Group A · Matchday 2
MexicovsSouth Korea
2026-06-18·19:00 localPredictions finalised
The forecast
Match-outcome probability
- Mexico win43.9%
- Draw27.9%
- South Korea win28.2%
A clash of identities: Mexico's high-press approach meets South Korea's counter-attacker style in a fixture the model gives to Mexico at 47%.
Why the model says this
Favoring Mexico
- ·Mexico holds a significantly higher FIFA rank at 15th globally, compared to South Korea's 22nd position.
- ·Historically, Mexico has dominated the head-to-head record, winning 8 of the 14 encounters, with South Korea winning 4 and 2 draws.
- ·The model's expected goals (xG) project Mexico to create more scoring opportunities, with 1.2 xG compared to South Korea's 0.95 xG.
- ·Mexico is favoured by the Elo rating system with a delta of 108 points, indicating a stronger underlying team strength.
Favoring South Korea
- ·South Korea secured a 2-2 draw in their most recent head-to-head fixture against Mexico in September 2025.
- ·Despite being the underdog, the ensemble model still assigns South Korea a 29.0% probability of winning, indicating a competitive fixture.
- ·The HP model gives South Korea its highest individual model probability of 30.6% for a win, suggesting some underlying factors favouring them.
What the model can't fully price
- ·Four players across both squads are carrying fitness doubts, with two of them projected starters. The model's lineup channel currently contributes zero, meaning these potential absences are not factored into the probabilities.
Form check
Mexico
SteadyMexico enters this match with a solid run of form, recording three wins and two draws in their last six fixtures. Their recent defensive performances have been strong, securing three clean sheets in their last five matches.
3 clean sheets in last 5 matches
South Korea
DecliningSouth Korea's recent form has been inconsistent, marked by three wins and three losses in their last six matches. They have suffered two consecutive defeats leading into this fixture, conceding five goals in those two games.
2 consecutive losses conceding 5 goals
Analysis
How it plays out
Mexico's high press game meets South Korea's counter attacker shape. South Korea will concede territory deliberately and look to hit the spaces Mexico's high line leaves behind. Mexico's aggressive press (PPDA 16.1) against South Korea's deeper build-up (PPDA 25.0) creates a clear territory question: can Mexico force errors high up, or will South Korea play through the press and find space behind it?
What decides it
Mexico press high (PPDA 16.1). If the press doesn't win the ball early, the space behind their back line becomes exposed. South Korea will concede possession willingly and attack in transition. Their defensive block needs to hold without fouling in dangerous areas. The scoring threat is evenly split: Raúl Jiménez (9.0%) and Son Heung-min (11.3%).
Off the pitch
South Korea travel 11,732km, 25x Mexico's journey. Second-half fatigue is a real factor at that differential.
The angle
Likely the last World Cup for Kim Seung-gyu. Tournament experience at this level is hard to quantify but hard to replace.
▸Goals & scorelines
Likeliest score 1–0 (14.4%) · xG 1.2 - 0.9
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–014.4%
- 1–113.9%
- 0–013.2%
- 0–19.9%
- 2–09.3%
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
- 0–035.8%
- 1–021.0%
- 0–114.6%
- 1–19.9%
- 2–06.6%
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 goals86.8%
- More than 1.5 goals62.6%
- More than 2.5 goals34.7%
- More than 3.5 goals15.9%
- More than 4.5 goals6.1%
- More than 5.5 goals2.0%
- Both teams score41.6%
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 zero42.3%
- South Korea clean sheetOpposing team scores zero29.3%
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+1.9%
- Mexico by 3+6.8%
- Mexico by 2+19.9%
- Mexico by 1+43.9%
- Draw31.0%
- South Korea by 1+25.1%
- South Korea by 2+8.6%
- South Korea by 3+2.1%
- South Korea by 4+0.4%
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 34.7% · BTTS 41.6%
Game state through the match
- Mexico ahead44.7%
- Level29.4%
- South Korea ahead25.9%
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
- 0–1529.4%
- 15–3020.8%
- 30–4514.6%
- 45–6010.3%
- 60–757.3%
- 75–905.2%
- No goal12.4%
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 → | HMexico win | DDraw | ASouth Korea win |
|---|---|---|---|
| HMexico ahead | 27.5% | 4.4% | 1.0% |
| DLevel | 15.5% | 20.9% | 9.9% |
| ASouth Korea ahead | 1.6% | 4.4% | 14.9% |
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 FT5.9%
- South Korea trail at HT, avoid defeat at FT5.4%
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.
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: Heung-min (11.3%)
Match detail
Mexico
Model-rated key players: Raúl Jiménez (FW) — P(scores) 9.0%; Santiago Giménez (FW) — P(scores) 3.9%; Hirving Lozano (FW) — P(scores) 3.3%.
Mexico under Javier Aguirre play a high press game, holding 55% of the ball — among the highest in the tournament field. Their likely shape is a 3-5-2, though they have also used 4-2-3-1 and 4-3-3. They press intensely (PPDA 16.1, top quartile (5th of 40)). They generate a high volume of shots (15.0 per 90).
Mexico 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.
South Korea
Model-rated key players: Son Heung-min (FW) — P(scores) 11.3%; Oh Hyeon-gyu (FW) — P(scores) 2.3%; Joo Min-kyu (FW) — P(scores) 1.9%.
South Korea under Hong Myung-bo play a counter attacker game, with just 44% possession — among the lowest in the field. Their likely shape is a 4-2-3-1, though they have also used 4-3-3 and 4-4-2. They apply moderate pressing intensity (PPDA 25.0).
South Korea 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. Managing minutes for Kim Seung-gyu across what could be seven matches will test the coaching staff's rotation planning.
Mexico historically converts 9.5% of xG from set-pieces, contributing 0.12 expected set-piece goals in this fixture. South Korea converts 12.6% from set-pieces (0.11 expected). Combined, the model expects 0.23 set-piece goals across the 90 minutes.
- P(Mexico scores set-piece goal) 11.0%
- P(South Korea scores set-piece goal) 10.3%
- P(set-piece goal in match) 20.2%
South Korea: Son Heung-min on corners (43 corners) (per fbref 2022 23)
If a penalty is awarded to Mexico, the model gives 72.5% conversion, 72.5% for South Korea.
Mexico primary PK: Raúl Jiménez (1/1 in 2021-22, per fbref 2021 22) · South Korea primary PK: Son Heung-min (1/1 in 2020-21, per fbref 2022 23).
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
- 16.1
- Possession
- 55%
- Directness (yds/pass)
- 6.7
- Long balls/90
- 37
- Set-piece xG
- 10%
- PPDA
- 25.0
- Possession
- 44%
- Directness (yds/pass)
- 7.1
- Long balls/90
- 40
- Set-piece xG
- 13%
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
Mexico
- Johan VásquezCentre-backCover: Jesús Alberto Angulo · 0.690.22gap
- Edson ÁlvarezDefensive midfieldCover: Luis Chávez · 0.700.19gap
- Orbelín PinedaCentral midfieldCover: Érick Sánchez · 0.670.00gap
South Korea
- Hwang In-beomDefensive midfieldCover: Park Jin-seob · 0.280.46gap
- Lee Kang-inAttacking midfieldCover: Lee Jae-sung · 0.410.46gap
- Cho Gue-sungStrikerNo natural backup0.31gap
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. Guadalajara sits at 1,565 m above sea level — thinner air affects stamina and ball flight.
- AltitudeHigh altitude1,565 m
- Avg temperatureFive-year mean over the tournament window20.2 °C
- Avg humidity76%
- Heat stressShade WBGT ~22.4 °CLow heat stress
- Pitch surfacenatural grass
Natural-grass football stadium.
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)
- Raúl JiménezPKFW9.0%
- Santiago GiménezFW3.9%
- Hirving LozanoFW3.3%
- Son Heung-minPKFW11.3%
- Oh Hyeon-gyuFW2.3%
- Joo Min-kyuFW1.9%
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
Mexico
vs Ecuador · avg 7.4
South Korea
vs South Africa · avg 5.8
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.
9Santiago GiménezScored both of Mexico's goals with clinical finishing and excellent movement in the penalty area.
Scored both of Mexico's goals with clinical finishing and excellent movement in the penalty area.
7Érick SánchezContributed significantly to Mexico's early control of the game through active participation in possession and dribbling.
Contributed significantly to Mexico's early control of the game through active participation in possession and dribbling.
Match observations
- The match, a group stage encounter in the FIFA World Cup, saw Mexico and South Korea engage in an exciting contest.
- Mexico established an early lead through a well-constructed attacking move, but South Korea swiftly responded with an equaliser, highlighting their quick passing and offensive capabilities.
- The game featured end-to-end action, with both teams displaying a strong desire to advance the ball and create scoring opportunities.
▸Under the hood
Model-by-model comparison
Mexico vs South Korea
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 62.3% | 22.0% | 15.8% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 43.0% | 30.8% | 26.2% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 41.4% | 30.7% | 27.9% |
| Bayesian stackingLearned-weight combination | — | 49.5% | 32.9% | 17.7% |
| Ensemble (published)Uniform average + isotonic calibration | — | 46.6% | 28.7% | 24.7% |
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(Mexico win)43.9%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Mexico win)43.9%
Decomposition of the published P(Mexico 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 |
|---|---|---|---|---|---|
| 18 Jun 2026 | FIFA World Cup | HZapopan | 1–0 | W | — |
| 9 Sep 2025 | Friendly | NNashville | 2–2 | D | — |
| 14 Nov 2020 | Friendly | NWiener Neustadt | 3–2 | W | — |
| 23 Jun 2018 | FIFA World Cup | NRostov-on-Don | 2–1 | W | 1.8–0.8 |
| 29 Jan 2014 | Friendly | NSan Antonio | 4–0 | W | — |
| 15 Feb 2006 | Friendly | NLos Angeles | 0–1 | L | — |
Mexico vs South Korea, every senior international meeting in the martj42 results dataset (score from Mexico's perspective; H/A/N = home/away/neutral; xG where the upstream dataset covers the match). See all 15 meetings →
Latest news & match context
No recent headlines for Mexico or South Korea.
- Stage:
- Group A · Matchday 2
- Date:
- 18 Jun
Mexico
Mexico come in at close to full strength.
South Korea
South Korea come in at close to full strength.
Mexico and South Korea 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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