Group F · Matchday 3
JapanvsSweden
2026-06-25·18:00 localPredictions finalised
The forecast
Match-outcome probability
- Japan win51.5%
- Draw25.7%
- Sweden win22.8%
A clash of identities: Japan's low-block approach meets Sweden's transition-heavy style in a fixture the model gives to Japan at 49%.
Why the model says this
Favoring Japan
- ·Japan is favoured by an Elo gap of 185 points, leading the Elo model to predict a 63.4% win probability for Japan.
- ·Japan's expected goals (1.4 xG) are higher than Sweden's (1.16 xG).
- ·Japan has won 5 of their last 6 matches, scoring 11 goals and conceding only 2.
- ·Japan holds a FIFA rank of 18.
Favoring Sweden
- ·Sweden has a superior historical head-to-head record against Japan, with 2 wins and 2 draws from 4 matches.
- ·The DC model predicts a 30.1% win probability for Sweden, and the HP model predicts 31.0%, both higher than the ensemble's 27.3%.
- ·Sweden has scored 6 goals in their last 2 matches.
- ·Sweden exhibits a highly direct style (96.2 percentile for directness index) and high set-piece reliance (19.3% of xG from set pieces, 88.2 percentile).
What the model can't fully price
- ·The model does not fully account for the impact of 3 players across both squads carrying a fitness doubt, as its lineup channel currently contributes zero.
- ·As a Group F Matchday 3 fixture, specific group stage scenarios and team motivation (e.g., needing a win or a draw) are not explicitly priced into the probabilities.
Form check
Japan
ImprovingJapan has been in excellent form, winning 5 of their last 6 matches. They have shown strong defensive solidity, conceding only 2 goals in this period, while scoring 11. Their only non-win was a 2-2 draw.
5 wins in last 6 matches
Sweden
ImprovingSweden's recent form shows an improvement after a difficult spell. Following three consecutive losses, they secured a draw and two wins, scoring 6 goals in their last two victories.
6 goals scored in last 2 matches
Analysis
How it plays out
Neither side wants sustained possession. Japan's low block and Sweden's transition approach could produce a cagey contest decided by set pieces and moments. Japan's aggressive press (PPDA 26.7) against Sweden's deeper build-up (PPDA 31.2) creates a clear territory question: can Japan force errors high up, or will Sweden play through the press and find space behind it?
What decides it
Japan defend deep and limit space. Set pieces and individual errors become the most likely routes to goal. Sweden will concede possession willingly and attack in transition. Their defensive block needs to hold without fouling in dangerous areas. The scoring threat is evenly split: Daichi Kamada (6.4%) and Emil Forsberg (4.8%).
Off the pitch
Hajime Moriyasu (8 years in charge of Japan) vs Graham Potter (1 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
Likely the last World Cup for Yūto Nagatomo. Tournament experience at this level is hard to quantify but hard to replace.
▸Goals & scorelines
Likeliest score 1–1 (13.4%) · xG 1.4 - 1.1
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–113.4%
- 1–010.8%
- 0–09.1%
- 2–18.8%
- 0–18.3%
From the Dixon-Coles joint Poisson with the low-score correction. Scorelines are listed in probability order; this is a description of the model's distribution, not a recommendation.
Most likely half-time scorelines
- 0–029.5%
- 1–019.5%
- 0–115.1%
- 1–111.6%
- 2–07.0%
Same Dixon-Coles fit as the full-time list above, with rates halved to a 45-minute window and the low-score correction applied to that 1st-half block. The 0-0 row sits higher here than at full-time because fewer minutes have elapsed.
Goal totals
- More than 0.5 goals90.9%
- More than 1.5 goals71.7%
- More than 2.5 goals45.2%
- More than 3.5 goals23.9%
- More than 4.5 goals10.7%
- More than 5.5 goals4.1%
- Both teams score50.7%
Each row is the probability the match finishes with more than the listed number of goals. Both-teams-to-score is the probability each side scores at least once. All values are marginals of the Dixon-Coles joint goal grid that produces the scoreline list above — not market lines or any other operator construct.
Event-typed probabilities
- Japan clean sheetOpposing team scores zero33.6%
- Sweden clean sheetOpposing team scores zero24.8%
Derived from the same Dixon-Coles joint distribution as the scoreline list. These are descriptive event probabilities — see CLAUDE.md §3/§4 (formerly COMPLIANCE.md §4.2.7) for the framing the project uses.
