away
South Africa Mexico
10% win 21.2% draw 68.8% loss
Most likely 0–2 (14.5%) λ 0.62–1.98 Over 2.5 48% · BTTS 41%
🇿🇦
South Africa
0.0% Champion probability ±0.00 MC-SE
- Coach
- Hugo Broos foreign · Belgian
- Elo (model)
- 1,518 world 80th
- Squad value
- €52M
- Power → Reality
- 38th 39th +0.00 pp · neutral draw
§ 01
The forecast
How far South Africa goes — the survival probability at each stage of the bracket, from the tournament Monte-Carlo simulation. Each bar carries its ±1.96·MC-SE interval.
Fig. D1 Fixture-aware · 100k sims
South Africa — stage progression
South Africa is most likely eliminated before the knockout rounds: 35% to clear the group. Champion probability 0.01%.
§ 02
The group & the path
Group A advancement odds, the bracket half South Africa sits in, and the earliest round they could meet each leading side.
| Group A | Confed | Advance (top 2) | Reach R32 | |
|---|---|---|---|---|
| 1 | 🇲🇽Mexico | CONCACAF | 54.5% | 92.7% |
| 2 | 🇨🇿Czechia | UEFA | 35.6% | 73.4% |
| 3 | 🇰🇷Korea Republic | AFC | 31.8% | 68.4% |
| 4 | 🇿🇦South Africa | CAF | 10.1% | 34.7% |
Source · Oxford Football Forecasting model
Bracket position Half 1 · Quadrant 2
Earliest possible meetings
No collision rows recorded for this team.
Collision = the earliest round the bracket wiring could pit South Africa against that side. Full bracket & collision matrix →
§ 03
Match by match
South Africa's three group fixtures, each with the predicted win / draw / loss split and the single most-likely scoreline. Probabilities are this team's own orientation; they sum to 100%.
away
South Africa Czechia
19.7% win 29.2% draw 51.1% loss
Most likely 0–1 (14.7%) λ 0.78–1.41 Over 2.5 38% · BTTS 42%
home
South Africa Korea Republic
20.3% win 30.9% draw 48.8% loss
Most likely 0–1 (15.8%) λ 0.75–1.30 Over 2.5 34% · BTTS 39%
How a match forecast is built
Each pairing is scored by the ensemble (Dixon-Coles bivariate-Poisson, the Bayesian hierarchical model and the global LightGBM-Poisson, log-pooled), producing an 11×11 scoreline grid that is marginalised into win/draw/loss, expected goals (λ), over/under 2.5 and both-teams-to-score. These are the same distributions the tournament simulator consumes, oriented here to South Africa. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where South Africa stands against the median of the 48-team field, metric by metric. The dot is South Africa; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
South Africa vs the field
Read each row as a multiple of the field median: dots to the right of the dashed line are above-field, to the left below. Raw values are labelled on the right so the comparison is transparent.
§ 05
History vs squad
The decoupling residual g — whether the squad’s market value sits above, or below, what the team’s record predicts.
Fig. D3 Bayesian projection residual g
South Africa on the decoupling axis
g = −0.50 ± 0.08: the record outruns the squad price — the team has achieved more than its comparatively modest squad value would predict.
What g means — and its limits
g is the residual from regressing a team’s current squad market value on its
history-based strength in the Bayesian hierarchical model. Positive g
means the squad is valued above what the team’s record predicts; negative means the
record outruns the squad’s price (the side achieves more than its market value implies).
Regressed on out-of-sample success the slope is positive — squad-rich sides go a touch
further — but not statistically significant at n = 3 tournaments, so treat
a single team’s g as a descriptive read, not a hard prediction.
The full decoupling essay →
§ 06
The squad
All 26 selected players — club, league (with relative strength), club-season minutes and goals, caps. Sort any column.
