Korea Republic
- Coach
- Hong Myung-bo home · South Korean
- Elo (model)
- 1,758
- Squad value
- €202M
- Power → Reality
- 30th 30th +0.03 pp · neutral draw
§ 01
The forecast
How far Korea Republic 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
Korea Republic — stage progression
On the central forecast, Korea Republic more likely than not reaches the Round of 32 (68%). Champion probability is 0.2% ± 0.01 pts.
§ 02
The group & the path
Group A advancement odds, the bracket half Korea Republic 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
Earliest possible meetings
No collision rows recorded for this team.
Collision = the earliest round the bracket wiring could pit Korea Republic against that side. Full bracket & collision matrix →
§ 03
Match by match
Korea Republic'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%.
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 Korea Republic. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where Korea Republic stands against the median of the 48-team field, metric by metric. The dot is Korea Republic; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
Korea Republic 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
Korea Republic on the decoupling axis
g = −0.23 ± 0.05: 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.
| # | Player | Pos | Club | League | Club min | Gls | Caps | NT gls |
|---|---|---|---|---|---|---|---|---|
| 1 | Kim Seung-gyu | GK | FC Tokyo | J1 League −0.99z | 1,260 | 0 | 87 | 0 |
| 2 | Lee Han-beom | DF | Midtjylland | Superliga −0.53z | 2,856 | 0 | 8 | 0 |
| 3 | Lee Gi-hyuk | MF | Gangwon FC | K League 1 | 1,291 | 0 | 3 | 0 |
| 4 | Kim Min-jae | DF | Bayern Munich | Bundesliga +1.84z | 2,113 | 1 | 79 | 4 |
| 5 | Kim Tae-hyeon | DF | Kashima Antlers | J1 League −0.99z | 2,393 | 0 | 7 | 0 |
| 6 | Hwang In-beom | MF | Feyenoord | Eredivisie +0.74z | 1,466 | 1 | 73 | 6 |
| 7 | Son Heung-min (captain) | FW | Los Angeles FC | Major League Soccer −0.71z | 1,103 | 12 | 144 | 56 |
| 8 | Paik Seung-ho | MF | Birmingham City | Championship +2.21z | 3,533 | 4 | 27 | 3 |
| 9 | Cho Gue-sung | FW | Midtjylland | Superliga −0.53z | 1,980 | 5 | 44 | 12 |
| 10 | Lee Jae-sung | MF | Mainz 05 | Bundesliga +1.84z | 2,787 | 6 | 105 | 15 |
| 11 | Hwang Hee-chan | MF | Wolverhampton Wanderers | — | — no club data | — | 79 | 17 |
| 12 | Song Bum-keun | GK | Jeonbuk Hyundai Motors | K League 1 | 1,530 | 0 | 3 | 0 |
| 13 | Lee Tae-seok | DF | Austria Wien | Bundesliga +0.26z | 2,577 | 3 | 15 | 1 |
| 14 | Cho Wi-je | DF | Jeonbuk Hyundai Motors | — | — no club data | — | 1 | 0 |
| 15 | Kim Moon-hwan | DF | Daejeon Hana Citizen | K League 1 | 1,065 | 0 | 35 | 0 |
| 16 | Park Jin-seob | DF | Zhejiang | — | — no club data | — | 14 | 1 |
| 17 | Bae Jun-ho | MF | Stoke City | Championship +2.21z | 3,074 | 3 | 13 | 2 |
| 18 | Oh Hyeon-gyu | FW | Beşiktaş | Süper Lig +0.49z | 1,265 | 7 | 27 | 6 |
| 19 | Lee Kang-in | MF | Paris Saint-Germain | Ligue 1 +1.70z | 1,897 | 3 | 47 | 11 |
| 20 | Yang Hyun-jun | MF | Celtic | Premiership −0.28z | 3,405 | 9 | 9 | 0 |
| 21 | Jo Hyeon-woo | GK | Ulsan HD | K League 1 | 2,340 | 0 | 48 | 0 |
| 22 | Seol Young-woo | DF | Red Star Belgrade | — | — no club data | — | 34 | 0 |
| 23 | Jens Castrop | DF | Borussia Mönchengladbach | Bundesliga +1.84z | 1,624 | 3 | 7 | 0 |
| 24 | Kim Jin-gyu | MF | Jeonbuk Hyundai Motors | K League 1 | 1,151 | 2 | 22 | 3 |
| 25 | Eom Ji-sung | MF | Swansea City | Championship +2.21z | 2,718 | 3 | 9 | 2 |
| 26 | Lee Dong-gyeong | MF | Ulsan HD | K League 1 | 416 | 0 | 18 | 4 |
Source · Official squad announcements · API-Football (global club coverage). 4 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 85%.
§ 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
1,173,605
23.0 per 1,000 of home population
Host-language familiarity
Foreign
primary language Korean
Climate adaptation gap
+4.6°C
home-vs-venue heat differential
Venue extremes
44°C
peak heat index · altitude up to 1,671 m
Travel
15h
max time-zone shift · nearest venue 8,161 km
Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records
§ 08
Elo trajectory
Korea Republic's long-run strength against the qualified-field median, 1950–2026.
Fig. D4 eloratings.net method · year-end values
Korea Republic — Elo since 1950
Korea Republic ends the series at 1881 Elo, the world’s 29th-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
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 Korea Republic, 4 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →