Group A · Matchday 1
MexicovsSouth Africa
2026-06-11·13:00 localPredictions finalised
The forecast
Match-outcome probability
- Mexico win59.5%
- Draw27.2%
- South Africa win13.4%
A 336-point Elo gap frames this as a significant mismatch, yet the model still gives South Africa a 13% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Mexico
- ·Mexico holds a significant Elo gap of 336 points over South Africa.
- ·Mexico is ranked 15th in FIFA rankings, substantially higher than South Africa at 61st.
- ·Mexico's expected goals (xG) for the match are 1.74, more than double South Africa's 0.71 xG.
- ·Mexico has a positive head-to-head record against South Africa, winning 2 and drawing 1 of their 4 encounters.
Favoring South Africa
- ·South Africa secured a 1-1 draw against Mexico in their most recent encounter at the 2010 FIFA World Cup.
- ·South Africa recorded a 2-1 victory over Mexico in the 2005 Gold Cup, demonstrating their capability to win against this opponent.
- ·The HP model assigns South Africa a 19.4% chance of victory, which is higher than the ensemble's 15.7% and significantly above the Elo model's 1.6%.
What the model can't fully price
- ·The model does not fully account for squad availability, with 2 players across both squads carrying a fitness doubt, including 1 projected starter.
- ·The impact of an elaborate opening ceremony and enthusiastic crowd, as noted in video analysis, is not quantifiable by the model and could influence match dynamics.
Form check
Mexico
SteadyMexico has shown strong recent form, securing three wins and two draws in their last five matches. They have maintained defensive solidity, keeping three clean sheets in this period, with their only recent loss being a narrow 1-2 defeat.
3 clean sheets in their last 5 matches
South Africa
DecliningSouth Africa's recent form has been inconsistent, with two wins, one draw, and three losses in their last six fixtures. They have struggled defensively, conceding in five of these matches, including two consecutive 2-1 defeats.
Lost 3 of their last 6 matches
Analysis
How it plays out
Mexico press high and force the tempo. South Africa's balanced setup needs to absorb that pressure early and find the right moments to play forward. Mexico's aggressive press (PPDA 16.1) against South Africa's deeper build-up (PPDA 23.9) creates a clear territory question: can Mexico force errors high up, or will South Africa 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. Raúl Jiménez's 8.8% scoring probability is the highest in this fixture. Containing that output is South Africa's primary defensive task.
Off the pitch
South Africa travel 14,106km while Mexico are essentially at home. That journey shows up in second-half intensity. Hugo Broos (5 years in charge of South Africa) vs Javier Aguirre (2 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
The model's 42nd-ranked side against the 19thth. Group stages reward the underdog who executes a clear plan.
▸Goals & scorelines
Likeliest score 1–0 (18.9%) · xG 1.3 - 0.6
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–018.9%
- 0–015.7%
- 2–012.8%
- 1–112.3%
- 0–18.2%
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
- 0–039.2%
- 1–024.8%
- 0–111.0%
- 2–08.2%
- 1–17.9%
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 goals84.3%
- More than 1.5 goals57.2%
- More than 2.5 goals29.5%
- More than 3.5 goals12.4%
- More than 4.5 goals4.4%
- More than 5.5 goals1.3%
- Both teams score33.2%
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 zero55.3%
- South Africa clean sheetOpposing team scores zero27.2%
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+2.8%
- Mexico by 3+9.5%
- Mexico by 2+25.9%
- Mexico by 1+53.3%
- Draw30.5%
- South Africa by 1+16.2%
- South Africa by 2+4.1%
- South Africa by 3+0.7%
- South Africa by 4+0.1%
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 29.5% · BTTS 33.2%
Game state through the match
- Mexico ahead54.0%
- Level29.1%
- South Africa ahead16.9%
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
- 0–1527.1%
- 15–3019.8%
- 30–4514.4%
- 45–6010.5%
- 60–757.7%
- 75–905.6%
- No goal15.0%
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 | 34.1% | 3.6% | 0.6% |
| DLevel | 18.4% | 22.1% | 7.0% |
| ASouth Africa ahead | 1.4% | 3.5% | 9.2% |
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 FT4.9%
- South Africa trail at HT, avoid defeat at FT4.2%
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: Jiménez (8.8%)
Match detail
Mexico
Model-rated key players: Raúl Jiménez (FW) — P(scores) 8.8%; Santiago Giménez (FW) — P(scores) 3.7%; 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 (0.07 expected). Combined, the model expects 0.19 set-piece goals across the 90 minutes.
- P(Mexico scores set-piece goal) 11.7%
- P(South Africa scores set-piece goal) 6.3%
- P(set-piece goal in match) 17.2%
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 JiménezPKFW8.8%
- Santiago GiménezFW3.7%
- 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/.
Recent match form
Last match player ratings
Mexico
vs Ecuador · avg 7.4
South Africa
vs Canada · avg 7.7
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.
9Julián Quiñones6'–8'Scored two crucial goals for Mexico with a close-range finish and a rebound, and was active in creating further opportunities.
2goals▼
Scored two crucial goals for Mexico with a close-range finish and a rebound, and was active in creating further opportunities.
Match timeline
9Raúl Jiménez36'–67'Scored two goals for Mexico, including a header and a composed finish, significantly extending his team's lead.
2goals▼
Scored two goals for Mexico, including a header and a composed finish, significantly extending his team's lead.
Match timeline
7Modiba40'–40'Registered South Africa's only notable shot on target with a long-range effort that required a save from the Mexico goalkeeper.
1shots1on target▼
Registered South Africa's only notable shot on target with a long-range effort that required a save from the Mexico goalkeeper.
Match timeline
Match observations
- The match between Mexico and South Africa was preceded by an elaborate opening ceremony, featuring national flags and mascots on the pitch.
- The stadium was filled with enthusiastic supporters, creating a vibrant atmosphere with confetti falling throughout the event.
- Mexico secured a 2-0 victory over South Africa, with goals registered in the 8th and 67th minutes.
▸Under the hood
Model-by-model comparison
Mexico vs South Africa
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 77.2% | 22.0% | 0.8% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 52.6% | 30.5% | 16.9% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 50.4% | 30.7% | 18.9% |
| Bayesian stackingLearned-weight combination | — | 62.6% | 33.2% | 4.1% |
| Ensemble (published)Uniform average + isotonic calibration | — | 56.9% | 30.5% | 12.6% |
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.
Head-to-head history
| Date | Competition | Venue | Score | Result | xG |
|---|---|---|---|---|---|
| 11 Jun 2026 | FIFA World Cup | HMexico City | 2–0 | W | — |
| 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
No recent headlines for Mexico or South Africa.
- Stage:
- Group A · Matchday 1
- Date:
- 11 Jun
Mexico
Mexico come in at close to full strength.
South Africa
South Africa come in at close to full strength.
Mexico and South Africa 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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