Group J · Matchday 1
AustriavsJordan
2026-06-16·21:00 localPredictions finalised
The forecast
Match-outcome probability
- Austria win54.7%
- Draw24.7%
- Jordan win20.6%
A clash of identities: Austria's high-press approach meets Jordan's balanced style in a fixture the model gives to Austria at 71%.
Why the model says this
Favoring Austria
- ·Austria holds a significantly higher FIFA ranking, placed 24th globally compared to Jordan's 66th.
- ·The Elo rating system indicates a 137-point advantage for Austria.
- ·Expected goals projections show Austria creating substantially more opportunities, with 1.9 xG compared to Jordan's 0.68 xG.
- ·Multiple model components show strong favour for Austria, with the DC model at 65.8% for a home win and the HP model at 66.1%.
Favoring Jordan
- ·Jordan has avoided defeat in their last two matches, securing 2-2 draws in both recent friendlies.
- ·Jordan has recorded 3 wins in their last 6 fixtures, demonstrating capability to secure victories.
What the model can't fully price
- ·Two projected starters across both squads are carrying fitness doubts. The model's current lineup channel does not account for specific player absences.
Form check
Austria
ImprovingAustria enters this match in strong form, having secured 4 wins and 1 draw in their last 5 fixtures, including a dominant 10-0 victory in World Cup qualification. Their recent results suggest a potent attack and solid defence.
4 wins in their last 5 matches
Jordan
SteadyJordan's recent form is mixed, with 3 wins, 2 draws, and 1 loss in their last 6 outings. They have shown resilience by securing 2-2 draws in their two most recent friendly matches.
2 draws in their last 2 matches
Analysis
How it plays out
Austria press high and force the tempo. Jordan's balanced setup needs to absorb that pressure early and find the right moments to play forward. Austria will expect to hold 53% possession. Jordan need their shape to stay compact without the ball and be clinical when they win it back.
What decides it
Austria press high (PPDA 17.0). If the press doesn't win the ball early, the space behind their back line becomes exposed. Marcel Sabitzer carries the marginally higher scoring probability (6.5% vs 3.2%).
Off the pitch
No major off-pitch asymmetries. This one is decided by the football.
The angle
Likely the last World Cup for Marko Arnautović. Tournament experience at this level is hard to quantify but hard to replace.
▸Goals & scorelines
Likeliest score 1–0 (13.7%) · xG 1.9 - 0.7
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–013.7%
- 2–013.7%
- 1–110.3%
- 2–19.3%
- 3–08.7%
From the Dixon-Coles joint Poisson with the low-score correction. Scorelines are listed in probability order; this is a description of the model's distribution, not a recommendation.
Most likely half-time scorelines
- 0–027.9%
- 1–025.6%
- 2–012.5%
- 1–19.4%
- 0–18.8%
Same Dixon-Coles fit as the full-time list above, with rates halved to a 45-minute window and the low-score correction applied to that 1st-half block. The 0-0 row sits higher here than at full-time because fewer minutes have elapsed.
Goal totals
- More than 0.5 goals91.9%
- More than 1.5 goals73.7%
- More than 2.5 goals47.9%
- More than 3.5 goals26.2%
- More than 4.5 goals12.1%
- More than 5.5 goals4.8%
- Both teams score42.7%
Each row is the probability the match finishes with more than the listed number of goals. Both-teams-to-score is the probability each side scores at least once. All values are marginals of the Dixon-Coles joint goal grid that produces the scoreline list above — not market lines or any other operator construct.
Event-typed probabilities
- Austria clean sheetOpposing team scores zero50.6%
- Jordan clean sheetOpposing team scores zero14.8%
Derived from the same Dixon-Coles joint distribution as the scoreline list. These are descriptive event probabilities — see CLAUDE.md §3/§4 (formerly COMPLIANCE.md §4.2.7) for the framing the project uses.
