Group J · Matchday 2
ArgentinavsAustria
2026-06-22·12:00 localPredictions finalised
The forecast
Match-outcome probability
- Argentina win56.1%
- Draw28.0%
- Austria win15.9%
A 286-point Elo gap frames this as a significant mismatch, yet the model still gives Austria a 10% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Argentina
- ·Argentina holds a significant Elo rating advantage of 286 points over Austria.
- ·Argentina is ranked 2nd in FIFA, significantly higher than Austria at 24th.
- ·Argentina's expected goals (xG) of 1.59 is more than double Austria's 0.65.
- ·In two historical encounters, Argentina has not lost to Austria, securing one win and one draw.
Favoring Austria
- ·Austria's recent form shows a goal difference of +16 (19 goals scored, 3 conceded) over their last six matches, slightly surpassing Argentina's +14.
- ·Austria has secured 4 wins and 1 draw in their last six fixtures, demonstrating a solid run of recent results.
What the model can't fully price
- ·The model does not account for the impact of the one player carrying a fitness doubt across both squads, as its lineup channel currently contributes zero to the forecast.
Form check
Argentina
ImprovingArgentina enters this match in strong form, having won five of their last six fixtures. During this period, they scored 16 goals and conceded only 2, with their sole defeat being a narrow 0-1 loss.
Five wins in their last six matches.
Austria
ImprovingAustria also demonstrates robust recent form, with four wins and one draw in their last six outings. They have been prolific in front of goal, scoring 19 times while conceding just 3, with their only loss also being a 0-1 result.
Goal difference of +16 over the last six fixtures.
Analysis
How it plays out
Argentina want to build from the back; Austria press high to prevent exactly that. If Argentina play through the press they'll find dangerous space. If they don't, turnovers come in costly areas. Argentina will expect to hold 59% possession. Austria need their shape to stay compact without the ball and be clinical when they win it back.
What decides it
Argentina's possession game (59% avg) requires patience in the final third and quick ball recovery when they lose it. Austria press high (PPDA 17.0). If the press doesn't win the ball early, the space behind their back line becomes exposed. Lionel Messi carries the marginally higher scoring probability (9.8% vs 6.2%).
Off the pitch
Lionel Scaloni (8 years in charge of Argentina) vs Ralf Rangnick (4 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
Argentina are the defending champions. That brings quality but also the weight of being everyone's scalp match.
▸Goals & scorelines
Likeliest score 1–0 (17.0%) · xG 1.6 - 0.6
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–017.0%
- 2–014.2%
- 0–011.6%
- 1–111.3%
- 2–18.5%
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
- 0–033.6%
- 1–026.2%
- 2–010.7%
- 0–19.5%
- 1–18.5%
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 goals88.4%
- More than 1.5 goals65.4%
- More than 2.5 goals38.0%
- More than 3.5 goals18.3%
- More than 4.5 goals7.4%
- More than 5.5 goals2.5%
- Both teams score36.8%
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
- Argentina clean sheetOpposing team scores zero54.8%
- Austria clean sheetOpposing team scores zero20.0%
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
- Argentina by 4+5.3%
- Argentina by 3+14.9%
- Argentina by 2+34.3%
- Argentina by 1+61.3%
- Draw25.7%
- Austria by 1+13.0%
- Austria by 2+3.3%
- Austria by 3+0.6%
- Austria 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 38.0% · BTTS 36.8%
Game state through the match
- Argentina ahead61.9%
- Level24.4%
- Austria ahead13.6%
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
- 0–1530.8%
- 15–3021.3%
- 30–4514.8%
- 45–6010.2%
- 60–757.1%
- 75–904.9%
- No goal11.0%
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 → | HArgentina win | DDraw | AAustria win |
|---|---|---|---|
| HArgentina ahead | 40.8% | 3.5% | 0.5% |
| DLevel | 19.3% | 17.7% | 5.5% |
| AAustria ahead | 1.7% | 3.4% | 7.4% |
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
- Argentina trail at HT, avoid defeat at FT5.1%
- Austria trail at HT, avoid defeat at FT4.0%
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: Messi (9.8%)
Match detail
Argentina
Model-rated key players: Lionel Messi (FW) — P(scores) 9.8%; Lautaro Martínez (FW) — P(scores) 5.0%; Nicolás González (FW) — P(scores) 3.8%.
Argentina under Lionel Scaloni play a possession dominant game, holding 59% of the ball — among the highest in the tournament field. Their likely shape is a 4-3-3, though they have also used 3-5-2 and 4-4-2. They apply moderate pressing intensity (PPDA 19.1) and build patiently through midfield with 7.8 passes per attacking sequence. They favour high-quality chances (xG/shot 0.163, among the best in the field).
To succeed, Argentina must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match. Managing minutes for Lionel Messi across what could be seven matches will test the coaching staff's rotation planning.
Austria
Model-rated key players: Marcel Sabitzer (MF) — P(scores) 6.3%; Marko Arnautović (FW) — P(scores) 2.4%; Michael Gregoritsch (FW) — P(scores) 2.4%.
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.
Argentina's predicted XI averages 1,997 club minutes over the 2024-25 season (moderate load). Austria's predicted XI averages 1,262 club minutes over the 2024-25 season (light load).
Argentina coverage: 85.0% (11/11 XI matched against the FBref Big-5) · Austria: 89.0% (10/11).
Argentina historically converts 17.1% of xG from set-pieces, contributing 0.28 expected set-piece goals in this fixture. Austria converts 11.2% from set-pieces (0.07 expected). Combined, the model expects 0.34 set-piece goals across the 90 minutes.
- P(Argentina scores set-piece goal) 24.0%
- P(Austria scores set-piece goal) 6.5%
- P(set-piece goal in match) 29.0%
Argentina: Lionel Messi on corners (32 corners), Guido Rodríguez on free kicks (per fbref 2022 23) · Austria: Alessandro Schöpf on corners (24 corners), Florian Grillitsch on free kicks (per fbref 2021 22)
If a penalty is awarded to Argentina, the model gives 77.0% conversion, 72.0% for Austria.
Argentina primary PK: Lionel Messi (3/5 in 2020-21, per fbref 2022 23) · 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
- 19.1
- Possession
- 59%
- Directness (yds/pass)
- 4.3
- Long balls/90
- 27
- Set-piece xG
- 17%
- PPDA
- 17.0
- Possession
- 53%
- Directness (yds/pass)
- 5.7
- Long balls/90
- 34
- Set-piece xG
- 11%
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
Argentina
- Giovani Lo CelsoAttacking midfieldNo natural backup0.30gap
- Lautaro MartínezStrikerCover: José Manuel López · 0.670.30gap
- Leandro ParedesDefensive midfieldNo natural backup0.26gap
Austria
- Konrad LaimerFull-backCover: Phillipp Mwene · 0.280.58gap
- Saša KalajdžićStrikerNo natural backup0.55gap
- Michael GregoritschStrikerNo natural backup0.50gap
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 level168 m
- Avg temperatureFive-year mean over the tournament window29.4 °C
- Avg humidity63%
- Heat stressShade WBGT ~30.8 °CHigh heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf stadium; a temporary natural-grass pitch on a sand root-zone is laid over the turf 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. Afternoon 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)
- Lionel MessiPKFW9.8%
- Lautaro MartínezFW5.0%
- Nicolás GonzálezFW3.8%
- Marcel SabitzerPKMF6.3%
- Marko ArnautovićFW2.4%
- Michael GregoritschFW2.4%
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
Argentina
vs Cape Verde · avg 6.6
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.
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.
6Lautaro MartínezContributed to the team's offensive shape with intelligent runs, though without direct goal involvement.
Contributed to the team's offensive shape with intelligent runs, though without direct goal involvement.
8Austrian Goalkeeper9'–716'Made numerous crucial saves, including an early penalty, preventing a much larger defeat for Austria.
11saves▼
Made numerous crucial saves, including an early penalty, preventing a much larger defeat for Austria.
Match timeline
Match observations
- The match highlights illustrate Argentina's attacking superiority, largely driven by Lionel Messi's influence. Austria adopted a defensive posture, frequently resorting to fouls to impede Argentina's offensive movements, especially against Messi.
- The game was characterized by Argentina's persistent pressure and Austria's resilient, albeit often physical, defending.
- The match began with a ceremonial display of national flags, setting a grand stage for the encounter.
▸Under the hood
Model-by-model comparison
Argentina vs Austria
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 75.0% | 22.0% | 2.9% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 61.1% | 25.7% | 13.2% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 57.9% | 26.0% | 16.1% |
| Bayesian stackingLearned-weight combination | — | 72.5% | 23.9% | 3.6% |
| Ensemble (published)Uniform average + isotonic calibration | — | 64.7% | 25.8% | 9.5% |
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(Argentina win)66.2%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Argentina win)66.2%
Decomposition of the published P(Argentina 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 |
|---|---|---|---|---|---|
| 22 Jun 2026 | FIFA World Cup | NArlington | 2–0 | W | — |
| 3 May 1990 | Friendly | AVienna | 1–1 | D | — |
| 21 May 1980 | Friendly | AVienna | 5–1 | W | — |
Argentina vs Austria, every senior international meeting in the martj42 results dataset (score from Argentina's perspective; H/A/N = home/away/neutral).
Latest news & match context
- Stage:
- Group J · Matchday 2
- Date:
- 22 Jun
Argentina
Argentina come in at close to full strength.
Austria
Austria come in at close to full strength.
Argentina and Austria 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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