Win-margin probability
- Japan by 4+2.4%
- Japan by 3+7.8%
- Japan by 2+20.9%
- Japan by 1+43.1%
- Draw28.3%
- Sweden by 1+28.6%
- Sweden by 2+11.4%
- Sweden by 3+3.4%
- Sweden by 4+0.8%
Each row is the probability the match ends with the listed margin or larger in that direction. Marginal of the Dixon-Coles joint goal grid; the “by 1+” rows plus the draw row sum to 1.
▸How the match unfolds
Over 2.5 goals 45.2% · BTTS 50.7%
Game state through the match
- Japan ahead43.9%
- Level26.7%
- Sweden ahead29.4%
Probability of each game state at minutes 0, 15, 30, 45, 60, 75, 90 — derived from two independent thinned-Poisson processes with the Dixon-Coles per-team rates. The three lines always sum to 1 at each minute. The right column shows the state at the match's closing minute.
When the first goal arrives
- 0–1533.9%
- 15–3022.4%
- 30–4514.8%
- 45–609.8%
- 60–756.5%
- 75–904.3%
- No goal8.3%
Probability the match's first goal arrives in each 15-minute window. Homogeneous Poisson with combined rate λ = λh + λa from the Dixon-Coles fit; the seven rows (six windows + no-goal tail) sum to 1.
Half-time / full-time grid
| HT ↓ / FT → | HJapan win | DDraw | ASweden win |
|---|---|---|---|
| HJapan ahead | 27.3% | 4.9% | 1.5% |
| DLevel | 14.5% | 17.3% | 10.5% |
| ASweden ahead | 1.9% | 4.8% | 17.3% |
Each cell is P(half-time result, full-time result). All nine cells sum to 1. Derived from a halved-λ Dixon-Coles fit for the first half plus an independent-Poisson second-half convolution.
Comeback probability
- Japan trail at HT, avoid defeat at FT6.8%
- Sweden trail at HT, avoid defeat at FT6.3%
Joint probability — P(side trailing at half-time AND avoiding defeat at full-time). NOT conditional on trailing at HT. Derived from the same half-time / full-time decomposition that produces the HT/FT grid above; a tied first half is neither a home nor an away comeback opportunity.
Cards
- Expected yellow cardsMean of the Poisson on total yellow cards.3.45
- Total yellows over 2.567.0%
- Total yellows over 3.545.3%
- Total yellows over 4.526.5%
- Any red cardP(at least one red card in the match).9.5%
Referee not yet assigned. Using the 2026 pool-mean per-match rate as a placeholder; the model picks up the referee's personal rate once the assignment is published. Total yellow cards modelled as a Poisson with mean equal to two team baselines plus the referee's deviation from the pool mean. Reds are modelled the same way, independently. See /docs/methodology/.
▸Teams & players
Top scorer: Kamada (6.4%)
Match detail
Japan
Model-rated key players: Daichi Kamada (MF) — P(scores) 6.4%; Ayase Ueda (FW) — P(scores) 4.1%; Daizen Maeda (FW) — P(scores) 3.5%.
Japan under Hajime Moriyasu play a low block game, with just 44% possession — among the lowest in the field. Their likely shape is a 4-2-3-1, though they have also used other. They sit deeper and pick their moments to press (PPDA 26.7).
Japan will look to stay compact and frustrate opponents, limiting space and hitting on the break. Set-piece proficiency — both attacking and defending — becomes critical when open-play chances are limited by design. Managing minutes for Yūto Nagatomo across what could be seven matches will test the coaching staff's rotation planning.
Sweden
Model-rated key players: Emil Forsberg (MF) — P(scores) 4.8%; Viktor Gyökeres (FW) — P(scores) 4.7%; Alexander Isak (FW) — P(scores) 4.0%.
Sweden under Graham Potter play a transition heavy game, with just 36% possession — among the lowest in the field. They sit deeper and pick their moments to press (PPDA 31.2) and move the ball forward quickly at 5.2 passes per attack. They are selective in their shooting (10.0 per 90) and rely heavily on set pieces (19% of their xG).
Sweden rely on defensive discipline and quick transitions — absorbing pressure and converting turnovers into attacking chances. Concentration and defensive organisation for full 90-minute stretches will determine whether the approach holds against top opposition.
Japan historically converts 6.3% of xG from set-pieces, contributing 0.09 expected set-piece goals in this fixture. Sweden converts 19.3% from set-pieces (0.21 expected). Combined, the model expects 0.30 set-piece goals across the 90 minutes.
- P(Japan scores set-piece goal) 8.4%
- P(Sweden scores set-piece goal) 18.9%
- P(set-piece goal in match) 25.8%
Japan: Takefusa Kubo on corners (18 corners), Daichi Kamada on free kicks (per fbref 2022 23) · Sweden: Niclas Eliasson on corners (56 corners) (per fbref 2020 21)
If a penalty is awarded to Japan, the model gives 71.4% conversion, 74.3% for Sweden.
Japan primary PK: Daichi Kamada (2/2 in 2022-23, per fbref 2022 23) · Sweden primary PK: Emil Forsberg (4/4 in 2021-22, per fbref 2020 21).
Derived from the model's per-fixture forecast joint and supporting reference data (predicted squads, set-piece xG share, PK posteriors, club minutes). See /docs/methodology/ for the full methodology.
Tactical forecast
- PPDA
- 26.7
- Possession
- 44%
- Directness (yds/pass)
- 6.5
- Long balls/90
- 35
- Set-piece xG
- 6%
- PPDA
- 31.2
- Possession
- 36%
- Directness (yds/pass)
- 9.2
- Long balls/90
- 41
- Set-piece xG
- 19%
Style profile per side from StatsBomb open-data aggregation across recent international tournaments (Euro 2020/2024, Copa America 2024, AFCON 2023, World Cup 2018/2022). The tactical-fingerprint badge maps each team’s observed style vector into one of eight canonical archetypes via a rule-based classifier; teams with fewer than three matches of qualifying coverage carry an “insufficient-data” label rather than being forced into a default. Sides outside the StatsBomb-open corpus use FotMob team match stats from recent qualifiers and friendlies instead (possession and shot volume only), marked as partial coverage. PPDA = passes the side allows per defensive action (lower = more intense press). Formation distributions are not yet produced — that head of the §2.7 classifier is pending its own data pull. See /docs/methodology/.
Squad depth
Most irreplaceable starters
Japan
- Ayase UedaStrikerCover: Yuito Suzuki · 0.310.34gap
- Kōki OgawaStrikerCover: Yuito Suzuki · 0.310.14gap
- Takefusa KuboWingerCover: Keito Nakamura · 0.590.13gap
Sweden
- Lucas BergvallCentral midfieldCover: Besfort Zeneli · 0.460.37gap
- Alexander IsakStrikerCover: Gustaf Nilsson · 0.620.33gap
- Yasin AyariCentral midfieldCover: Besfort Zeneli · 0.460.23gap
Gap = how far a side's rating at the position falls from the starter to his likely in-squad replacement (named under each name). Larger = harder to replace. Descriptive metric, does not feed the published probabilities. Methodology →
Match conditions
- AltitudeNear sea level168 m
- Avg temperatureFive-year mean over the tournament window29.4 °C
- Avg humidity63%
- Heat stressShade WBGT ~30.8 °CHigh heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf stadium; a temporary natural-grass pitch on a sand root-zone is laid over the turf for the tournament.
Heat stress is a shade Wet-Bulb Globe Temperature proxy from the venue's climatology mean temperature and humidity; FIFA mandates cooling breaks at WBGT 32 °C. Evening kickoff (local time). These are long-window averages, not a match-day forecast, and they are not inputs to the forecast.
Top scorers · P(scores in this match)
- Daichi KamadaPKMF6.4%
- Ayase UedaFW4.1%
- Daizen MaedaFW3.5%
- Emil ForsbergPKMF4.8%
- Viktor GyökeresFW4.7%
- Alexander IsakFW4.0%
Per-player scoring rate from Model #5 (`p_score_per_match`). Reflects each player's npxG/90, expected minutes, team xG share, and the average opposing-team defence. See /docs/methodology/.
Recent match form
Last match player ratings
Japan
vs Brazil · avg 6.7
Sweden
vs France · avg 6.0
Player scores from official highlight analysis of each team's most recent match. Observational, not a model input. Methodology →
Video analysis: player performance
Per-player ratings and event breakdowns from official highlights analysis. Tap a player to see their full match timeline.
8Daizen MaedaScored Japan's crucial opening goal with excellent composure and finishing ability.
1goals▼
Scored Japan's crucial opening goal with excellent composure and finishing ability.
Match timeline
6Ao TanakaPositioned himself well for a shot but failed to hit the target, indicating a missed opportunity.