Win-margin probability
- Austria by 4+8.1%
- Austria by 3+20.0%
- Austria by 2+40.7%
- Austria by 1+66.0%
- Draw22.1%
- Jordan by 1+11.9%
- Jordan by 2+3.3%
- Jordan by 3+0.6%
- Jordan by 4+0.1%
Each row is the probability the match ends with the listed margin or larger in that direction. Marginal of the Dixon-Coles joint goal grid; the “by 1+” rows plus the draw row sum to 1.
▸How the match unfolds
Over 2.5 goals 47.9% · BTTS 42.7%
Game state through the match
- Austria ahead66.6%
- Level20.9%
- Jordan ahead12.5%
Probability of each game state at minutes 0, 15, 30, 45, 60, 75, 90 — derived from two independent thinned-Poisson processes with the Dixon-Coles per-team rates. The three lines always sum to 1 at each minute. The right column shows the state at the match's closing minute.
When the first goal arrives
- 0–1535.0%
- 15–3022.8%
- 30–4514.8%
- 45–609.6%
- 60–756.2%
- 75–904.0%
- No goal7.5%
Probability the match's first goal arrives in each 15-minute window. Homogeneous Poisson with combined rate λ = λh + λa from the Dixon-Coles fit; the seven rows (six windows + no-goal tail) sum to 1.
Half-time / full-time grid
| HT ↓ / FT → | HAustria win | DDraw | AJordan win |
|---|---|---|---|
| HAustria ahead | 45.3% | 3.5% | 0.6% |
| DLevel | 19.1% | 14.1% | 4.9% |
| AJordan ahead | 2.1% | 3.4% | 6.8% |
Each cell is P(half-time result, full-time result). All nine cells sum to 1. Derived from a halved-λ Dixon-Coles fit for the first half plus an independent-Poisson second-half convolution.
Comeback probability
- Austria trail at HT, avoid defeat at FT5.5%
- Jordan trail at HT, avoid defeat at FT4.2%
Joint probability — P(side trailing at half-time AND avoiding defeat at full-time). NOT conditional on trailing at HT. Derived from the same half-time / full-time decomposition that produces the HT/FT grid above; a tied first half is neither a home nor an away comeback opportunity.
Cards
- Expected yellow cardsMean of the Poisson on total yellow cards.3.45
- Total yellows over 2.567.0%
- Total yellows over 3.545.3%
- Total yellows over 4.526.5%
- Any red cardP(at least one red card in the match).9.5%
Referee not yet assigned. Using the 2026 pool-mean per-match rate as a placeholder; the model picks up the referee's personal rate once the assignment is published. Total yellow cards modelled as a Poisson with mean equal to two team baselines plus the referee's deviation from the pool mean. Reds are modelled the same way, independently. See /docs/methodology/.
▸Teams & players
Top scorer: Sabitzer (6.5%)
Match detail
Austria
Model-rated key players: Marcel Sabitzer (MF) — P(scores) 6.5%; Marko Arnautović (FW) — P(scores) 4.9%; Michael Gregoritsch (FW) — P(scores) 4.8%.
Austria under Ralf Rangnick play a high press game with 53% possession. They apply moderate pressing intensity (PPDA 17.0).
Austria need their high press to force turnovers in dangerous areas — if opponents can play through the press, the space left behind is vulnerable. Physical conditioning and squad rotation will be critical to sustain pressing intensity across a long tournament. Managing minutes for Marko Arnautović across what could be seven matches will test the coaching staff's rotation planning.
Jordan
Model-rated key players: Ahmad Ersan (FW) — P(scores) 3.3%; Ali Olwan (FW) — P(scores) 3.3%; Baha' Faisal (FW) — P(scores) 3.3%.
Limited recent tournament data is available for Jordan's tactical profile. Early indicators suggest a balanced approach.
Jordan will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries.
Austria's predicted XI averages 1,262 club minutes over the 2024-25 season (light load).
Austria coverage: 89.0% (10/11 XI matched against the FBref Big-5) · Jordan: 0.0% (0/11).
Austria historically converts 11.2% of xG from set-pieces, contributing 0.21 expected set-piece goals in this fixture. Combined, the model expects 0.21 set-piece goals across the 90 minutes.
