Group B · Matchday 2
Bosnia and HerzegovinavsSwitzerland
2026-06-18·12:00 localPredictions finalised
The forecast
Match-outcome probability
- Bosnia and Herzegovina win9.9%
- Draw21.8%
- Switzerland win68.3%
A 295-point Elo gap frames this as a significant mismatch, yet the model still gives Bosnia and Herzegovina a 7% probability of a result — enough to make this more than a formality.
Why the model says this
Favoring Bosnia and Herzegovina
- ·Bosnia and Herzegovina won their only previous head-to-head encounter against Switzerland, a 2-0 victory in 2016.
- ·Bosnia and Herzegovina are undefeated in their last 6 matches, recording 2 wins and 4 draws.
Favoring Switzerland
- ·Switzerland holds a significant Elo rating advantage of 295 points over Bosnia and Herzegovina.
- ·The model's expected goals (xG) project Switzerland to score 1.83 goals compared to Bosnia and Herzegovina's 0.7 goals.
- ·Switzerland is ranked 17th in the FIFA World Rankings.
- ·Individual model components like Elo (73.5%), DC (63.7%), HP (62.8%), and Stacking (68.2%) all project a higher probability for a Switzerland win than the ensemble's 59.3%.
What the model can't fully price
- ·The model does not account for squad availability, with 4 players across both squads carrying a fitness doubt, including 1 projected starter.
- ·Bosnia and Herzegovina have an additional day of rest (6 days) compared to Switzerland (5 days) since their last match, a factor not included in the model's inputs.
- ·The 'electric atmosphere' noted in video analysis, potentially influencing team motivation or home advantage, is not a quantifiable input for the model.
Form check
Bosnia and Herzegovina
SteadyBosnia and Herzegovina enter this match on an undefeated run of six games, securing two wins and four draws. They have consistently found the net, scoring 12 goals across these fixtures, while conceding 7.
Undefeated in their last 6 matches (2 wins, 4 draws).
Switzerland
DecliningSwitzerland's recent form shows two wins, three draws, and one loss in their last six outings. Their most recent fixture was a 3-4 defeat in a friendly, which ended a five-match unbeaten streak.
Conceded 4 goals in their most recent friendly match.
Analysis
How it plays out
Neither side has a rigid tactical identity. Both adapt to the opponent, so the first 15 minutes will reveal who imposes their plan first.
What decides it
Switzerland adjust shape to the opponent. That flexibility is an asset, but it takes longer to settle into a game. The scoring threat is evenly split: Ermin Bičakčić (4.5%) and Ricardo Rodriguez (7.0%).
Off the pitch
Murat Yakin (5 years in charge of Switzerland) vs Sergej Barbarez (2 years). That tenure gap shows up in squad familiarity and set-piece coordination.
The angle
The model gives Bosnia and Herzegovina just 14.3% to win. Every World Cup produces group-stage upsets; the question is whether this fixture is one of them.
▸Goals & scorelines
Likeliest score 0–1 (15.0%) · xG 0.6 - 1.8
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 0–115.0%
- 0–214.3%
- 1–110.5%
- 0–09.1%
- 1–29.0%
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
- 0–029.7%
- 0–126.2%
- 0–212.2%
- 1–19.0%
- 1–08.7%
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 goals71.1%
- More than 2.5 goals44.6%
- More than 3.5 goals23.4%
- More than 4.5 goals10.4%
- More than 5.5 goals4.0%
- Both teams score39.9%
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
- Bosnia and Herzegovina clean sheetOpposing team scores zero16.1%
- Switzerland clean sheetOpposing team scores zero53.2%
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
- Bosnia and Herzegovina by 4+0.1%
- Bosnia and Herzegovina by 3+0.6%
- Bosnia and Herzegovina by 2+3.0%
- Bosnia and Herzegovina by 1+11.6%
- Draw22.9%
- Switzerland by 1+65.6%
- Switzerland by 2+39.6%
- Switzerland by 3+19.0%
- Switzerland by 4+7.4%
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 44.6% · BTTS 39.9%
Game state through the match
- Bosnia and Herzegovina ahead12.2%
- Level21.6%
- Switzerland ahead66.2%
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
- 0–1533.7%
- 15–3022.3%
- 30–4514.8%
- 45–609.8%
- 60–756.5%
- 75–904.3%
- No goal8.5%
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 → | HBosnia and Herzegovina win | DDraw | ASwitzerland win |
|---|---|---|---|
| HBosnia and Herzegovina ahead | 6.6% | 3.3% | 2.0% |
| DLevel | 4.9% | 15.0% | 19.4% |
| ASwitzerland ahead | 0.6% | 3.4% | 44.8% |
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
- Bosnia and Herzegovina trail at HT, avoid defeat at FT4.0%
- Switzerland trail at HT, avoid defeat at FT5.3%
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: Rodriguez (7.0%)
Match detail
Bosnia and Herzegovina
Model-rated key players: Ermin Bičakčić (DF) — P(scores) 4.5%; Edin Džeko (FW) — P(scores) 4.1%; Ermedin Demirović (FW) — P(scores) 4.1%.
Limited recent tournament data is available for Bosnia and Herzegovina's tactical profile. Early indicators suggest a balanced approach.
Bosnia and Herzegovina will need to leverage their strengths while managing the physical demands of a tournament spread across three host countries. Managing minutes for Edin Džeko across what could be seven matches will test the coaching staff's rotation planning.
Switzerland
Model-rated key players: Ricardo Rodriguez (DF) — P(scores) 7.0%; Breel Embolo (FW) — P(scores) 6.4%; Zeki Amdouni (FW) — P(scores) 3.6%.
