Group A · Matchday 2
South AfricavsCzech Republic
2026-06-18·12:00 localPredictions finalised
The forecast
Match-outcome probability
- South Africa win21.2%
- Draw27.2%
- Czech Republic win51.7%
A 202-point Elo gap frames this as a significant mismatch, yet the model still gives South Africa a 19% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring South Africa
- ·The only previous head-to-head fixture between these two nations resulted in a 2-2 draw in 1997, meaning South Africa have historically avoided defeat against Czech Republic.
- ·South Africa holds a FIFA ranking of 61st.
Favoring Czech Republic
- ·Czech Republic is favoured by a significant 202 Elo points over South Africa, indicating a substantial difference in underlying team strength.
- ·Czech Republic's expected goals (xG) for this fixture are 1.71, nearly double South Africa's 0.86 xG, suggesting a greater offensive threat.
- ·Czech Republic's recent form shows 2 wins, 3 draws, and 1 loss in their last six matches, a stronger record compared to South Africa's 2 wins, 1 draw, and 3 losses over the same period.
- ·Czech Republic's 'Counter-attacking' style includes a very direct attacking approach, ranking in the 91.2 percentile for directness, which could exploit defensive transitions.
What the model can't fully price
- ·Three players across both squads are carrying fitness doubts, which the model's current lineup channel does not account for in its probabilities.
- ·The model does not explicitly factor in the potential motivational boost from a vibrant home crowd, as noted by the 'vibrant and enthusiastic' atmosphere among South African fans in previous matches.
Form check
South Africa
DecliningSouth Africa's recent form shows inconsistency, with 2 wins, 1 draw, and 3 losses in their last six matches. They have scored 8 goals and conceded 9 in this period, indicating a tendency for close contests but also defensive vulnerabilities.
8 goals scored, 9 conceded in last 6 matches
Czech Republic
SteadyCzech Republic enters this match in more stable form, with 2 wins, 3 draws, and only 1 loss in their last six fixtures. They have been more potent in attack, scoring 12 goals, and more solid defensively, conceding only 6 goals over the same period.
12 goals scored, 6 conceded in last 6 matches
Analysis
How it plays out
South Africa's balanced setup will need to hold shape against Czech Republic's direct transition game. The risk for South Africa: getting caught between attacking and defending. Czech Republic's aggressive press (PPDA 20.4) against South Africa's deeper build-up (PPDA 23.9) creates a clear territory question: can Czech Republic force errors high up, or will South Africa play through the press and find space behind it?
What decides it
Czech Republic 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: Evidence Makgopa (3.7%) and Patrik Schick (6.2%).
Off the pitch
Hugo Broos (5 years in charge of South Africa) vs Miroslav Koubek (1 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
A Group A fixture where the result matters more for the standings than the headlines.
▸Goals & scorelines
Likeliest score 0–1 (15.6%) · xG 0.8 - 1.3
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 0–115.6%
- 0–013.7%
- 1–113.5%
- 0–210.4%
- 1–09.3%
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
- 0–036.5%
- 0–122.2%
- 1–013.5%
- 1–19.4%
- 0–27.2%
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 goals86.3%
- More than 1.5 goals61.4%
- More than 2.5 goals33.5%
- More than 3.5 goals15.1%
- More than 4.5 goals5.7%
- More than 5.5 goals1.8%
- Both teams score39.6%
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
- South Africa clean sheetOpposing team scores zero28.2%
- Czech Republic clean sheetOpposing team scores zero45.9%
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
- South Africa by 4+0.3%
- South Africa by 3+1.6%
- South Africa by 2+7.0%
- South Africa by 1+22.1%
- Draw30.8%
- Czech Republic by 1+47.1%
- Czech Republic by 2+22.0%
- Czech Republic by 3+7.8%
- Czech Republic by 4+2.2%
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.
▸How the match unfolds
Over 2.5 goals 33.5% · BTTS 39.6%
Game state through the match
- South Africa ahead22.9%
- Level29.2%
- Czech Republic ahead47.9%
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
- 0–1528.9%
- 15–3020.5%
- 30–4514.6%
- 45–6010.4%
- 60–757.4%
- 75–905.3%
- No goal12.9%
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 → | HSouth Africa win | DDraw | ACzech Republic win |
|---|---|---|---|
| HSouth Africa ahead | 13.0% | 4.2% | 1.5% |
| DLevel | 8.9% | 21.1% | 16.5% |
| ACzech Republic ahead | 0.9% | 4.2% | 29.8% |
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
- South Africa trail at HT, avoid defeat at FT5.1%
- Czech Republic trail at HT, avoid defeat at FT5.7%
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: Schick (6.2%)
Match detail
South Africa
Model-rated key players: Evidence Makgopa (FW) — P(scores) 3.7%; Oswin Appollis (FW) — P(scores) 3.7%; Thapelo Morena (FW) — P(scores) 3.7%.