- P(Austria scores set-piece goal) 19.3%
- P(set-piece goal in match) 19.3%
Austria: Alessandro Schöpf on corners (24 corners), Florian Grillitsch on free kicks (per fbref 2021 22)
If a penalty is awarded to Austria, the model gives 72.0% conversion, 72.0% for Jordan.
Austria primary PK: Marcel Sabitzer (4/4 in 2020-21, per fbref 2021 22).
Derived from the model's per-fixture forecast joint and supporting reference data (predicted squads, set-piece xG share, PK posteriors, club minutes). See /docs/methodology/ for the full methodology.
Tactical forecast
- PPDA
- 17.0
- Possession
- 53%
- Directness (yds/pass)
- 5.7
- Long balls/90
- 34
- Set-piece xG
- 11%
Partial coverage from FotMob match stats (recent qualifiers and friendlies): possession and shot volume only. Press and build-up metrics are not available for this side.
- PPDA
- —
- Possession
- 37%
- Directness (yds/pass)
- —
- Long balls/90
- —
- Set-piece xG
- —
Style profile per side from StatsBomb open-data aggregation across recent international tournaments (Euro 2020/2024, Copa America 2024, AFCON 2023, World Cup 2018/2022). The tactical-fingerprint badge maps each team’s observed style vector into one of eight canonical archetypes via a rule-based classifier; teams with fewer than three matches of qualifying coverage carry an “insufficient-data” label rather than being forced into a default. Sides outside the StatsBomb-open corpus use FotMob team match stats from recent qualifiers and friendlies instead (possession and shot volume only), marked as partial coverage. PPDA = passes the side allows per defensive action (lower = more intense press). Formation distributions are not yet produced — that head of the §2.7 classifier is pending its own data pull. See /docs/methodology/.
Squad depth
Most irreplaceable starters
Austria
- Konrad LaimerFull-backCover: Phillipp Mwene · 0.280.58gap
- Saša KalajdžićStrikerNo natural backup0.55gap
- Michael GregoritschStrikerNo natural backup0.50gap
Jordan
- Musa Al-TaamariWingerCover: Mohammad Abu Zrayq · 0.110.49gap
- Yazan Al-ArabCentre-backCover: Mohammad Abualnadi · 0.060.23gap
- Noor Al-RawabdehCentral midfieldCover: Amer Jamous · 0.000.17gap
Gap = how far a side's rating at the position falls from the starter to his likely in-squad replacement (named under each name). Larger = harder to replace. Descriptive metric, does not feed the published probabilities. Methodology →
Match conditions
- AltitudeNear sea level4 m
- Avg temperatureFive-year mean over the tournament window19.6 °C
- Avg humidity62%
- Heat stressShade WBGT ~20.6 °CLow heat stress
- Pitch surfacenatural grass
Natural-grass NFL stadium; FIFA-standard hybrid pitch for the tournament.
Heat stress is a shade Wet-Bulb Globe Temperature proxy from the venue's climatology mean temperature and humidity; FIFA mandates cooling breaks at WBGT 32 °C. Night kickoff (local time). These are long-window averages, not a match-day forecast, and they are not inputs to the forecast.
Top scorers · P(scores in this match)
- Marcel SabitzerPKMF6.5%
- Marko ArnautovićFW4.9%
- Michael GregoritschFW4.8%
- Ahmad ErsanFW3.3%
- Ali OlwanFW3.3%
- Baha' FaisalFW3.3%
Per-player scoring rate from Model #5 (`p_score_per_match`). Reflects each player's npxG/90, expected minutes, team xG share, and the average opposing-team defence. See /docs/methodology/.
Recent match form
Last match player ratings
Austria
vs Spain · avg 9.0
Worked well: The performance of their goalkeeper, Alexander Schlager, was exceptional, keeping the team in the match for extended periods with crucial saves.
Struggled: Austria struggled significantly to retain possession and mount any sustained offensive movements, remaining largely on the back foot throughout the encounter.
Jordan
vs Argentina · avg 6.4
Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →
Video analysis: player performance
Per-player ratings and event breakdowns from official highlights analysis. Tap a player to see their full match timeline.
