Round of 32 · Match 5
FrancevsSweden
2026-06-30·17:00 local·MetLife Stadium · New York/New JerseyPredictions finalised
Match signals
Factors that favour each side, from statistical models to group stage form and match conditions. Longer bars = stronger advantage.
France are dominant at 69% vs Sweden's 9%. Quality, form, and model estimates all point the same way. An upset here would be a major story.
📊What the Models Say
Rates teams by a single strength number updated after every match. Simpler but fast to react. It rates France at 79% to win vs Sweden at 0%.
Simulates the goal-scoring process using attack and defence strength. The heaviest-weighted model. It rates France at 64% to win vs Sweden at 14%.
Groups teams by confederation to share information. Helps for teams with fewer matches. It rates France at 62% to win vs Sweden at 16%.
The published probability after calibration and adjustments. This is what the model says. It rates France at 69% to win vs Sweden at 9%.
All 3 models agree: France is favoured. When models agree, the signal is stronger.
⚽Tournament Form
France collected 18 points (6W 0D 0L) vs Sweden's 4 (1W 1D 2L). A stronger tournament record.
France averaged 2.67 goals per match vs Sweden's 1.75. More firepower coming in.
France conceded just 0.33 goals/match vs Sweden's 2.5. Tighter at the back.
France's goal difference of +14 is better than Sweden's -3. They outperformed opponents by more.
📈Momentum
France's rating rose +41.4 during the tournament while Sweden's moved +12.6. The tournament has been kinder to France.
Sweden's players improved their form ratings during the tournament (+0.0012) vs France (-0.0074). Players trending upward.
🏆Team Quality
France is rated 2081 vs Sweden's 1719 (gap: 362). That's a very large gap in historical team strength.
The model expects France to create 1.97 expected goals vs Sweden's 0.82. More and better chances projected.
Similar star-player quality in both squads.
France's starters play together at club level more often (0.052 cohesion) than Sweden's (0.000). More shared understanding on the pitch.
🌍Match Conditions
Similar travel distances for both teams.
16 signals across 5 categories. Signal strength reflects how large the gap is between the two teams on each factor. Signals are descriptive, not prescriptive.
التوقعات
Match-outcome probability
- France win57.0%
- Draw26.0%
- Sweden win17.0%
A 362-point Elo gap frames this as a significant mismatch, yet the model still gives Sweden a 9% probability of a result — enough to make this more than a formality.
▸الأهداف والنتائج
Likeliest score 2–0 (11.9%) · xG 2.0 - 0.8
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 2–011.9%
- 1–011.5%
- 1–110.5%
- 2–19.8%
- 3–07.8%
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
- 0–025.5%
- 1–023.8%
- 2–012.0%
- 1–110.6%
- 0–19.6%
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 goals93.2%
- More than 1.5 goals77.2%
- More than 2.5 goals52.7%
- More than 3.5 goals30.4%
- More than 4.5 goals15.0%
- More than 5.5 goals6.4%
- Both teams score48.7%
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
- France clean sheetOpposing team scores zero44.1%
- Sweden clean sheetOpposing team scores zero14.0%
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
- France by 4+8.1%
- France by 3+19.6%
- France by 2+39.4%
- France by 1+63.7%
- Draw22.1%
- Sweden by 1+14.2%
- Sweden by 2+4.4%
- Sweden by 3+1.0%
- Sweden by 4+0.2%
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.
▸كيف تتطور المباراة
Over 2.5 goals 52.7% · BTTS 48.7%
Game state through the match
- France ahead64.3%
- Level20.9%
- Sweden ahead14.8%
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
- 0–1537.1%
- 15–3023.3%
- 30–4514.7%
- 45–609.2%
- 60–755.8%
- 75–903.6%
- No goal6.2%
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 → | HFrance win | DDraw | ASweden win |
|---|---|---|---|
| HFrance ahead | 43.6% | 3.9% | 0.8% |
| DLevel | 18.2% | 13.3% | 5.6% |
| ASweden ahead | 2.4% | 3.8% | 8.2% |
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
- France trail at HT, avoid defeat at FT6.2%
- Sweden trail at HT, avoid defeat at FT4.8%
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.
PK shootout simulator
If the match ends level after extra time, the model estimates the shootout outcome from each team's Bayesian-smoothed conversion / save rate (Model #15). The bracket simulator uses the symmetric (averaged) ordering; the two what-if scenarios below show how the win probabilities shift when conditioning on which team kicks first.
- France50.8%
- Sweden49.2%
- France63.4%
- Sweden36.6%
- France38.3%
- Sweden61.7%
First-kicker advantage
The first kicker's per-kick conversion rate is scaled by ×1.050 (about +5.0%), stacked on the Markov chain's structural asymmetry. Real World Cup shootouts use a coin toss for kicker order, so on average the order is 50/50 — the symmetric path above is the relevant number for a single fixture. The ordering-conditioned probabilities are a descriptive what-if scenario.
Literature: first kickers win ≈ 60% historically (Apesteguia & Palacios-Huerta, American Economic Review 2010; Vandebroek et al. 2016).
Per-team posteriors: France conv 73.3%, save 24.4%; Sweden conv 74.3%, save 22.9%. Smoothed against the global prior with prior strength 20 — see /docs/methodology/.
▸الفرق واللاعبون
Top scorer: Thuram (12.0%)
Match detail
France
Model-rated key players: Marcus Thuram (FW) — P(scores) 12.0%; Kylian Mbappé (FW) — P(scores) 8.9%; Bradley Barcola (FW) — P(scores) 7.3%.
France under Didier Deschamps play a balanced game with 51% possession. Their likely shape is a 4-2-3-1, though they have also used 4-3-3. They sit deeper and pick their moments to press (PPDA 26.1) and build patiently through midfield with 7.5 passes per attacking sequence.
France will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. Managing the fitness of Kylian Mbappé could prove decisive — their availability transforms the team's ceiling.
Sweden
Model-rated key players: Emil Forsberg (MF) — P(scores) 4.3%; Viktor Gyökeres (FW) — P(scores) 2.4%; Alexander Isak (FW) — P(scores) 2.1%.
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.
France's predicted XI averages 2,336 club minutes over the 2024-25 season (moderate load).
France coverage: 92.0% (11/11 XI matched against the FBref Big-5) · Sweden: 54.0% (7/11).
France historically converts 16.4% of xG from set-pieces, contributing 0.32 expected set-piece goals in this fixture. Sweden converts 19.3% from set-pieces (0.16 expected). Combined, the model expects 0.48 set-piece goals across the 90 minutes.
- P(France scores set-piece goal) 27.6%
- P(Sweden scores set-piece goal) 14.6%
- P(set-piece goal in match) 38.2%
France: Florian Thauvin on corners (70 corners) (per fbref 2020 21) · Sweden: Niclas Eliasson on corners (56 corners) (per fbref 2020 21)
If a penalty is awarded to France, the model gives 73.3% conversion, 74.3% for Sweden. If this match goes to a shootout, the symmetric (coin-toss averaged) win probability is 50.8% France / 49.2% Sweden.
France primary PK: Marcus Thuram (4/5 in 2018-19, per fbref 2020 21) · 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.
Squad depth
Most irreplaceable starters
France
- N'Golo KantéDefensive midfieldNo natural backup0.43gap
- Aurélien TchouaméniDefensive midfieldNo natural backup0.26gap
- Kylian MbappéStrikerCover: Jean-Philippe Mateta · 0.770.21gap
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 level7 m
- Avg temperatureFive-year mean over the tournament window23.8 °C
- Avg humidity71%
- Heat stressShade WBGT ~25.7 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Artificial-turf NFL stadium; a temporary hybrid natural-grass pitch is being installed over the turf for the tournament, including the final.
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)
- Marcus ThuramPKFW12.0%
- Kylian MbappéFW8.9%
- Bradley BarcolaFW7.3%
- Emil ForsbergPKMF4.3%
- Viktor GyökeresFW2.4%
- Alexander IsakFW2.1%
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
France
vs Paraguay · avg 7.0
Sweden
vs Japan · 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.
10Kylian Mbappé4'–151'Mbappé delivered an exceptional performance, scoring four goals and providing an assist, showcasing clinical finishing and constant threat.
4goals1shots1on target▼
Mbappé delivered an exceptional performance, scoring four goals and providing an assist, showcasing clinical finishing and constant threat.
Match timeline
9Michael Olise38'–140'Olise was instrumental in France's attack, scoring a goal, providing an assist, and consistently creating danger with his exceptional dribbling.
1goals3shots3on target▼
Olise was instrumental in France's attack, scoring a goal, providing an assist, and consistently creating danger with his exceptional dribbling.
Match timeline
7Adrien Rabiot13'–13'Rabiot contributed to the attack with a shot on target and a key assist, while also applying midfield pressure.
1shots1on target▼
Rabiot contributed to the attack with a shot on target and a key assist, while also applying midfield pressure.
Match timeline
6Jules KoundeKounde made an notable attacking contribution by hitting the post, with no defensive issues reported.
Kounde made an notable attacking contribution by hitting the post, with no defensive issues reported.
6Ousmane Dembélé29'–29'Dembélé showcased his attacking flair with an ambitious overhead kick, though it didn't result in a goal.
1shots1on target▼
Dembélé showcased his attacking flair with an ambitious overhead kick, though it didn't result in a goal.
Match timeline
8Sweden Goalkeeper13'–140'The Sweden goalkeeper made numerous crucial saves, single-handedly preventing a much larger deficit for his team.
6saves▼
The Sweden goalkeeper made numerous crucial saves, single-handedly preventing a much larger deficit for his team.
Match timeline
4LagerbielkeLagerbielke struggled defensively against France's potent attack and was prone to losing possession in dangerous areas.
▼
Lagerbielke struggled defensively against France's potent attack and was prone to losing possession in dangerous areas.
Match timeline
Match observations
- France delivered a dominant attacking performance, creating numerous high-quality scoring opportunities against Sweden.
- The match was characterized by France's fluid offensive movements and individual brilliance from their star attackers.
- Sweden's defence struggled to contain the French onslaught, relying heavily on their goalkeeper to prevent a larger deficit.
▸خلف الكواليس
Model-by-model comparison
France vs Sweden
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 79.5% | 20.5% | 0.0% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 63.9% | 21.9% | 14.2% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 61.6% | 22.2% | 16.2% |
| Bayesian stackingLearned-weight combination | — | 77.3% | 19.2% | 3.6% |
| Ensemble (published)Uniform average + isotonic calibration | — | 68.7% | 21.9% | 9.4% |
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
Latest news & match context
- France vs. Spain, 2026 World Cup semifinals: Match thread and discussion · Stars and Stripes FC · 14 Jul
- World Cup Watch Thread: Semi Finals | France vs Spain · Royal Blue Mersey · 14 Jul
- France v Spain - who would England rather face in the World Cup final? · Daily Mirror — Football · 14 Jul
- What color jerseys are France and Spain wearing today? World Cup kit reveal · Yahoo Sports Australia · 14 Jul
- How and where to watch Spain vs. France 2026 World Cup match: TV channel, streaming options · The New York Times · 14 Jul
- Stage:
- Round of 32 · Match 5
- Date:
- 30 Jun
- Venue:
- MetLife Stadium, New York/New Jersey
Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.
- 1.Elimination stakes: A one-off elimination tie. Motivation, risk appetite and game management under tournament pressure are not model inputs; the forecast rests on team strength and the style matchup.
- 2.Rest differential: Sweden have had 5 days since their previous match versus 4 for France. Rest and recovery are not model inputs.
France and Sweden both come in at close to full strength, so the forecast rests on baseline team strength rather than late team-news swings.
Availability from the predicted squads and injury feed; forecast adjustments from the model's own decomposition. See /docs/methodology/.
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