MexicovsEcuador

Expected goals

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

Most likely scorelines

• 0–0
  23.2%
• 0–1
  17.5%
• 1–0
  14.5%
• 1–1
  13.2%
• 0–2
  7.4%

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
  47.8%
• 0–1
  18.9%
• 1–0
  15.7%
• 1–1
  7.0%
• 0–2
  3.9%

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
  76.8%
• More than 1.5 goals
  44.7%
• More than 2.5 goals
  18.9%
• More than 3.5 goals
  6.5%
• More than 4.5 goals
  1.8%
• More than 5.5 goals
  0.4%
• Both teams score
  28.2%

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

• Mexico clean sheetOpposing team scores zero44.4%
• Ecuador clean sheetOpposing team scores zero50.6%

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?

• Mexico by 4+
  0.3%
• Mexico by 3+
  1.6%
• Mexico by 2+
  7.8%
• Mexico by 1+
  27.0%
• Draw
  38.3%
• Ecuador by 1+
  34.8%
• Ecuador by 2+
  11.7%
• Ecuador by 3+
  2.8%
• Ecuador by 4+
  0.5%

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?

• Mexico ahead27.8%
• Level36.7%
• Ecuador ahead35.5%

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?

• 0–15
  22.0%
• 15–30
  17.2%
• 30–45
  13.4%
• 45–60
  10.4%
• 60–75
  8.1%
• 75–90
  6.3%
• No goal
  22.5%

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?

• Mexico trail at HT, avoid defeat at FT
  4.3%
• Ecuador trail at HT, avoid defeat at FT
  4.4%

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: Mexico conv 72.5%, save 20.0%; Ecuador conv 72.0%, 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

High-altitude venue. Mexico City sits at 2,240 m above sea level — thinner air affects stamina and ball flight.

• AltitudeHigh altitude2,240 m
• Avg temperatureFive-year mean over the tournament window17.7 °C
• Avg humidity70%
• Heat stressShade WBGT ~19.5 °CLow heat stress
• Pitch surfacenatural grass

Natural-grass football stadium; a new pitch was laid during the stadium's renovation ahead of 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 →

Video analysis: player performance

Per-player ratings and event breakdowns from official highlights analysis. Tap a player to see their full match timeline.

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