5Austria Player #16105'–105'Committed a foul in a dangerous area, which could have led to a scoring opportunity for the opponent.
1fouls▼
Committed a foul in a dangerous area, which could have led to a scoring opportunity for the opponent.
Match timeline
3Austria Player #19116'–222'Displayed poor discipline throughout the match, committing multiple fouls and receiving a yellow card.
2fouls1 yellow▼
Displayed poor discipline throughout the match, committing multiple fouls and receiving a yellow card.
Match timeline
7Jordan Player #10Showed attacking intent and individual skill, creating opportunities despite some execution errors.
Showed attacking intent and individual skill, creating opportunities despite some execution errors.
6Jordan Player #23Consistently drew fouls from the opposition, indicating a disruptive presence and ability to retain possession under pressure.
Consistently drew fouls from the opposition, indicating a disruptive presence and ability to retain possession under pressure.
▸Under the hood
Model-by-model comparison
Austria vs Jordan
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 61.1% | 22.0% | 16.9% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 66.4% | 21.6% | 12.0% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 65.8% | 21.4% | 12.8% |
| Bayesian stackingLearned-weight combination | — | 74.8% | 20.1% | 5.1% |
| Ensemble (published)Uniform average + isotonic calibration | — | 71.1% | 21.6% | 7.2% |
How each model works
- Elo
- Each team carries a single strength rating updated after every match by a margin-aware K-factor. Match probabilities come from the logistic function of the rating gap. Elo is fast-adapting but coarse — it sees only who won and by how much, not how the goals were scored.
- Dixon-Coles
- A Poisson regression on team-level attack and defence parameters, fitted via maximum likelihood with an exponential time-decay weighting. The Dixon-Coles correction adjusts the four low-score cells (0-0, 1-0, 0-1, 1-1) where independent Poisson underestimates dependence. Produces full scoreline distributions, not just H/D/A.
- Hierarchical Poisson
- A Bayesian Poisson model fitted via MCMC (PyMC) with hierarchical priors that pool attack and defence parameters within confederations. Shrinks small-sample teams toward their confederation mean — helpful for nations with few recent competitive fixtures. Slower to fit but better-calibrated on the tails.
- Bayesian stacking
- Optimises simplex weights (w_elo, w_dc, w_hp) to maximise the leave-one-out log-score across a walk-forward backtest (Yao et al. 2018). The result is a weighted average of the three component models' probabilities, then isotonic-calibrated. Adds no extra features — just learns which component to trust more from historical accuracy.
- Ensemble (published)
- Equal-weight average of all three component models, followed by per-class isotonic regression calibration fitted on 24 months of walk-forward out-of-fold predictions. This is the probability published on the site. The uniform mean is deliberately simple — it avoids overfitting to the stacking weights' training window.
Three independent component models feed two combination strategies. The uniform ensemble is the published probability; Bayesian stacking uses learned weights. Amber bars flag >5pp divergence from the published number. Full methodology
Probability decomposition (transparency surface)
- Baseline ensemble — P(Austria win)54.7%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Austria win)54.7%
Decomposition of the published P(Austria win) into the calibrated- baseline plus contributions from the §2.3 expected-XI lineup delta and the §2.7 style-matchup interaction. The §2.7 roadmap is explicit that style effects are second-order to team strength — single-digit-percentage P(win) shifts on extreme style matchups, near-zero on balanced ones. We surface the decomposition for transparency even when the contributions are small; the baseline carries the prediction. Methodology: /docs/methodology.
For this fixture both contributions round to under 0.05pp — the fitted style-matchup pair effect is in the small-magnitude regime the model expects to dominate.
Head-to-head history
| Date | Competition | Venue | Score | Result | xG |
|---|---|---|---|---|---|
| 16 Jun 2026 | FIFA World Cup | NSanta Clara | 3–1 | W | — |
Austria vs Jordan, every senior international meeting in the martj42 results dataset (score from Austria's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for Austria or Jordan.
- Stage:
- Group J · Matchday 1
- Date:
- 16 Jun
Austria and Jordan both come in at close to full strength, so the forecast rests on baseline team strength rather than late team-news swings.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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