WC 2026 · Forecasting Oxford Football Forecasting
🇯🇵

Japan

AFC Group F
1.6% Champion probability ±0.04 MC-SE
Coach
Hajime Moriyasu home · Japanese
Elo (model)
1,906 world 11th
Squad value
€384M
Power → Reality
14th 16th −0.23 pp · tough draw

Fig. D1 Fixture-aware · 100k sims

Japan — stage progression

Round of 32: 83.07% (95% MC 82.83%–83.30%; MC-SE ±0.12 pts) Round of 32 reach 83.1% ±0.12 Round of 16: 37.89% (95% MC 37.59%–38.19%; MC-SE ±0.15 pts) Round of 16 reach 37.9% ±0.15 Quarter-final: 20.89% (95% MC 20.64%–21.14%; MC-SE ±0.13 pts) Quarter-final reach 20.9% ±0.13 Semi-final: 9.98% (95% MC 9.79%–10.16%; MC-SE ±0.09 pts) Semi-final reach 10.0% ±0.09 Final: 4.20% (95% MC 4.08%–4.33%; MC-SE ±0.06 pts) Final reach 4.2% ±0.06 Champion: 1.65% (95% MC 1.57%–1.73%; MC-SE ±0.04 pts) Champion reach 1.6% ±0.04

On the central forecast, Japan more likely than not reaches the Round of 32 (83%). Champion probability is 1.6% ± 0.04 pts.

Source · Oxford Football Forecasting model
Group F Confed Advance (top 2) Reach R32
1🇳🇱NetherlandsUEFA52.2%92.3%
2🇯🇵JapanAFC37.9%83.1%
3🇸🇪SwedenUEFA17.8%60.2%
4🇹🇳TunisiaCAF6.6%33.2%

Source · Oxford Football Forecasting model

Bracket position Half 0 · Quadrant 0

Earliest possible meetings

No collision rows recorded for this team.

Collision = the earliest round the bracket wiring could pit Japan against that side. Full bracket & collision matrix →

Match 11 · 2026-06-14 · Dallas Stadium away
Japan Netherlands
26.9% win 30.9% draw 42.2% loss
Most likely 1–1 (14.2%) λ 0.94–1.25 Over 2.5 37% · BTTS 45%
Match 36 · 2026-06-21 · Monterrey Stadium away
Japan Tunisia
57.2% win 28.7% draw 14.1% loss
Most likely 1–0 (18.3%) λ 1.43–0.58 Over 2.5 33% · BTTS 34%
Match 57 · 2026-06-25 · Dallas Stadium home
Japan Sweden
48% win 29.2% draw 22.8% loss
Most likely 1–1 (13.6%) λ 1.40–0.89 Over 2.5 40% · BTTS 46%
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 Japan. 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

Japan vs the field

Elo rating: 1906 vs field median 1780 (1.07× the field) Elo rating 1906 med 1780 Recent NT form: 2.20 ppg vs field median 1.87 ppg (1.18× the field) Recent NT form 2.20 ppg med 1.87 ppg Squad value: €384M vs field median €286M (1.34× the field) Squad value €384M med €286M Squad form (global): 0.208 vs field median 0.211 (0.98× the field) Squad form (global) 0.208 med 0.211 Fitness readiness: 0.753 vs field median 0.707 (1.07× the field) Fitness readiness 0.753 med 0.707 Familiarity / chemistry: 0.009 vs field median 0.015 (0.60× the field) Familiarity / chemistry 0.009 med 0.015 Experience (mean caps): 18 vs field median 25 (0.72× the field) Experience (mean caps) 18 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

Japan on the decoupling axis

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

g = −0.19 ± 0.07: 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
25.0mean age
18mean caps
62%in a top-5 league
23distinct clubs
2largest club bloc
# Player Pos Club League Club min Gls Caps NT gls
1Zion SuzukiGKParmaSerie A +1.70z1,9850240
2Yukinari SugawaraDFWerder BremenBundesliga +1.84z2,4320212
3Shōgo TaniguchiDFSint-TruidenJupiler Pro League −0.07z2,9621381
4Kō ItakuraDFAjaxEredivisie +0.74z1,9151402
5Yūto NagatomoDFFC TokyoJ1 League −0.99z1,75901454
6Wataru Endo (captain)MFLiverpoolPremier League +2.21z4970734
7Ao TanakaMFLeeds UnitedPremier League +2.21z1,8664388
8Takefusa KuboMFReal SociedadLa Liga +2.13z1,7452497
9Keisuke GotōFWSint-TruidenJupiler Pro League −0.07z2,8851140
10Ritsu DōanMFEintracht FrankfurtBundesliga +1.84z3,05776511
11Daizen MaedaMFCelticPremiership −0.28z3,65914274
12Keisuke ŌsakoGKSanfrecce HiroshimaJ1 League −0.99z7,1030110
13Keito NakamuraMFReimsLigue 2 +1.70z2,491142510
14Junya ItōMFGenkno club data6915
15Daichi KamadaMFCrystal PalacePremier League +2.21z3,20814912
16Tsuyoshi WatanabeDFFeyenoordEredivisie +0.74z3,3205110
17Yuito SuzukiMFSC FreiburgBundesliga +1.84z2,951960
18Ayase UedaFWFeyenoordEredivisie +0.74z3,315263916
19Kōki OgawaFWNECno club data1511
20Ayumu SekoDFLe HavreLigue 1 +1.70z2,6250140
21Hiroki ItōDFBayern Munichno club data241
22Takehiro TomiyasuDFAjaxEredivisie +0.74z2470431
23Tomoki HayakawaGKKashima AntlersJ1 League −0.99z3,510040
24Kaishū SanoMFMainz 05Bundesliga +1.84z4,1632130
25Junnosuke SuzukiDFCopenhagenSuperliga −0.53z2,472260
26Kento ShiogaiFWVfL WolfsburgBundesliga +1.84z344120

Source · Official squad announcements · API-Football (global club coverage). 3 of 26 players have no club season matched in API-Football — shown as “— no club data”, not imputed. Form coverage for this squad: 88%.

Diaspora in the hosts

353,416

3.0 per 1,000 of home population

Host-language familiarity

Foreign

primary language Japanese

Climate adaptation gap

+9.6°C

home-vs-venue heat differential

Venue extremes

45°C

peak heat index · altitude up to 493 m

Travel

16h

max time-zone shift · nearest venue 7,550 km

Source · UN DESA international migrant stock · US Census Bureau · Open-Meteo & venue records

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

Japan — Elo since 1951

1994 world #11
Japan Qualified-field median

Japan ends the series at 1994 Elo, the world’s 11th-ranked side — above 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.

88% Squad club-form coverage Share of this squad with a matched club season feeding the global form layer.
88% 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 Japan, 3 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →