Group A · Matchday 1
MexicovsSouth Africa
·13:00 local
Historial de enfrentamientos
| Date | Competition | Venue | Score | Result | xG |
|---|---|---|---|---|---|
| 11 Jun 2010 | FIFA World Cup | AJohannesburg | 1–1 | D | — |
| 8 Jul 2005 | Gold Cup | NCarson | 1–2 | L | — |
| 7 Jun 2000 | USA Cup | NDallas | 4–2 | W | — |
| 6 Oct 1993 | Friendly | NLos Angeles | 4–0 | W | — |
Mexico vs South Africa, every senior international meeting in the martj42 results dataset (score from Mexico's perspective; H/A/N = home/away/neutral).
Latest news & match context
- World Cup Daily: Finally, the biggest tournament ever is about to begin with Mexico vs. SA · ESPN · 11 Jun
- World Cup predictions: Mexico vs South Africa, South Korea vs Czech Republic on matchday 1 · The New York Times · 11 Jun
- Stage:
- Group A · Matchday 1
Mexico
Mexico: 2 carrying a fitness doubt.
- DoubtCésar Montes, a projected starter at defender, is recovering from Leg injury and is a fitness watch item; if unavailable the projected XI shifts.
- DoubtJulián Araujo (defender) is carrying Hamstring injury — a depth-level fitness watch item.
South Africa
South Africa come in at close to full strength.
Mexico faces defensive uncertainty with key defender César Montes doubtful due to a leg injury.
Fellow defender Julián Araujo is also a doubt with a hamstring issue.
South Africa, by contrast, reports a fully fit squad.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
El pronóstico
Analysis
The model rates Mexico as clear favourites at 59.5%, with South Africa at 13.4% and the draw at 27.2%. The Elo gap is substantial at 336 points in Mexico's favour, reflecting a clear strength differential in recent form. In Group A, Mexico are expected to advance (96.0%) while South Africa face a tighter path (22.9%) — this result could be decisive for South Africa's campaign.
Tactical matchup
Mexico (high press) meet South Africa (balanced) — contrasting tactical identities that should shape the contest. Mexico press significantly higher (PPDA 16.1) — their ability to force turnovers in advanced positions will be a key tactical dynamic.
Key battlegrounds
Mexico must sustain their pressing intensity to force turnovers in advanced positions. The individual battle features Raúl Jiménez (P(scores) 9.3%) against Evidence Makgopa (3.0%) — their impact could prove decisive.
Situational factors
South Africa face a 14,106km journey to the venue versus Mexico's 14km — acclimatisation and fatigue could factor into the second half. South Africa's Hugo Broos (5.4 years in post) brings significantly more tenure than Javier Aguirre (2.4 years) — squad cohesion and tactical familiarity may differ accordingly.
Match storyline
At 13.4%, a South Africa result would qualify as a genuine upset — the kind of group-stage surprise every World Cup produces.
Key numbers
Match-outcome probability
- Mexico win59.5%
- Draw27.2%
- South Africa win13.4%
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Goles y marcadores
Most likely scorelines
- 1–016.9%
- 0–014.5%
- 1–113.1%
- 2–011.0%
- 0–19.4%
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
- 0–037.7%
- 1–023.1%
- 0–112.9%
- 1–18.8%
- 2–07.4%
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 goals59.2%
- More than 2.5 goals31.6%
- More than 3.5 goals13.8%
- More than 4.5 goals5.0%
- More than 5.5 goals1.6%
- Both teams score37.1%
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 zero49.0%
- South Africa clean sheetOpposing team scores zero28.4%
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
- Mexico by 4+2.3%
- Mexico by 3+8.0%
- Mexico by 2+22.7%
- Mexico by 1+48.8%
- Draw30.7%
- South Africa by 1+20.5%
- South Africa by 2+6.0%
- South Africa by 3+1.3%
- South Africa 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.
Cómo se desarrolla el partido
Game state through the match
- Mexico ahead49.4%
- Level29.5%
- South Africa ahead21.1%
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
- 0–1528.0%
- 15–3020.2%
- 30–4514.5%
- 45–6010.4%
- 60–757.5%
- 75–905.4%
- No goal13.9%
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 Africa win |
|---|---|---|---|
| HMexico ahead | 30.9% | 4.0% | 0.8% |
| DLevel | 16.9% | 21.7% | 8.3% |
| ASouth Africa ahead | 1.5% | 4.0% | 11.9% |
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 FT5.5%
- South Africa trail at HT, avoid defeat at FT4.8%
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/.
Equipos y jugadores
Match detail
Mexico
Model-rated key players: Raúl Jiménez (FW) — P(scores) 9.3%; Santiago Giménez (FW) — P(scores) 4.6%; Hirving Lozano (FW) — P(scores) 3.1%.
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 Africa
Model-rated key players: Evidence Makgopa (FW) — P(scores) 3.0%; Oswin Appollis (FW) — P(scores) 3.0%; Thapelo Morena (FW) — P(scores) 3.0%.
South Africa under Hugo Broos play a balanced game with 52% possession. They apply moderate pressing intensity (PPDA 23.9). They favour high-quality chances (xG/shot 0.189, among the best in the field).
South Africa will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries.
Mexico historically converts 9.5% of xG from set-pieces, contributing 0.12 expected set-piece goals in this fixture. South Africa converts 10.9% from set-pieces (0.08 expected). Combined, the model expects 0.20 set-piece goals across the 90 minutes.
- P(Mexico scores set-piece goal) 11.3%
- P(South Africa scores set-piece goal) 7.5%
- P(set-piece goal in match) 18.0%
If a penalty is awarded to Mexico, the model gives 72.5% conversion, 71.4% for South Africa.
Mexico primary PK: Raúl Jiménez (1/1 in 2021-22, 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
- 16.1
- Possession
- 55%
- Directness (yds/pass)
- 6.7
- Long balls/90
- 37
- Set-piece xG
- 10%
- PPDA
- 23.9
- Possession
- 52%
- Directness (yds/pass)
- 6.8
- Long balls/90
- 44
- Set-piece xG
- 11%
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 Africa
- Nkosinathi SibisiCentre-backCover: Mbekezeli Mbokazi · 0.000.41gap
- Aubrey ModibaFull-backCover: Thabang Matuludi · 0.180.28gap
- Khuliso MudauFull-backCover: Thabang Matuludi · 0.180.24gap
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. 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)
- Raúl JiménezPKFW9.3%
- Santiago GiménezFW4.6%
- Hirving LozanoFW3.1%
- Evidence MakgopaFW3.0%
- Oswin AppollisFW3.0%
- Thapelo MorenaFW3.0%
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/.
Entre bastidores
Model-by-model comparison
Mexico vs South Africa
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 76.4% | 22.0% | 1.6% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 48.8% | 30.6% | 20.5% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 53.6% | 28.7% | 17.7% |
| Bayesian stackingLearned-weight combination | — | 57.6% | 34.5% | 7.8% |
| Ensemble (published)Uniform average + isotonic calibration | — | 59.5% | 27.2% | 13.4% |
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)59.5%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Mexico win)59.5%
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.
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