Round of 32 · Match 5

FrancevsSweden

2026-06-30·17:00 local·MetLife Stadium · New York/New JerseyPredictions finalised

Snapshot · 2026-07-14Model 1.0.0Final prediction · locked 30 Jun, 19:12 UTCFrance·Sweden·
Full time · forecast gradedFrance 3 0 SwedenThe locked pre-match forecast has been graded against this result.See the calibration recap →

Match signals

Factors that favour each side, from statistical models to group stage form and match conditions. Longer bars = stronger advantage.

FranceSignal balanceSweden
95%5%

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

5 France
79%Elo Rating Model0%
StrongStrong

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%.

64%Dixon-Coles Model14%
StrongStrong

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%.

62%Hierarchical Poisson16%
StrongStrong

Groups teams by confederation to share information. Helps for teams with fewer matches. It rates France at 62% to win vs Sweden at 16%.

69%Final Ensemble9%
StrongStrong

The published probability after calibration and adjustments. This is what the model says. It rates France at 69% to win vs Sweden at 9%.

3/3Model Agreement0/3
StrongStrong

All 3 models agree: France is favoured. When models agree, the signal is stronger.

Tournament Form

4 France
18pts (6W 0D 0L)Tournament Record4pts (1W 1D 2L)
StrongStrong

France collected 18 points (6W 0D 0L) vs Sweden's 4 (1W 1D 2L). A stronger tournament record.

2.67/matchGoals Scored1.75/match
ModerateModerate

France averaged 2.67 goals per match vs Sweden's 1.75. More firepower coming in.

0.33 conceded/matchDefence2.5 conceded/match
StrongStrong

France conceded just 0.33 goals/match vs Sweden's 2.5. Tighter at the back.

+14Goal Difference-3
StrongStrong

France's goal difference of +14 is better than Sweden's -3. They outperformed opponents by more.

📈Momentum

1 France1 Sweden
+41.4Tournament Rating Change+12.6
ModerateModerate

France's rating rose +41.4 during the tournament while Sweden's moved +12.6. The tournament has been kinder to France.

-0.0074Player Form Trend+0.0012
ModerateModerate

Sweden's players improved their form ratings during the tournament (+0.0012) vs France (-0.0074). Players trending upward.

🏆Team Quality

3 France
2081Overall Strength (Elo)1719
StrongStrong

France is rated 2081 vs Sweden's 1719 (gap: 362). That's a very large gap in historical team strength.

1.97 xGExpected Chance Creation0.82 xG
StrongStrong

The model expects France to create 1.97 expected goals vs Sweden's 0.82. More and better chances projected.

0.30Star Power0.31
Even

Similar star-player quality in both squads.

0.052Squad Familiarity0.000
StrongStrong

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

5,900kmTravel Distance6,306km
Even

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.

예측

Match-outcome probability

  • France win
    57.0%
  • Draw
    26.0%
  • Sweden win
    17.0%

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.

Likeliest score2–011.9%
First goal0-15'37.1%
Both teams score48.7%
Over 2.5 goals52.7%
Top scorerThuram12.0%
Expected goals2.0 - 0.8
Loading pitch visualisation...

골 및 스코어라인

Likeliest score 2–0 (11.9%) · xG 2.0 - 0.8

Expected goals

France
1.97
Sweden
0.82

Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.

Most likely scorelines

  • 2–0
    11.9%
  • 1–0
    11.5%
  • 1–1
    10.5%
  • 2–1
    9.8%
  • 3–0
    7.8%

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

  • 0–0
    25.5%
  • 1–0
    23.8%
  • 2–0
    12.0%
  • 1–1
    10.6%
  • 0–1
    9.6%

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 goals
    93.2%
  • More than 1.5 goals
    77.2%
  • More than 2.5 goals
    52.7%
  • More than 3.5 goals
    30.4%
  • More than 4.5 goals
    15.0%
  • More than 5.5 goals
    6.4%
  • Both teams score
    48.7%

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%
  • Draw
    22.1%
  • 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.

경기 전개 양상

Over 2.5 goals 52.7% · BTTS 48.7%

Game state through the match

0%25%50%75%100%0'15'30'45'60'75'90'
  • 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

  • 0–15
    37.1%
  • 15–30
    23.3%
  • 30–45
    14.7%
  • 45–60
    9.2%
  • 60–75
    5.8%
  • 75–90
    3.6%
  • No goal
    6.2%

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

Joint probability of half-time and full-time results
HT ↓ / FT →HFrance winDDrawASweden win
HFrance ahead43.6%3.9%0.8%
DLevel18.2%13.3%5.6%
ASweden ahead2.4%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 FT
    6.2%
  • Sweden trail at HT, avoid defeat at FT
    4.8%

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.

Symmetric (averaged over both orderings — used by the bracket simulator)
  • France
    50.8%
  • Sweden
    49.2%
If France kicks first
  • France
    63.4%
  • Sweden
    36.6%
If Sweden kicks first
  • France
    38.3%
  • Sweden
    61.7%
Expected paired rounds
4.8
Decided in regulation 5 kicks
72.7%

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/.

팀 및 선수

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%.

How they play

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.

What they must execute

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.

Storylines
Out injured: Kylian MbappéTorn muscle fiber, no expected return. Composite 0.99 — would have been a likely starter.
Touchline: Didier DeschampsDefending champion — Winner 2022.
Club xG: Squad averages 1.90 xG per match across club football last season — #2 of 20 in the field for attacking pedigree from each player's domestic side (21 of 24 players matched to a known club).

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%.

How they play

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).

What they must execute

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.

Storylines
Heat schedule: 3 group-stage matches at venues averaging 26°C+ — Monterrey, Houston, Dallas (peak 29.4°C average).
Touchline: Graham PotterFirst World Cup as head coach, appointed 2025.
Dead-ball: Niclas EliassonTakes corners and free kicks — the team's dead-ball threat.
Workload going in

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).

Set-piece outlook

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)

Penalty outlook

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

  1. N'Golo KantéDefensive midfieldNo natural backup0.43gap
  2. Aurélien TchouaméniDefensive midfieldNo natural backup0.26gap
  3. Kylian MbappéStrikerCover: Jean-Philippe Mateta · 0.770.21gap

Sweden

  1. Lucas BergvallCentral midfieldCover: Besfort Zeneli · 0.460.37gap
  2. Alexander IsakStrikerCover: Gustaf Nilsson · 0.620.33gap
  3. 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)

Sweden

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

8
Marcus ThuramST
ATK
DEF
PAS
7
Lucas DigneLB
ATK
DEF
PAS
7
Kylian MbappeST
ATK
DEF
PAS
7
Maghnes AklioucheAM
ATK
DEF
PAS
6
Warren Zaire-EmeryCM
ATK
DEF
PAS

Sweden

vs Japan · avg 6.0

6
Benjamin Nygren
ATK
DEF
PAS

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.

France
10
Kylian Mbappé4'–151'

Mbappé delivered an exceptional performance, scoring four goals and providing an assist, showcasing clinical finishing and constant threat.

4goals1shots1on target

Match timeline

4'Kylian Mbappé scores for France, but the goal is disallowed for offside.
17'Sweden's goalkeeper saves a shot from Kylian Mbappé inside the box.
44'Kylian Mbappé scores the opening goal for France from close range.
53'Kylian Mbappé celebrates his goal with the coach.
116'Kylian Mbappé scores his second goal of the match, assisted by Michael Olise.
151'Kylian Mbappé is substituted off for France.
9
Michael 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

Match timeline

38'Sweden's goalkeeper saves a shot from Michael Olise after a skillful dribble.
104'Michael Olise scores France's second goal with a powerful finish.
113'Sweden's goalkeeper makes another save from a shot by Michael Olise.
116'assisted by Michael Olise.
140'Sweden's goalkeeper saves a shot from Michael Olise after an impressive dribbling run.
7
Adrien Rabiot13'–13'

Rabiot contributed to the attack with a shot on target and a key assist, while also applying midfield pressure.

1shots1on target

Match timeline

13'Sweden's goalkeeper makes a save from a shot by Adrien Rabiot following a corner.
6
Jules Kounde

Kounde made an notable attacking contribution by hitting the post, with no defensive issues reported.

6
Ousmane Dembélé29'–29'

Dembélé showcased his attacking flair with an ambitious overhead kick, though it didn't result in a goal.

1shots1on target

Match timeline

29'Sweden's goalkeeper saves an acrobatic overhead kick from Ousmane Dembélé.
Sweden
8
Sweden Goalkeeper13'–140'

The Sweden goalkeeper made numerous crucial saves, single-handedly preventing a much larger deficit for his team.

6saves

Match timeline

13'Sweden's goalkeeper makes a save from a shot by Adrien Rabiot following a corner.
17'Sweden's goalkeeper saves a shot from Kylian Mbappé inside the box.
29'Sweden's goalkeeper saves an acrobatic overhead kick from Ousmane Dembélé.
38'Sweden's goalkeeper saves a shot from Michael Olise after a skillful dribble.
113'Sweden's goalkeeper makes another save from a shot by Michael Olise.
140'Sweden's goalkeeper saves a shot from Michael Olise after an impressive dribbling run.
4
Lagerbielke

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.

분석 내부

Model-by-model comparison

France vs Sweden

High disagreement (17.9%)
ModelWeightHomeDrawAway
EloRating-based strength estimate32%
79.5%
20.5%
0.0%
Dixon-ColesGoal-process model with low-score correction63%
63.9%
21.9%
14.2%
Hierarchical PoissonBayesian model with confederation pooling6%
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%
Home spread: 17.9%
Draw spread: 1.6%
Away spread: 16.2%
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

Match conditions
Stage:
Round of 32 · Match 5
Date:
30 Jun
Venue:
MetLife Stadium, New York/New Jersey
Beyond the model

Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.

  1. 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. 2.Rest differential: Sweden have had 5 days since their previous match versus 4 for France. Rest and recovery are not model inputs.
Availability

France

France come in at close to full strength.

Sweden

Sweden come in at close to full strength.

What it means

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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