Round of 32 · Match 8
EnglandvsDR Congo
2026-07-01·12:00 local·Mercedes-Benz Stadium · AtlantaPredictions finalised
Match signals
Factors that favour each side, from statistical models to group stage form and match conditions. Longer bars = stronger advantage.
England are dominant at 72% vs DR Congo's 4%. 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 England at 78% to win vs DR Congo at 0%.
Simulates the goal-scoring process using attack and defence strength. The heaviest-weighted model. It rates England at 67% to win vs DR Congo at 9%.
Groups teams by confederation to share information. Helps for teams with fewer matches. It rates England at 67% to win vs DR Congo at 9%.
The published probability after calibration and adjustments. This is what the model says. It rates England at 72% to win vs DR Congo at 4%.
All 3 models agree: England is favoured. When models agree, the signal is stronger.
⚽Tournament Form
England collected 16 points (5W 1D 0L) vs DR Congo's 4 (1W 1D 2L). A stronger tournament record.
England averaged 2.17 goals per match vs DR Congo's 1.25. More firepower coming in.
Similar defensive records: England 1.0, DR Congo 1.25 goals conceded per match.
England's goal difference of +7 is better than DR Congo's +0. They outperformed opponents by more.
📈Momentum
England's rating rose +33.4 during the tournament while DR Congo's moved +27.4. The tournament has been kinder to England.
DR Congo's players improved their form ratings during the tournament (+0.0060) vs England (-0.0068). Players trending upward.
🏆Team Quality
England is rated 2020 vs DR Congo's 1655 (gap: 365). That's a very large gap in historical team strength.
The model expects England to create 1.60 expected goals vs DR Congo's 0.41. More and better chances projected.
England's top 3 starters are harder to replace (avg VORP 0.26) than DR Congo's (0.17). More star power in key positions.
England's starters play together at club level more often (0.049 cohesion) than DR Congo's (0.000). More shared understanding on the pitch.
🌍Match Conditions
England traveled 6,673km vs DR Congo's 11,749km. A shorter journey means less fatigue.
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.
La previsione
Match-outcome probability
- England win58.2%
- Draw30.3%
- DR Congo win11.5%
A clash of identities: England's balanced approach meets DR Congo's counter-attacker style in a fixture the model gives to England at 72%.
▸Gol e punteggi
Likeliest score 1–0 (20.9%) · xG 1.6 - 0.4
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–020.9%
- 2–017.2%
- 0–014.0%
- 1–19.3%
- 3–09.1%
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
- 0–037.0%
- 1–028.9%
- 2–011.7%
- 0–17.1%
- 1–16.3%
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 goals86.0%
- More than 1.5 goals60.1%
- More than 2.5 goals32.5%
- More than 3.5 goals14.4%
- More than 4.5 goals5.3%
- More than 5.5 goals1.7%
- Both teams score27.3%
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
- England clean sheetOpposing team scores zero66.4%
- DR Congo clean sheetOpposing team scores zero20.3%
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
- England by 4+5.9%
- England by 3+16.6%
- England by 2+37.8%
- England by 1+66.6%
- Draw24.9%
- DR Congo by 1+8.6%
- DR Congo by 2+1.6%
- DR Congo by 3+0.2%
- DR Congo by 4+<0.1%
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.
▸Come si sviluppa la partita
Over 2.5 goals 32.5% · BTTS 27.3%
Game state through the match
- England ahead67.1%
- Level23.8%
- DR Congo ahead9.1%
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
- 0–1528.4%
- 15–3020.3%
- 30–4514.6%
- 45–6010.4%
- 60–757.5%
- 75–905.3%
- No goal13.5%
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 → | HEngland win | DDraw | ADR Congo win |
|---|---|---|---|
| HEngland ahead | 44.8% | 2.6% | 0.3% |
| DLevel | 21.0% | 18.7% | 3.9% |
| ADR Congo ahead | 1.3% | 2.6% | 4.8% |
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
- England trail at HT, avoid defeat at FT3.9%
- DR Congo trail at HT, avoid defeat at FT2.9%
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.
