 · 2026-06-28 · Kansas City Stadium away
Austria Algeria
38.6% win 31.1% draw 30.3% loss
Most likely 1–1 (14.4%) λ 1.18–1.02 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 Austria. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.

Fig. D2 Relative to the 48-team median

Austria vs the field

Elo rating: 1830 vs field median 1780 (1.03× the field) Elo rating 1830 med 1780 Recent NT form: 2.20 ppg vs field median 1.87 ppg (1.18× the field) Recent NT form 2.20 ppg med 1.87 ppg Squad value: €281M vs field median €286M (0.99× the field) Squad value €281M med €286M Squad form (global): 0.149 vs field median 0.211 (0.71× the field) Squad form (global) 0.149 med 0.211 Fitness readiness: 0.785 vs field median 0.707 (1.11× the field) Fitness readiness 0.785 med 0.707 Familiarity / chemistry: 0.013 vs field median 0.015 (0.86× the field) Familiarity / chemistry 0.013 med 0.015 Experience (mean caps): 34 vs field median 25 (1.38× the field) Experience (mean caps) 34 med 25

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.

Source · Oxford Football Forecasting model

Fig. D3 Bayesian projection residual g

Austria on the decoupling axis

aligned (0) ← record > squad price squad valued > record →

g = +0.31 ± 0.06: the squad is valued above its record — the transfer market rates this side above what its results have earned.

Source · Oxford Football Forecasting model
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 →

25players
26.4mean age
34mean caps
84%in a top-5 league
21distinct clubs
2largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Alexander SchlagerGKRed Bull SalzburgBundesliga +0.26z3,8250260
2David AffengruberDFElcheLa Liga +2.13z3,051110
3Kevin DansoDFTottenham HotspurPremier League +2.21z2,3630320
4Xaver SchlagerMFRB LeipzigBundesliga +1.84z2,0673514
5Stefan PoschDFMainz 05Bundesliga +1.84z1,7552525
6Nicolas SeiwaldMFRB LeipzigBundesliga +1.84z3,0520471
7Marko ArnautovićFWRed Star Belgradeno club data13347
8David Alaba (captain)DFReal MadridCopa del Rey +2.13z244011315
9Marcel SabitzerMFBorussia DortmundBundesliga +1.84z2,80719826
10Florian GrillitschMFBragaPrimeira Liga +1.14z1,8043581
11Michael GregoritschFWFC AugsburgBundesliga +1.84z92767524
12Florian WiegeleGKViktoria PlzeňCzech Liga +0.20z2,446010
13Patrick PentzGKBrøndbySuperliga −0.53z3,3580180
14Saša KalajdžićFWLASKBundesliga +0.26z1,3236224
15Philipp LienhartDFSC FreiburgBundesliga +1.84z2,4542413
16Phillipp MweneDFMainz 05Bundesliga +1.84z2,7801300
17Carney ChukwuemekaMFBorussia DortmundBundesliga +1.84z1,257331
18Romano SchmidMFWerder BremenBundesliga +1.84z3,0784343
20Konrad LaimerMFBayern MunichBundesliga +1.84z3,6563577
21Patrick WimmerFWVfL WolfsburgBundesliga +1.84z1,9785301
22Alexander PrassMFTSG HoffenheimBundesliga +1.84z1,4383190
23Marco FriedlDFWerder BremenBundesliga +1.84z2,6391110
24Paul WannerMFPSV EindhovenEredivisie +0.74z2,265530
25Michael SvobodaDFVeneziaSerie B +1.70z2,552340
26Alessandro SchöpfMFWolfsberger ACBundesliga +0.26z2,1555356

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%.

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

Fig. D4 eloratings.net method · year-end values

Austria — Elo since 1950

1890 world #27
Austria Qualified-field median

Austria ends the series at 1890 Elo, the world’s 27th-ranked side — below the qualified-field median.

Source · eloratings.net
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.

96% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
96% 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 Austria, 1 of 25 players are shown as “— no club data”. Full validation, calibration & conformal coverage →