Japan
- Coach
- Hajime Moriyasu home · Japanese
- Elo (model)
- 1,906 world 11th
- Squad value
- €384M
- Power → Reality
- 14th 16th −0.23 pp · tough draw
§ 01
The forecast
How far Japan 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
Japan — stage progression
On the central forecast, Japan more likely than not reaches the Round of 32 (83%). Champion probability is 1.6% ± 0.04 pts.
§ 02
The group & the path
Group F advancement odds, the bracket half Japan sits in, and the earliest round they could meet each leading side.
| Group F | Confed | Advance (top 2) | Reach R32 | |
|---|---|---|---|---|
| 1 | 🇳🇱Netherlands | UEFA | 52.2% | 92.3% |
| 2 | 🇯🇵Japan | AFC | 37.9% | 83.1% |
| 3 | 🇸🇪Sweden | UEFA | 17.8% | 60.2% |
| 4 | 🇹🇳Tunisia | CAF | 6.6% | 33.2% |
Source · Oxford Football Forecasting model
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 →
§ 03
Match by match
Japan'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 Japan. Knockout fixtures are not shown — their occupants are still probabilistic, so there is no single pairing to forecast yet.
§ 04
Strength profile
Where Japan stands against the median of the 48-team field, metric by metric. The dot is Japan; the dashed line is the field median (1.0×).
Fig. D2 Relative to the 48-team median
Japan 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
Japan on the decoupling axis
g = −0.19 ± 0.07: 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 | Zion Suzuki | GK | Parma | Serie A +1.70z | 1,985 | 0 | 24 | 0 |
| 2 | Yukinari Sugawara | DF | Werder Bremen | Bundesliga +1.84z | 2,432 | 0 | 21 | 2 |
| 3 | Shōgo Taniguchi | DF | Sint-Truiden | Jupiler Pro League −0.07z | 2,962 | 1 | 38 | 1 |
| 4 | Kō Itakura | DF | Ajax | Eredivisie +0.74z | 1,915 | 1 | 40 | 2 |
| 5 | Yūto Nagatomo | DF | FC Tokyo | J1 League −0.99z | 1,759 | 0 | 145 | 4 |
| 6 | Wataru Endo (captain) | MF | Liverpool | Premier League +2.21z | 497 | 0 | 73 | 4 |
| 7 | Ao Tanaka | MF | Leeds United | Premier League +2.21z | 1,866 | 4 | 38 | 8 |
| 8 | Takefusa Kubo | MF | Real Sociedad | La Liga +2.13z | 1,745 | 2 | 49 | 7 |
| 9 | Keisuke Gotō | FW | Sint-Truiden | Jupiler Pro League −0.07z | 2,885 | 11 | 4 | 0 |
| 10 | Ritsu Dōan | MF | Eintracht Frankfurt | Bundesliga +1.84z | 3,057 | 7 | 65 | 11 |
| 11 | Daizen Maeda | MF | Celtic | Premiership −0.28z | 3,659 | 14 | 27 | 4 |
| 12 | Keisuke Ōsako | GK | Sanfrecce Hiroshima | J1 League −0.99z | 7,103 | 0 | 11 | 0 |
| 13 | Keito Nakamura | MF | Reims | Ligue 2 +1.70z | 2,491 | 14 | 25 | 10 |
| 14 | Junya Itō | MF | Genk | — | — no club data | — | 69 | 15 |
| 15 | Daichi Kamada | MF | Crystal Palace | Premier League +2.21z | 3,208 | 1 | 49 | 12 |
| 16 | Tsuyoshi Watanabe | DF | Feyenoord | Eredivisie +0.74z | 3,320 | 5 | 11 | 0 |
| 17 | Yuito Suzuki | MF | SC Freiburg | Bundesliga +1.84z | 2,951 | 9 | 6 | 0 |
| 18 | Ayase Ueda | FW | Feyenoord | Eredivisie +0.74z | 3,315 | 26 | 39 | 16 |
| 19 | Kōki Ogawa | FW | NEC | — | — no club data | — | 15 | 11 |
| 20 | Ayumu Seko | DF | Le Havre | Ligue 1 +1.70z | 2,625 | 0 | 14 | 0 |
| 21 | Hiroki Itō | DF | Bayern Munich | — | — no club data | — | 24 | 1 |
| 22 | Takehiro Tomiyasu | DF | Ajax | Eredivisie +0.74z | 247 | 0 | 43 | 1 |
| 23 | Tomoki Hayakawa | GK | Kashima Antlers | J1 League −0.99z | 3,510 | 0 | 4 | 0 |
| 24 | Kaishū Sano | MF | Mainz 05 | Bundesliga +1.84z | 4,163 | 2 | 13 | 0 |
| 25 | Junnosuke Suzuki | DF | Copenhagen | Superliga −0.53z | 2,472 | 2 | 6 | 0 |
| 26 | Kento Shiogai | FW | VfL Wolfsburg | Bundesliga +1.84z | 344 | 1 | 2 | 0 |
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%.
§ 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
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
§ 08
Elo trajectory
Japan's long-run strength against the qualified-field median, 1951–2026.
Fig. D4 eloratings.net method · year-end values
Japan — Elo since 1951
Japan ends the series at 1994 Elo, the world’s 11th-ranked side — above 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 Japan, 3 of 26 players are shown as “— no club data”. Full validation, calibration & conformal coverage →