- England42.7%
- DR Congo57.3%
- England54.0%
- DR Congo46.0%
- England31.1%
- DR Congo68.9%
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: England conv 68.6%, save 22.9%; DR Congo conv 73.3%, save 23.3%. Smoothed against the global prior with prior strength 20 — see /docs/methodology/.
▸Squadre e giocatori
Top scorer: Wissa (8.0%)
Match detail
England
Model-rated key players: Harry Kane (FW) — P(scores) 4.9%; Marcus Rashford (FW) — P(scores) 6.3%; Ollie Watkins (FW) — P(scores) 2.4%.
England under Thomas Tuchel play a balanced game, holding 55% of the ball — among the highest in the tournament field. Their likely shape is a 4-1-4-1, though they have also used 4-3-3. They apply moderate pressing intensity (PPDA 23.5) and build patiently through midfield with 8.5 passes per attacking sequence. They favour high-quality chances (xG/shot 0.142, among the best in the field).
England will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. Managing the fitness of Tino Livramento could prove decisive — their availability transforms the team's ceiling.
DR Congo
Model-rated key players: Yoane Wissa (FW) — P(scores) 8.0%; Cédric Bakambu (FW) — P(scores) 2.4%; Jackson Muleka (FW) — P(scores) 1.4%.
DR Congo under Sébastien Desabre play a counter attacker game with 46% possession. They apply moderate pressing intensity (PPDA 20.9) and move the ball forward quickly at 4.9 passes per attack. They favour high-quality chances (xG/shot 0.200, among the best in the field) and rely heavily on set pieces (20% of their xG).
DR Congo 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.
England's predicted XI averages 2,119 club minutes over the 2024-25 season (moderate load).
England coverage: 79.0% (11/11 XI matched against the FBref Big-5) · DR Congo: 58.0% (8/11).
England historically converts 15.2% of xG from set-pieces, contributing 0.24 expected set-piece goals in this fixture. DR Congo converts 20.5% from set-pieces (0.08 expected). Combined, the model expects 0.33 set-piece goals across the 90 minutes.
- P(England scores set-piece goal) 21.6%
- P(DR Congo scores set-piece goal) 8.1%
- P(set-piece goal in match) 27.9%
England: Trent Alexander-Arnold on corners (32 corners), Eberechi Eze on free kicks (per fbref 2022 23)
If a penalty is awarded to England, the model gives 68.6% conversion, 73.3% for DR Congo. If this match goes to a shootout, the symmetric (coin-toss averaged) win probability is 42.7% England / 57.3% DR Congo.
England primary PK: Marcus Rashford (6/8 in 2019-20, per fbref 2022 23) · DR Congo primary PK: Yoane Wissa (4/5 in 2020-21, 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
England
- Marc GuéhiCentre-backCover: Jarell Quansah · 0.650.32gap
- Jude BellinghamAttacking midfieldCover: Morgan Rogers · 0.720.27gap
- Marcus RashfordWingerCover: Anthony Gordon · 0.620.19gap
DR Congo
- Meschak EliaAttacking midfieldNo natural backup0.20gap
- Gaël KakutaAttacking midfieldNo natural backup0.16gap
- Yoane WissaStrikerCover: Simon Banza · 0.750.14gap
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 level320 m
- Avg temperatureFive-year mean over the tournament window25.7 °C
- Avg humidity73%
- Heat stressShade WBGT ~27.9 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf stadium converting to a temporary natural-grass pitch 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. Afternoon 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)
- Harry KaneFW4.9%
- Marcus RashfordPKFW6.3%
- Ollie WatkinsFW2.4%
- Yoane WissaPKFW8.0%
- Cédric BakambuFW2.4%
- Jackson MulekaFW1.4%
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
England
vs Panama · avg 6.4
DR Congo
vs Uzbekistan · avg 8.0
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.
9Harry Kane1'–1'Scored both crucial goals for England, including the winner, showcasing clinical finishing and leadership when his team needed it most.
2goals1shots1on target1headers▼
Scored both crucial goals for England, including the winner, showcasing clinical finishing and leadership when his team needed it most.
Match timeline
9Noni MaduekeScored two goals with clinical finishing, establishing himself as a key attacking threat for England.
2goals▼
Scored two goals with clinical finishing, establishing himself as a key attacking threat for England.
Match timeline
8Anthony GordonScored a goal, demonstrating good positioning and clinical finishing within the penalty area.
1goals▼
Scored a goal, demonstrating good positioning and clinical finishing within the penalty area.
Match timeline
8England GKMade two significant saves, showcasing strong reflexes and positioning to prevent further goals for the opposition.
Made two significant saves, showcasing strong reflexes and positioning to prevent further goals for the opposition.
7Jude BellinghamTested the opposition goalkeeper with a powerful shot, showcasing his offensive capabilities from midfield.
1shots1on target▼
Tested the opposition goalkeeper with a powerful shot, showcasing his offensive capabilities from midfield.
Match timeline
7Bukayo SakaForced a save from the opposition goalkeeper with a shot on target, contributing to England's offensive pressure.
1shots1on target▼
Forced a save from the opposition goalkeeper with a shot on target, contributing to England's offensive pressure.
Match timeline
6Marcus Rashford1'–1'Created several attacking opportunities with shots on goal but lacked the clinical finish to convert them.
2shots1on target▼
Created several attacking opportunities with shots on goal but lacked the clinical finish to convert them.
Match timeline
6Ezri KonsaShowed good attacking intent by hitting the post from inside the box, despite being a defender.
1shots▼
Showed good attacking intent by hitting the post from inside the box, despite being a defender.
Match timeline
9Congo DR GK #1Delivered an exceptional goalkeeping performance, making numerous crucial saves that kept his team competitive throughout the match.
Delivered an exceptional goalkeeping performance, making numerous crucial saves that kept his team competitive throughout the match.
8Sapanga0'–0'Scored a crucial opening goal for Congo DR with a clinical finish, giving his team an early lead.
1goals▼
Scored a crucial opening goal for Congo DR with a clinical finish, giving his team an early lead.
Match timeline
6Chancel MbembaMade an attacking effort that forced a save from the England goalkeeper, showing offensive intent from defense.
1shots1on target▼
Made an attacking effort that forced a save from the England goalkeeper, showing offensive intent from defense.
Match timeline
Match observations
- England then mounted sustained pressure, creating numerous chances that were repeatedly denied by an outstanding goalkeeping performance.
- Despite the resilience of Congo DR, England eventually found their rhythm and secured a late victory through their captain.
- The atmosphere in the stadium was electric, with fans reacting passionately to every significant moment.
▸Dietro le quinte
Model-by-model comparison
England vs DR Congo
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 78.4% | 21.6% | 0.0% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 66.9% | 24.4% | 8.6% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 67.3% | 23.4% | 9.3% |
| Bayesian stackingLearned-weight combination | — | 78.4% | 21.6% | 0.0% |
| Ensemble (published)Uniform average + isotonic calibration | — | 72.0% | 23.9% | 4.0% |
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 v Spain - who would England rather face in the World Cup final? · Daily Mirror — Football · 14 Jul
- England captain Harry Kane hits out at ITV interviewer Gabriel Clarke 'trying to create division' between Thomas Tuchel and Jude Bellingham with his questioning at the World Cup · Daily Mail — Football · 14 Jul
- England World Cup semi-final and final? A reminder of where and when! · Sky Sports — Football · 14 Jul
- England vs. Argentina ticket prices: How much do World Cup semifinal tickets cost? · USA Today · 14 Jul
- World Cup 2026: American Ismail Elfath to referee England v Argentina · BBC · 14 Jul
- Stage:
- Round of 32 · Match 8
- Date:
- 1 Jul
- Venue:
- Mercedes-Benz Stadium, Atlanta
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.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.
England
England: 1 carrying a fitness doubt.
- DoubtTino Livramento (defender) is carrying Knee injury — a depth-level fitness watch item.
DR Congo
DR Congo 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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