1shots▼
Positioned himself well for a shot but failed to hit the target, indicating a missed opportunity.
Match timeline
6Benjamin NygrenMade a late appearance without any described impact on the match.
▼
Made a late appearance without any described impact on the match.
Match timeline
Match observations
- The match was an evenly contested affair, with both teams creating several opportunities to score.
- Japan initially gained the advantage through a well-executed attacking move, but Sweden responded with a moment of individual brilliance.
- The game featured periods of sustained pressure from both sides, leading to multiple shots on target and crucial saves.
▸Under the hood
Model-by-model comparison
Japan vs Sweden
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 60.1% | 22.0% | 17.9% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 44.2% | 27.7% | 28.1% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 43.5% | 27.1% | 29.4% |
| Bayesian stackingLearned-weight combination | — | 51.6% | 26.0% | 22.4% |
| Ensemble (published)Uniform average + isotonic calibration | — | 49.1% | 24.3% | 26.6% |
How each model works
- Elo
- Each team carries a single strength rating updated after every match by a margin-aware K-factor. Match probabilities come from the logistic function of the rating gap. Elo is fast-adapting but coarse — it sees only who won and by how much, not how the goals were scored.
- Dixon-Coles
- A Poisson regression on team-level attack and defence parameters, fitted via maximum likelihood with an exponential time-decay weighting. The Dixon-Coles correction adjusts the four low-score cells (0-0, 1-0, 0-1, 1-1) where independent Poisson underestimates dependence. Produces full scoreline distributions, not just H/D/A.
- Hierarchical Poisson
- A Bayesian Poisson model fitted via MCMC (PyMC) with hierarchical priors that pool attack and defence parameters within confederations. Shrinks small-sample teams toward their confederation mean — helpful for nations with few recent competitive fixtures. Slower to fit but better-calibrated on the tails.
- Bayesian stacking
- Optimises simplex weights (w_elo, w_dc, w_hp) to maximise the leave-one-out log-score across a walk-forward backtest (Yao et al. 2018). The result is a weighted average of the three component models' probabilities, then isotonic-calibrated. Adds no extra features — just learns which component to trust more from historical accuracy.
- Ensemble (published)
- Equal-weight average of all three component models, followed by per-class isotonic regression calibration fitted on 24 months of walk-forward out-of-fold predictions. This is the probability published on the site. The uniform mean is deliberately simple — it avoids overfitting to the stacking weights' training window.
Three independent component models feed two combination strategies. The uniform ensemble is the published probability; Bayesian stacking uses learned weights. Amber bars flag >5pp divergence from the published number. Full methodology
Probability decomposition (transparency surface)
- Baseline ensemble — P(Japan win)51.4%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Japan win)51.4%
Decomposition of the published P(Japan win) into the calibrated- baseline plus contributions from the §2.3 expected-XI lineup delta and the §2.7 style-matchup interaction. The §2.7 roadmap is explicit that style effects are second-order to team strength — single-digit-percentage P(win) shifts on extreme style matchups, near-zero on balanced ones. We surface the decomposition for transparency even when the contributions are small; the baseline carries the prediction. Methodology: /docs/methodology.
For this fixture both contributions round to under 0.05pp — the fitted style-matchup pair effect is in the small-magnitude regime the model expects to dominate.
Head-to-head history
| Date | Competition | Venue | Score | Result | xG |
|---|---|---|---|---|---|
| 25 May 2002 | Friendly | HTokyo | 1–1 | D | — |
| 13 Feb 1997 | King's Cup | NBangkok | 0–1 | L | — |
| 22 Feb 1996 | Lunar New Year Cup | NSo Kon Po | 1–1(pens) | L | — |
| 10 Jun 1995 | Friendly | NNottingham | 2–2 | D | — |
Japan vs Sweden, every senior international meeting in the martj42 results dataset (score from Japan's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for Japan or Sweden.
- Stage:
- Group F · Matchday 3
- Date:
- 25 Jun
Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.
- 1.Squad availability: 1 carrying a fitness doubt across the two squads. The forecast does not adjust for who is missing: its lineup channel currently contributes zero, so this is context the probabilities do not include.
Japan
Japan: 1 carrying a fitness doubt.
- DoubtWataru Endo (midfielder) is carrying Foot injury — a depth-level fitness watch item.
Sweden
Sweden come in at close to full strength.
Both projected XIs look intact; the fitness concerns are at squad-depth level rather than among first-choice starters.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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