26players
25.7mean age
12mean caps
4%in a top-5 league
12distinct clubs
8largest club bloc
| # | Player | Pos | Club | League | Club min | Gls | Caps | NT gls |
|---|---|---|---|---|---|---|---|---|
| 1 | Ronwen Williams (captain) | GK | Mamelodi Sundowns | Premier Soccer League −1.35z | 4,599 | 0 | 62 | 0 |
| 2 | Thabang Matuludi | DF | Polokwane City | Premier Soccer League −1.35z | 1,950 | 2 | 2 | 0 |
| 3 | Khulumani Ndamane | DF | Mamelodi Sundowns | Premier Soccer League −1.35z | 1,608 | 0 | 5 | 0 |
| 4 | Teboho Mokoena | MF | Mamelodi Sundowns | Premier Soccer League −1.35z | 3,239 | 6 | 51 | 9 |
| 5 | Thalente Mbatha | MF | Orlando Pirates | Premier Soccer League −1.35z | 1,607 | 0 | 14 | 3 |
| 6 | Aubrey Modiba | DF | Mamelodi Sundowns | Premier Soccer League −1.35z | 3,067 | 1 | 44 | 3 |
| 7 | Oswin Appollis | FW | Orlando Pirates | Premier Soccer League −1.35z | 2,933 | 13 | 25 | 8 |
| 8 | Tshepang Moremi | FW | Orlando Pirates | Premier Soccer League −1.35z | 1,969 | 6 | 9 | 1 |
| 9 | Lyle Foster | FW | Burnley | Premier League +2.21z | 1,462 | 3 | 26 | 10 |
| 10 | Relebohile Mofokeng | FW | Orlando Pirates | Premier Soccer League −1.35z | 2,222 | 10 | 12 | 0 |
| 11 | Themba Zwane | MF | Mamelodi Sundowns | Premier Soccer League −1.35z | 572 | 0 | 53 | 12 |
| 12 | Thapelo Maseko | FW | AEL Limassol | 1. Division −0.31z | 401 | 0 | 9 | 1 |
| 13 | Sphephelo Sithole | MF | Tondela | Primeira Liga +1.14z | 1,560 | 1 | 27 | 1 |
| 14 | Mbekezeli Mbokazi | DF | Chicago Fire FC | — | — no club data | — | 10 | 1 |
| 15 | Iqraam Rayners | FW | Mamelodi Sundowns | Premier Soccer League −1.35z | 3,057 | 16 | 13 | 4 |
| 16 | Sipho Chaine | GK | Orlando Pirates | Premier Soccer League −1.35z | 2,790 | 0 | 3 | 0 |
| 17 | Evidence Makgopa | FW | Orlando Pirates | Premier Soccer League −1.35z | 1,402 | 8 | 26 | 6 |
| 18 | Samukele Kabini | DF | Molde | Eliteserien −0.13z | 1,639 | 1 | 5 | 0 |
| 19 | Nkosinathi Sibisi | DF | Orlando Pirates | Premier Soccer League −1.35z | 2,395 | 0 | 19 | 0 |
| 20 | Khuliso Mudau | DF | Mamelodi Sundowns | Premier Soccer League −1.35z | 3,161 | 1 | 32 | 1 |
| 21 | Ime Okon | DF | Hannover 96 | 2. Bundesliga +1.84z | 1,806 | 2 | 7 | 1 |
| 22 | Ricardo Goss | GK | Siwelele | Premier Soccer League −1.35z | 2,610 | 0 | 4 | 0 |
| 23 | Jayden Adams | MF | Mamelodi Sundowns | Premier Soccer League −1.35z | 2,764 | 2 | 4 | 0 |
| 24 | Olwethu Makhanya | DF | Philadelphia Union | Major League Soccer −0.71z | 2,448 | 1 | 0 | 0 |
| 25 | Kamogelo Sebelebele | FW | Orlando Pirates | Premier Soccer League −1.35z | 2,316 | 5 | 2 | 0 |
| 26 | Bradley Cross | DF | Kaizer Chiefs | Premier Soccer League −1.35z | 1,843 | 0 | 0 | 0 |
Source · Official squad announcements · API-Football (global club coverage). 1 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 96%.
§ 07
Tournament context
The host-nation environment this team meets — diaspora support, climate and altitude exposure at their venues, language familiarity.
Diaspora in the hosts
165,634
3.0 per 1,000 of home population
Host-language familiarity
Shared
primary language Afrikaans · spoken in a host
Climate adaptation gap
+9.0°C
home-vs-venue heat differential
Venue extremes
44°C
peak heat index · altitude up to 2,287 m
Travel
9h
max time-zone shift · nearest venue 12,635 km
Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records
§ 08
Elo trajectory
South Africa's long-run strength against the qualified-field median, 1950–2026.
Fig. D4 eloratings.net method · year-end values
South Africa — Elo since 1950
South Africa Qualified-field median
South Africa ends the series at 1663 Elo, the world’s 80th-ranked side — below the qualified-field median.
Which Elo is this?
This line is the public eloratings.net series (year-end ratings), which terminates exactly at the current rating and world rank shown on the marker. It is a different number from the Elo shown in the header band (a panel-normalised rating used inside the forecast); the two are ~0.99 correlated but on different scales. We keep them distinct rather than blend them.
§ 09
Data coverage
96% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
96% Fitness-readiness coverage Where below 100%, part of the fitness signal is imputed by the de-biasing layer.
n = 3 Out-of-sample tournaments The model is validated on three held-out World Cups; it matches the market, it does not beat it.
Validated on n=3 held-out tournaments; coverage below 1.0 means part of this squad's club-form/fitness is imputed (the global de-biasing layer). For South Africa, 1 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →