Group D · Matchday 2
United StatesvsAustralia
2026-06-19·12:00 localPredictions finalised
The forecast
Match-outcome probability
- United States win30.9%
- Draw28.1%
- Australia win41.0%
The model projects one of the most closely-contested fixtures of the round — United States and Australia are separated by fine margins across every outcome.
Why the model says this
Favoring United States
- ·United States holds a higher FIFA ranking of 14th, compared to Australia's 26th.
- ·In head-to-head encounters, United States has a positive historical record with 2 wins against Australia's 1 win in 4 matches.
- ·United States' recent form includes 3 wins and 1 draw in their last 6 matches, indicating a slightly better overall consistency than Australia's 3 wins and 3 losses.
Favoring Australia
- ·The ELO rating system favours Australia, with an ELO gap of 62 points over the United States.
- ·Australia's expected goals (xG) for this fixture are projected at 1.05, marginally higher than United States' 0.96 xG.
- ·The ELO model specifically assigns Australia a 47.8% probability of winning, which is significantly higher than the 30.2% for a United States victory from the same model.
- ·The overall ensemble model predicts Australia as the most likely winner, with a 38.9% chance of victory.
What the model can't fully price
- ·One projected starter across both squads is carrying a fitness doubt, a factor not adjusted for by the model's current lineup channel.
- ·United States has had 7 days of rest since their last match, one day more than Australia's 6 days, a differential not included in the model's inputs.
Form check
United States
DecliningThe United States has recorded 3 wins, 1 draw, and 2 losses in their last six matches. While they had a strong run of three wins and a draw, their most recent two fixtures ended in defeats (0-2, 2-5).
10 goals scored in last 6 matches
Australia
ImprovingAustralia's recent form shows 3 wins and 3 losses in their last six outings. After a period of three consecutive defeats, they have secured victories in their two most recent matches (5-1, 1-0).
7 goals scored in last 6 matches
Analysis
How it plays out
United States's balanced setup will need to hold shape against Australia's direct transition game. The risk for United States: getting caught between attacking and defending. United States's aggressive press (PPDA 27.7) against Australia's deeper build-up (PPDA 37.0) creates a clear territory question: can United States force errors high up, or will Australia play through the press and find space behind it?
What decides it
Australia will concede possession willingly and attack in transition. Their defensive block needs to hold without fouling in dangerous areas. Folarin Balogun's 11.7% scoring probability is the highest in this fixture. Containing that output is Australia's primary defensive task.
Off the pitch
Australia travel 13,065km, 5x United States's journey. Second-half fatigue is a real factor at that differential.
The angle
Likely the last World Cup for Tim Ream. Tournament experience at this level is hard to quantify but hard to replace.
▸Goals & scorelines
Likeliest score 0–0 (14.5%) · xG 1.0 - 1.0
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 0–014.5%
- 1–114.3%
- 0–113.1%
- 1–012.5%
- 0–27.1%
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
- 0–037.5%
- 0–118.3%
- 1–017.4%
- 1–19.7%
- 0–24.8%
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 goals85.5%
- More than 1.5 goals59.9%
- More than 2.5 goals32.0%
- More than 3.5 goals14.1%
- More than 4.5 goals5.2%
- More than 5.5 goals1.6%
- Both teams score40.5%
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
- United States clean sheetOpposing team scores zero36.2%
- Australia clean sheetOpposing team scores zero37.8%
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
- United States by 4+0.8%
- United States by 3+3.4%
- United States by 2+12.3%
- United States by 1+32.5%
- Draw32.6%
- Australia by 1+34.9%
- Australia by 2+13.7%
- Australia by 3+4.0%
- Australia by 4+0.9%
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 32.0% · BTTS 40.5%
Game state through the match
- United States ahead33.4%
- Level30.9%
- Australia ahead35.7%
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
- 0–1528.2%
- 15–3020.3%
- 30–4514.5%
- 45–6010.4%
- 60–757.5%
- 75–905.4%
- No goal13.7%
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 → | HUnited States win | DDraw | AAustralia win |
|---|---|---|---|
| HUnited States ahead | 19.6% | 4.4% | 1.3% |
| DLevel | 12.4% | 22.3% | 13.1% |
| AAustralia ahead | 1.2% | 4.5% | 21.2% |
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
- United States trail at HT, avoid defeat at FT5.7%
- Australia trail at HT, avoid defeat at FT5.7%
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: Balogun (11.7%)
Match detail
United States
Model-rated key players: Folarin Balogun (FW) — P(scores) 11.7%; Diego Luna (FW) — P(scores) 5.2%; Haji Wright (FW) — P(scores) 3.9%.
United States under Mauricio Pochettino play a balanced game with 50% possession. Their likely shape is a 4-3-3. They sit deeper and pick their moments to press (PPDA 27.7).
United States will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. Managing minutes for Tim Ream across what could be seven matches will test the coaching staff's rotation planning.
Australia
Model-rated key players: Brandon Borrello (FW) — P(scores) 2.9%; Mitch Duke (FW) — P(scores) 2.3%; Martin Boyle (FW) — P(scores) 2.1%.
Australia under Tony Popovic play a transition heavy game, with just 44% possession — among the lowest in the field. Their likely shape is a 4-4-2, though they have also used 4-2-3-1 and 4-3-3. They sit deeper and pick their moments to press (PPDA 37.0). They are selective in their shooting (8.0 per 90).
Australia rely on defensive discipline and quick transitions — absorbing pressure and converting turnovers into attacking chances. Concentration and defensive organisation for full 90-minute stretches will determine whether the approach holds against top opposition.
United States historically converts 5.2% of xG from set-pieces, contributing 0.05 expected set-piece goals in this fixture. Combined, the model expects 0.05 set-piece goals across the 90 minutes.
- P(United States scores set-piece goal) 4.9%
- P(set-piece goal in match) 4.9%
United States: Timothy Tillman on corners (42 corners), Gianluca Busio on free kicks (per fbref 2021 22) · Australia: Ajdin Hrustić on free kicks (per fbref 2022 23)
If a penalty is awarded to United States, the model gives 71.4% conversion, 71.4% for Australia.
United States primary PK: Folarin Balogun (2/2 in 2022-23, 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
- 27.7
- Possession
- 50%
- Directness (yds/pass)
- 6.4
- Long balls/90
- 38
- Set-piece xG
- 5%
- PPDA
- 37.0
- Possession
- 44%
- Directness (yds/pass)
- 7.2
- Long balls/90
- 46
- 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
United States
- Christian PulisicWingerCover: Alejandro Zendejas · 0.570.27gap
- Tyler AdamsDefensive midfieldNo natural backup0.26gap
- Antonee RobinsonFull-backCover: Joe Scally · 0.770.22gap
Australia
- Mathew RyanGoalkeeperCover: Paul Izzo · 0.330.56gap
- Nestory IrankundaWingerCover: Nishan Velupillay · 0.090.36gap
- Connor MetcalfeCentral midfieldCover: Patrick Yazbek · 0.420.33gap
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 level16 m
- Avg temperatureFive-year mean over the tournament window18.0 °C
- Avg humidity68%
- Heat stressShade WBGT ~19.6 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Artificial-turf NFL stadium laying a temporary natural-grass 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. 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)
- Folarin BalogunPKFW11.7%
- Diego LunaFW5.2%
- Haji WrightFW3.9%
- Brandon BorrelloFW2.9%
- Mitch DukeFW2.3%
- Martin BoyleFW2.1%
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
United States
vs Bosnia and Herzegovina · avg 5.5
Worked well: Their offensive movement and ability to create chances, particularly from wide areas and set pieces, proved effective. They maintained their attacking threat even after a player was dismissed.
Struggled: The team struggled with offside calls, indicating issues with timing runs. A red card also highlighted a lapse in discipline.
Australia
vs Egypt · avg 6.2
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.
9Matt Turner128'–146'Made multiple crucial saves, especially in the second half, to secure the clean sheet and the team's victory.
3saves▼
Made multiple crucial saves, especially in the second half, to secure the clean sheet and the team's victory.
Match timeline
8Dest17'–52'Scored a crucial goal that doubled the USA's lead and contributed to offensive pressure.
1goals1shots▼
Scored a crucial goal that doubled the USA's lead and contributed to offensive pressure.
Match timeline
7Balogun119'–119'Created a good scoring opportunity through a strong run and shot, demonstrating his direct attacking style.
1shots▼
Created a good scoring opportunity through a strong run and shot, demonstrating his direct attacking style.
Match timeline
7Leckie44'–44'Provided a quality cross that contributed to Australia's attacking build-up from a wide position.
▼
Provided a quality cross that contributed to Australia's attacking build-up from a wide position.
Match timeline
6Luque38'–38'Showed offensive ambition by attempting a shot, but it was blocked and didn't lead to a significant chance.
1shots▼
Showed offensive ambition by attempting a shot, but it was blocked and didn't lead to a significant chance.
Match timeline
6Metcalfe135'–135'Contributed to Australia's attacking build-up by delivering a cross into the box.
▼
Contributed to Australia's attacking build-up by delivering a cross into the box.
Match timeline
3Cameron Burgess23'–23'His own goal directly put his team at a disadvantage early in the match.
1goals▼
His own goal directly put his team at a disadvantage early in the match.
Match timeline
Match observations
- The match saw the United States secure a 2-0 victory over Australia in a competitive encounter.
- The USA took an early lead through an unfortunate own goal, which was then extended by a second goal confirmed after a VAR review.
- Australia created numerous attacking opportunities, particularly in the second half, but were unable to convert them into goals.
▸Under the hood
Model-by-model comparison
United States vs Australia
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 34.5% | 22.0% | 43.5% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 31.5% | 32.1% | 36.4% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 32.8% | 30.9% | 36.3% |
| Bayesian stackingLearned-weight combination | — | 30.2% | 33.4% | 36.4% |
| Ensemble (published)Uniform average + isotonic calibration | — | 32.5% | 29.5% | 38.0% |
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(United States win)30.9%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(United States win)30.9%
Decomposition of the published P(United States 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 |
|---|---|---|---|---|---|
| 19 Jun 2026 | FIFA World Cup | HSeattle | 2–0 | W | — |
| 14 Oct 2025 | Friendly | HDenver | 2–1 | W | — |
| 5 Jun 2010 | Friendly | NRoodepoort | 3–1 | W | — |
| 6 Nov 1998 | Friendly | HSan Jose | 0–0 | D | — |
| 13 Jun 1992 | Friendly | HOrlando | 0–1 | L | — |
United States vs Australia, every senior international meeting in the martj42 results dataset (score from United States's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for United States or Australia.
- Stage:
- Group D · Matchday 2
- Date:
- 19 Jun
Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.
- 1.Squad availability: 1 carrying a fitness doubt across the two squads, 1 of them projected starters. The forecast does not adjust for who is missing: its lineup channel currently contributes zero, so this is context the probabilities do not include.
- 2.Rest differential: United States have had 7 days since their previous match versus 6 for Australia. Rest and recovery are not model inputs.
United States
United States: 1 carrying a fitness doubt.
- DoubtChristian Pulisic, the first-choice forward, is recovering from Calf injury and is a fitness watch item; if unavailable the projected XI shifts.
Australia
Australia come in at close to full strength.
Availability runs in Australia's favour here: United States are managing a fitness concern over Christian Pulisic, while Australia's projected XI looks intact.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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