ArgentinavsSwitzerland

Expected goals

Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.

Most likely scorelines

• 1–0
  16.2%
• 1–1
  12.4%
• 2–0
  12.4%
• 0–0
  12.2%
• 2–1
  8.6%

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

• 0–0
  34.4%
• 1–0
  24.4%
• 0–1
  11.2%
• 1–1
  9.2%
• 2–0
  9.2%

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 goals
  87.8%
• More than 1.5 goals
  64.4%
• More than 2.5 goals
  36.8%
• More than 3.5 goals
  17.4%
• More than 4.5 goals
  6.9%
• More than 5.5 goals
  2.3%
• Both teams score
  39.3%

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 zero50.0%
• Switzerland clean sheetOpposing team scores zero23.0%

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+
  3.7%
• Argentina by 3+
  11.5%
• Argentina by 2+
  28.7%
• Argentina by 1+
  55.1%
• Draw
  28.0%
• Switzerland by 1+
  16.9%
• Switzerland by 2+
  4.8%
• Switzerland by 3+
  1.0%
• Switzerland by 4+
  0.2%

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.

Game state through the match?

• Argentina ahead55.8%
• Level26.6%
• Switzerland ahead17.6%

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?

• 0–15
  30.3%
• 15–30
  21.1%
• 30–45
  14.7%
• 45–60
  10.3%
• 60–75
  7.1%
• 75–90
  5.0%
• No goal
  11.5%

Probability the match's first goal arrives in each 15-minute window. Homogeneous Poisson with combined rate λ = λ_h + λ_a from the Dixon-Coles fit; the seven rows (six windows + no-goal tail) sum to 1.

Half-time / full-time grid?

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 FT
  5.5%
• Switzerland trail at HT, avoid defeat at FT
  4.6%

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.

PK shootout simulator?

If the match ends level after extra time, the model estimates the shootout outcome from each team's Bayesian-smoothed conversion / save rate (Model #15). The bracket simulator uses the symmetric (averaged) ordering; the two what-if scenarios below show how the win probabilities shift when conditioning on which team kicks first.

Per-team posteriors: Argentina conv 77.0%, save 27.9%; Switzerland conv 71.4%, save 20.0%. Smoothed against the global prior with prior strength 20 — see /docs/methodology/.

Match detail?

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.

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 level229 m
• Avg temperatureFive-year mean over the tournament window25.8 °C
• Avg humidity69%
• Heat stressShade WBGT ~27.5 °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. Evening 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)

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/.

Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →

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