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.
Czech Republic
Model-rated key players: Patrik Schick (FW) — P(scores) 6.2%; Vladimír Darida (MF) — P(scores) 3.2%; Tomáš Chorý (FW) — P(scores) 3.2%.
Czech Republic under Miroslav Koubek play a counter attacker game, with just 45% possession — among the lowest in the field. They apply moderate pressing intensity (PPDA 20.4).
Czech Republic 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.
South Africa historically converts 10.9% of xG from set-pieces, contributing 0.09 expected set-piece goals in this fixture. Czech Republic converts 6.5% from set-pieces (0.08 expected). Combined, the model expects 0.17 set-piece goals across the 90 minutes.
- P(South Africa scores set-piece goal) 8.2%
- P(Czech Republic scores set-piece goal) 7.9%
- P(set-piece goal in match) 15.4%
Czech Republic: Adam Hložek on corners (9 corners), Tomáš Souček on free kicks (per fbref 2022 23)
If a penalty is awarded to South Africa, the model gives 71.4% conversion, 73.3% for Czech Republic.
Czech Republic primary PK: Vladimír Darida (5/6 in 2014-15, 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
- 23.9
- Possession
- 52%
- Directness (yds/pass)
- 6.8
- Long balls/90
- 44
- Set-piece xG
- 11%
- PPDA
- 20.4
- Possession
- 45%
- Directness (yds/pass)
- 7.8
- Long balls/90
- 41
- Set-piece xG
- 6%
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
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
Czech Republic
- Lukáš ProvodWingerNo natural backup0.38gap
- Patrik SchickStrikerCover: Mojmír Chytil · 0.600.30gap
- Matěj KovářGoalkeeperCover: Jindřich Staněk · 0.650.19gap
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 level320 m
- Avg temperatureFive-year mean over the tournament window25.7 °C
- Avg humidity73%
- Heat stressShade WBGT ~27.9 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf stadium converting to a temporary natural-grass pitch 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. 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)
- Evidence MakgopaFW3.7%
- Oswin AppollisFW3.7%
- Thapelo MorenaFW3.7%
- Patrik SchickFW6.2%
- Vladimír DaridaPKMF3.2%
- Tomáš ChorýFW3.2%
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
South Africa
vs Canada · avg 7.7
Czech Republic
vs Mexico · avg 5.3
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.
9Itumeleng KhuneMade multiple crucial saves throughout the match, keeping his team in the game and preserving the lead.
Made multiple crucial saves throughout the match, keeping his team in the game and preserving the lead.
8Lyle Foster32'–32'Scored the crucial opening goal with clinical finishing, giving his team the lead.
1goals▼
Scored the crucial opening goal with clinical finishing, giving his team the lead.
Match timeline
7Patrik Schick2'–2'Showed good aerial presence and was involved in several attacking movements, despite being unable to convert his chances.
1shots1on target▼
Showed good aerial presence and was involved in several attacking movements, despite being unable to convert his chances.
Match timeline
6Lukáš SadílekEngaged in midfield duels and attempted to distribute the ball to initiate attacks.
Engaged in midfield duels and attempted to distribute the ball to initiate attacks.
Match observations
- The atmosphere among South African fans was vibrant and enthusiastic, with supporters singing, dancing, and waving flags. Despite the 1-1 draw against Czechia, a strong sense of national pride and optimism was evident. Fans expressed excitement about the experience and hope for improved future performances. The collective celebration highlighted the joy of supporting their team, regardless of the outcome.
- The match began with an electrifying atmosphere at the Atlanta Stadium, marking day one of the FIFA World Cup 2026.
- Both teams started cautiously, feeling each other out, but the Czech Republic showed early intent with several attacking forays.
▸Under the hood
Model-by-model comparison
South Africa vs Czech Republic
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 20.3% | 22.0% | 57.8% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 22.6% | 30.5% | 46.9% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 22.9% | 29.7% | 47.4% |
| Bayesian stackingLearned-weight combination | — | 18.0% | 30.9% | 51.1% |
| Ensemble (published)Uniform average + isotonic calibration | — | 18.6% | 28.5% | 52.9% |
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(South Africa win)21.2%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(South Africa win)21.2%
Decomposition of the published P(South Africa 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 | NAtlanta | 1–1 | D | — |
| 13 Dec 1997 | Confederations Cup | NRiyadh | 2–2 | D | — |
South Africa vs Czech Republic, every senior international meeting in the martj42 results dataset (score from South Africa's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for South Africa or Czech Republic.
- Stage:
- Group A · Matchday 2
- Date:
- 18 Jun
South Africa
South Africa come in at close to full strength.
Czech Republic
Czech Republic come in at close to full strength.
South Africa and Czech Republic 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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