WC 2026 · Forecasting Oxford Football Forecasting
🇰🇷

Korea Republic

AFC Group A
0.2% Champion probability ±0.01 MC-SE
Coach
Hong Myung-bo home · South Korean
Elo (model)
1,758
Squad value
€202M
Power → Reality
30th 30th +0.03 pp · neutral draw

Fig. D1 Fixture-aware · 100k sims

Korea Republic — stage progression

Round of 32: 68.42% (95% MC 68.13%–68.71%; MC-SE ±0.15 pts) Round of 32 reach 68.4% ±0.15 Round of 16: 31.81% (95% MC 31.53%–32.10%; MC-SE ±0.15 pts) Round of 16 reach 31.8% ±0.15 Quarter-final: 11.10% (95% MC 10.91%–11.29%; MC-SE ±0.10 pts) Quarter-final reach 11.1% ±0.10 Semi-final: 3.04% (95% MC 2.94%–3.15%; MC-SE ±0.05 pts) Semi-final reach 3.0% ±0.05 Final: 0.86% (95% MC 0.80%–0.92%; MC-SE ±0.03 pts) Final reach 0.9% ±0.03 Champion: 0.22% (95% MC 0.19%–0.24%; MC-SE ±0.01 pts) Champion reach 0.2% ±0.01

On the central forecast, Korea Republic more likely than not reaches the Round of 32 (68%). Champion probability is 0.2% ± 0.01 pts.

Source · Oxford Football Forecasting model
Group A Confed Advance (top 2) Reach R32
1🇲🇽MexicoCONCACAF54.5%92.7%
2🇨🇿CzechiaUEFA35.6%73.4%
3🇰🇷Korea RepublicAFC31.8%68.4%
4🇿🇦South AfricaCAF10.1%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 Korea Republic against that side. Full bracket & collision matrix →

Match 2 · 2026-06-12 · Guadalajara Stadium home
Korea Republic Czechia
33.5% win 30.6% draw 35.9% loss
Most likely 1–1 (14.3%) λ 1.12–1.17 Over 2.5 40% · BTTS 48%
Match 28 · 2026-06-19 · Guadalajara Stadium away
Korea Republic Mexico
18.2% win 25.7% draw 56.1% loss
Most likely 1–1 (12.2%) λ 0.88–1.70 Over 2.5 48% · BTTS 49%
Match 54 · 2026-06-25 · Monterrey Stadium away
Korea Republic South Africa
48.8% win 30.9% draw 20.3% loss
Most likely 1–0 (15.8%) λ 1.30–0.75 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 Korea Republic. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.

Fig. D2 Relative to the 48-team median

Korea Republic vs the field

Elo rating: 1758 vs field median 1780 (0.99× the field) Elo rating 1758 med 1780 Recent NT form: 1.87 ppg vs field median 1.87 ppg (1.00× the field) Recent NT form 1.87 ppg med 1.87 ppg Squad value: €202M vs field median €286M (0.71× the field) Squad value €202M med €286M Squad form (global): 0.160 vs field median 0.211 (0.76× the field) Squad form (global) 0.160 med 0.211 Fitness readiness: 0.701 vs field median 0.707 (0.99× the field) Fitness readiness 0.701 med 0.707 Familiarity / chemistry: 0.015 vs field median 0.015 (1.00× the field) Familiarity / chemistry 0.015 med 0.015 Experience (mean caps): 22 vs field median 25 (0.90× the field) Experience (mean caps) 22 med 25

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.

Source · Oxford Football Forecasting model

Fig. D3 Bayesian projection residual g

Korea Republic on the decoupling axis

aligned (0) ← record > squad price squad valued > record →

g = −0.23 ± 0.05: the record outruns the squad price — the team has achieved more than its comparatively modest squad value would predict.

Source · Oxford Football Forecasting model
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 →

26players
26.0mean age
22mean caps
12%in a top-5 league
22distinct clubs
3largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Kim Seung-gyuGKFC TokyoJ1 League −0.99z1,2600870
2Lee Han-beomDFMidtjyllandSuperliga −0.53z2,856080
3Lee Gi-hyukMFGangwon FCK League 11,291030
4Kim Min-jaeDFBayern MunichBundesliga +1.84z2,1131794
5Kim Tae-hyeonDFKashima AntlersJ1 League −0.99z2,393070
6Hwang In-beomMFFeyenoordEredivisie +0.74z1,4661736
7Son Heung-min (captain)FWLos Angeles FCMajor League Soccer −0.71z1,1031214456
8Paik Seung-hoMFBirmingham CityChampionship +2.21z3,5334273
9Cho Gue-sungFWMidtjyllandSuperliga −0.53z1,98054412
10Lee Jae-sungMFMainz 05Bundesliga +1.84z2,787610515
11Hwang Hee-chanMFWolverhampton Wanderersno club data7917
12Song Bum-keunGKJeonbuk Hyundai MotorsK League 11,530030
13Lee Tae-seokDFAustria WienBundesliga +0.26z2,5773151
14Cho Wi-jeDFJeonbuk Hyundai Motorsno club data10
15Kim Moon-hwanDFDaejeon Hana CitizenK League 11,0650350
16Park Jin-seobDFZhejiangno club data141
17Bae Jun-hoMFStoke CityChampionship +2.21z3,0743132
18Oh Hyeon-gyuFWBeşiktaşSüper Lig +0.49z1,2657276
19Lee Kang-inMFParis Saint-GermainLigue 1 +1.70z1,89734711
20Yang Hyun-junMFCelticPremiership −0.28z3,405990
21Jo Hyeon-wooGKUlsan HDK League 12,3400480
22Seol Young-wooDFRed Star Belgradeno club data340
23Jens CastropDFBorussia MönchengladbachBundesliga +1.84z1,624370
24Kim Jin-gyuMFJeonbuk Hyundai MotorsK League 11,1512223
25Eom Ji-sungMFSwansea CityChampionship +2.21z2,718392
26Lee Dong-gyeongMFUlsan HDK League 14160184

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%.

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

Fig. D4 eloratings.net method · year-end values

Korea Republic — Elo since 1950

1881 world #29
Korea Republic Qualified-field median

Korea Republic ends the series at 1881 Elo, the world’s 29th-ranked side — below the qualified-field median.

Source · eloratings.net
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.

85% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
85% 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 Korea Republic, 4 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →