Group E · Matchday 2
GermanyvsIvory Coast
2026-06-20·16:00 localPredictions finalised
The forecast
Match-outcome probability
- Germany win61.3%
- Draw23.8%
- Ivory Coast win14.9%
A 247-point Elo gap frames this as a significant mismatch, yet the model still gives Ivory Coast a 12% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Germany
- ·Germany holds a significantly higher FIFA ranking at 9th globally, compared to Ivory Coast's 42nd position.
- ·The Elo rating system identifies Germany as the favoured side with a 247-point advantage over Ivory Coast.
- ·Germany's expected goals (xG) output of 1.73 is more than double Ivory Coast's 0.81.
- ·Germany has won all six of their most recent matches, scoring 19 goals and conceding 4.
Favoring Ivory Coast
- ·The only previous head-to-head encounter between these two nations resulted in a 2-2 draw.
- ·Ivory Coast has secured four wins, one draw, and one loss in their last six fixtures, demonstrating competitive form.
- ·Ivory Coast exhibits a high pressing intensity, with a PPDA of 13.7, placing them in the 96.2 percentile for pressing.
What the model can't fully price
- ·Three players across both squads are carrying fitness doubts (Germany 2, Ivory Coast 1), a factor not adjusted for by the model's current lineup channel.
- ·Video analysis noted two disallowed goals for Germany due to fouls and a generally physical contest, indicating specific in-game dynamics and refereeing interpretations that pre-match models do not fully capture.
Form check
Germany
ImprovingGermany enters this match in excellent form, having secured six consecutive victories across World Cup qualifiers and friendlies. During this run, they have scored 19 goals while conceding only 4.
6 consecutive wins
Ivory Coast
SteadyIvory Coast's recent form shows resilience, with four wins, one draw, and one loss in their last six fixtures. This run includes two clean sheet victories in recent friendlies, with their only defeat being a narrow 2-3 loss in the African Cup of Nations.
4 wins in last 6 matches
Analysis
How it plays out
Both sides run a possession dominant system, so this becomes a test of who executes the same ideas better on the day. Ivory Coast's aggressive press (PPDA 13.7) against Germany's deeper build-up (PPDA 17.8) creates a clear territory question: can Ivory Coast force errors high up, or will Germany play through the press and find space behind it?
What decides it
Both sides run the same system (possession dominant), so execution quality separates them, not tactical asymmetry. The scoring threat is evenly split: Niclas Füllkrug (6.2%) and Franck Kessié (8.3%).
Off the pitch
No major off-pitch asymmetries. This one is decided by the football.
The angle
The model's 29th-ranked side against the 6thth. Group stages reward the underdog who executes a clear plan.
▸Goals & scorelines
Likeliest score 1–0 (12.7%) · xG 1.7 - 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
- 1–012.7%
- 1–112.0%
- 2–011.3%
- 2–19.6%
- 0–08.6%
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
- 0–028.8%
- 1–023.2%
- 0–111.3%
- 1–110.7%
- 2–010.0%
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 goals91.4%
- More than 1.5 goals72.6%
- More than 2.5 goals46.4%
- More than 3.5 goals24.9%
- More than 4.5 goals11.3%
- More than 5.5 goals4.4%
- Both teams score47.2%
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
- Germany clean sheetOpposing team scores zero43.0%
- Ivory Coast clean sheetOpposing team scores zero18.5%
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
- Germany by 4+5.1%
- Germany by 3+14.1%
- Germany by 2+31.8%
- Germany by 1+56.7%
- Draw25.4%
- Ivory Coast by 1+17.9%
- Ivory Coast by 2+5.8%
- Ivory Coast by 3+1.4%
- Ivory Coast by 4+0.3%
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 46.4% · BTTS 47.2%
Game state through the match
- Germany ahead57.4%
- Level24.0%
- Ivory Coast ahead18.6%
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
- 0–1534.4%
- 15–3022.6%
- 30–4514.8%
- 45–609.7%
- 60–756.4%
- 75–904.2%
- No goal8.0%
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 → | HGermany win | DDraw | AIvory Coast win |
|---|---|---|---|
| HGermany ahead | 37.6% | 4.3% | 0.9% |
| DLevel | 17.5% | 15.8% | 7.0% |
| AIvory Coast ahead | 2.2% | 4.2% | 10.5% |
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
- Germany trail at HT, avoid defeat at FT6.3%
- Ivory Coast trail at HT, avoid defeat at FT5.2%
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: Kessié (8.3%)
Match detail
Germany
Model-rated key players: Niclas Füllkrug (FW) — P(scores) 6.2%; Leroy Sané (FW) — P(scores) 3.8%; Kai Havertz (FW) — P(scores) 3.7%.
Germany under Julian Nagelsmann play a possession dominant game, holding 64% of the ball — among the highest in the tournament field. Their likely shape is a 4-2-3-1. They apply moderate pressing intensity (PPDA 17.8) and build patiently through midfield with 8.6 passes per attacking sequence. They generate a high volume of shots (17.4 per 90).
To succeed, Germany must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match. Managing the fitness of Florian Wirtz could prove decisive — their availability transforms the team's ceiling.
Ivory Coast
Model-rated key players: Franck Kessié (MF) — P(scores) 8.3%; Simon Adingra (FW) — P(scores) 2.5%; Jérémie Boga (FW) — P(scores) 2.3%.
Ivory Coast under Emerse Faé play a possession dominant game, holding 58% of the ball — among the highest in the tournament field. They press intensely (PPDA 13.7, 2nd in the field).
To succeed, Ivory Coast must control tempo and territory in midfield — their possession-dominant approach depends on dictating the rhythm of each match.
