ArgentinavsJordan

Expected goals

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

Most likely scorelines

• 2–0
  16.8%
• 3–0
  15.4%
• 1–0
  11.9%
• 4–0
  10.6%
• 2–1
  6.1%

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

• 1–0
  28.6%
• 0–0
  21.4%
• 2–0
  19.9%
• 3–0
  9.1%
• 1–1
  5.6%

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
  95.3%
• More than 1.5 goals
  82.0%
• More than 2.5 goals
  60.3%
• More than 3.5 goals
  37.9%
• More than 4.5 goals
  20.5%
• More than 5.5 goals
  9.6%
• Both teams score
  29.0%

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 zero69.3%
• Jordan clean sheetOpposing team scores zero6.4%

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+
  24.6%
• Argentina by 3+
  44.2%
• Argentina by 2+
  67.4%
• Argentina by 1+
  86.5%
• Draw
  10.7%
• Jordan by 1+
  2.8%
• Jordan by 2+
  0.5%
• Jordan by 3+
  0.1%
• Jordan by 4+
  0.0%

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 ahead86.8%
• Level10.2%
• Jordan ahead3.0%

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
  40.5%
• 15–30
  24.1%
• 30–45
  14.3%
• 45–60
  8.5%
• 60–75
  5.1%
• 75–90
  3.0%
• No goal
  4.4%

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
  2.9%
• Jordan trail at HT, avoid defeat at FT
  1.6%

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.

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/.

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.

Tactical forecast

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/.

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. 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)

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

Probability decomposition (transparency surface)

• Baseline ensemble — P(Argentina win)79.3%
• + Lineup contribution
  0.0pp
• + Style-matchup contribution
  0.0pp
• Published P(Argentina win)79.3%