Germany's predicted XI averages 2,067 club minutes over the 2024-25 season (moderate load). Ivory Coast's predicted XI averages 1,658 club minutes over the 2024-25 season (light load).
Germany coverage: 88.0% (10/11 XI matched against the FBref Big-5) · Ivory Coast: 60.0% (7/11).
Germany historically converts 14.8% of xG from set-pieces, contributing 0.25 expected set-piece goals in this fixture. Ivory Coast converts 16.5% from set-pieces (0.14 expected). Combined, the model expects 0.39 set-piece goals across the 90 minutes.
- P(Germany scores set-piece goal) 22.0%
- P(Ivory Coast scores set-piece goal) 13.1%
- P(set-piece goal in match) 32.2%
Germany: Joshua Kimmich on corners (62 corners) (per fbref 2022 23) · Ivory Coast: Nicolas Pépé on corners (13 corners), Ibrahim Sangaré on free kicks (per fbref 2022 23)
If a penalty is awarded to Germany, the model gives 78.2% conversion, 73.3% for Ivory Coast.
Germany primary PK: Niclas Füllkrug (3/3 in 2022-23, per fbref 2022 23) · Ivory Coast primary PK: Franck Kessié (2/3 in 2021-22, per fbref 2022 23).
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
- 17.8
- Possession
- 64%
- Directness (yds/pass)
- 4.4
- Long balls/90
- 28
- Set-piece xG
- 15%
- PPDA
- 13.7
- Possession
- 58%
- Directness (yds/pass)
- 6.4
- Long balls/90
- 34
- Set-piece xG
- 17%
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
Germany
- Deniz UndavStrikerCover: Maximilian Beier · 0.680.25gap
- Leroy SanéWingerCover: Jamie Leweling · 0.730.18gap
- Kai HavertzStrikerCover: Maximilian Beier · 0.680.17gap
Ivory Coast
- Oumar DiakitéStrikerCover: Elye Wahi · 0.000.67gap
- Ibrahim SangaréDefensive midfieldNo natural backup0.30gap
- Ousmane DiomandeCentre-backCover: Emmanuel Agbadou · 0.730.23gap
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 level78 m
- Avg temperatureFive-year mean over the tournament window21.2 °C
- Avg humidity71%
- Heat stressShade WBGT ~22.9 °CLow heat stress
- Pitch surfacenatural grass
Natural-grass football stadium.
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)
- Niclas FüllkrugPKFW6.2%
- Leroy SanéFW3.8%
- Kai HavertzFW3.7%
- Franck KessiéPKMF8.3%
- Simon AdingraFW2.5%
- Jérémie BogaFW2.3%
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
Germany
vs Paraguay · avg 5.5
Worked well: Their persistence led to an equalizer, and they generated several dangerous opportunities from crosses and corners.
Struggled: They struggled to convert their attacking pressure into clear goals, missing several chances and having one disallowed. A crucial penalty miss proved costly.
Ivory Coast
vs Norway · avg 7.5
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.
9Deniz Undav121'–142'Came off the bench to score two decisive goals, including the equalizer and winner, completely turning the match around for Germany.
2goals1headers▼
Came off the bench to score two decisive goals, including the equalizer and winner, completely turning the match around for Germany.
Match timeline
7Kai Havertz48'–48'Despite two disallowed goals, he was a constant threat in the German attack, demonstrating good movement and finishing instincts.
▼
Despite two disallowed goals, he was a constant threat in the German attack, demonstrating good movement and finishing instincts.
Match timeline
7Florian WirtzA creative force for Germany, he used his technical ability, dribbling, and vision to unlock the opposition defense.
A creative force for Germany, he used his technical ability, dribbling, and vision to unlock the opposition defense.
7Leroy Sané56'–56'Displayed excellent close control and dribbling, creating opportunities and contributing to Germany's attacking phases.
▼
Displayed excellent close control and dribbling, creating opportunities and contributing to Germany's attacking phases.
Match timeline
7Yahia Fofana7'–21'Made crucial saves and commanded his box well, despite a foul call and ultimately conceding two goals.
1saves1fouls▼
Made crucial saves and commanded his box well, despite a foul call and ultimately conceding two goals.
Match timeline
6Odilon Kossounou48'–48'Was involved in a crucial defensive moment where a foul on him led to a German goal being disallowed.
1fouls won▼
Was involved in a crucial defensive moment where a foul on him led to a German goal being disallowed.
Match timeline
Match observations
- The match began with Germany showing early attacking intent, but Ivory Coast's goalkeeper Fofana was in fine form.
- Ivory Coast took a surprising lead, but Germany responded with sustained pressure and several attacking movements.
- The game featured two disallowed goals for Germany, both due to fouls, highlighting the physical nature of the contest.
▸Under the hood
Model-by-model comparison
Germany vs Ivory Coast
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 65.8% | 22.0% | 12.2% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 57.1% | 25.2% | 17.7% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 57.3% | 24.4% | 18.3% |
| Bayesian stackingLearned-weight combination | — | 66.7% | 23.7% | 9.6% |
| Ensemble (published)Uniform average + isotonic calibration | — | 63.9% | 23.8% | 12.3% |
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(Germany win)61.3%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Germany win)61.3%
Decomposition of the published P(Germany 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 |
|---|---|---|---|---|---|
| 20 Jun 2026 | FIFA World Cup | NToronto | 2–1 | W | — |
| 18 Nov 2009 | Friendly | HGelsenkirchen | 2–2 | D | — |
Germany vs Ivory Coast, every senior international meeting in the martj42 results dataset (score from Germany's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for Germany or Ivory Coast.
- Stage:
- Group E · Matchday 2
- Date:
- 20 Jun
Germany
Germany come in at close to full strength.
Ivory Coast
Ivory Coast come in at close to full strength.
Germany and Ivory Coast 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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