Switzerland under Murat Yakin play a pragmatic game with 50% possession. Their likely shape is a 4-2-3-1, though they have also used 4-3-3. They apply moderate pressing intensity (PPDA 22.8).
Switzerland play a pragmatic, results-oriented game that adapts shape to the opposition. Tactical flexibility is their strength. The risk is inconsistency — without a default identity, a poor result can cascade if the team struggles to find a Plan B. Managing minutes for Remo Freuler across what could be seven matches will test the coaching staff's rotation planning.
Switzerland's predicted XI averages 1,993 club minutes over the 2024-25 season (moderate load).
Bosnia and Herzegovina coverage: 24.0% (4/11 XI matched against the FBref Big-5) · Switzerland: 76.0% (11/11).
Bosnia and Herzegovina historically converts 8.1% of xG from set-pieces, contributing 0.05 expected set-piece goals in this fixture. Switzerland converts 10.3% from set-pieces (0.19 expected). Combined, the model expects 0.24 set-piece goals across the 90 minutes.
- P(Bosnia and Herzegovina scores set-piece goal) 5.0%
- P(Switzerland scores set-piece goal) 17.2%
- P(set-piece goal in match) 21.3%
Bosnia and Herzegovina: Amer Gojak on corners (14 corners) (per fbref 2020 21) · Switzerland: Granit Xhaka on free kicks (per fbref 2022 23)
If a penalty is awarded to Bosnia and Herzegovina, the model gives 72.0% conversion, 71.4% for Switzerland.
Bosnia and Herzegovina primary PK: Ermin Bičakčić (0/1 in 2013-14, per fbref 2020 21) · Switzerland primary PK: Ricardo Rodriguez (1/2 in 2017-18, 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
Partial coverage from FotMob match stats (recent qualifiers and friendlies): possession and shot volume only. Press and build-up metrics are not available for this side.
- PPDA
- —
- Possession
- 49%
- Directness (yds/pass)
- —
- Long balls/90
- —
- Set-piece xG
- 8%
- PPDA
- 22.8
- Possession
- 50%
- Directness (yds/pass)
- 5.4
- Long balls/90
- 34
- Set-piece xG
- 10%
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
Bosnia and Herzegovina
- Amar DedićFull-backCover: Arjan Malić · 0.270.56gap
- Ermedin DemirovićStrikerCover: Haris Tabaković · 0.360.47gap
- Edin DžekoStrikerCover: Haris Tabaković · 0.360.33gap
Switzerland
- Dan NdoyeWingerCover: Noah Okafor · 0.000.53gap
- Manuel AkanjiCentre-backCover: Aurèle Amenda · 0.360.53gap
- Nico ElvediCentre-backCover: Aurèle Amenda · 0.360.51gap
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 level26 m
- Avg temperatureFive-year mean over the tournament window20.8 °C
- Avg humidity70%
- Heat stressShade WBGT ~22.5 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Indoor artificial-turf stadium; natural grass is grown on a drainage-tray system over the turf under the translucent roof.
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)
- Ermin BičakčićPKDF4.5%
- Edin DžekoFW4.1%
- Ermedin DemirovićFW4.1%
- Ricardo RodriguezPKDF7.0%
- Breel EmboloFW6.4%
- Zeki AmdouniFW3.6%
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
Bosnia and Herzegovina
vs United States · avg 6.5
Worked well: Their goalkeeper made some good saves, and the defence managed to clear several dangerous situations, including a corner kick.
Struggled: The team found it difficult to transition into attack and create meaningful scoring opportunities, with only one notable shot on target shown in the highlights.
Switzerland
vs Algeria · avg 7.6
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.
7Steven ZuberZuber was an energetic presence on the wing, actively involved in both attacking movements and defensive pressing for Switzerland.
Zuber was an energetic presence on the wing, actively involved in both attacking movements and defensive pressing for Switzerland.
Match observations
- The match began with an electric atmosphere, with both sets of supporters creating a vibrant backdrop.
- Switzerland demonstrated composure in possession, building attacks from midfield.
- Bosnia and Herzegovina adopted a compact defensive approach, showing discipline to limit clear opportunities for Switzerland.
▸Under the hood
Model-by-model comparison
Bosnia and Herzegovina vs Switzerland
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 3.5% | 22.0% | 74.5% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 11.5% | 22.2% | 66.2% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 12.8% | 22.3% | 64.9% |
| Bayesian stackingLearned-weight combination | — | 3.3% | 17.7% | 79.0% |
| Ensemble (published)Uniform average + isotonic calibration | — | 6.6% | 22.6% | 70.8% |
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(Bosnia and Herzegovina win)9.9%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Bosnia and Herzegovina win)9.9%
Decomposition of the published P(Bosnia and Herzegovina 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 |
|---|---|---|---|---|---|
| 18 Jun 2026 | FIFA World Cup | NInglewood | 1–4 | L | — |
| 29 Mar 2016 | Friendly | AZürich | 2–0 | W | — |
Bosnia and Herzegovina vs Switzerland, every senior international meeting in the martj42 results dataset (score from Bosnia and Herzegovina's perspective; H/A/N = home/away/neutral).
Latest news & match context
No recent headlines for Bosnia and Herzegovina or Switzerland.
- Stage:
- Group B · Matchday 2
- Date:
- 18 Jun
Ranked by likely importance. None of these feed the forecast: the probabilities rest on team strength, venue conditions and the style matchup.
- 1.Rest differential: Bosnia and Herzegovina have had 6 days since their previous match versus 5 for Switzerland. Rest and recovery are not model inputs.
Bosnia and Herzegovina
Bosnia and Herzegovina come in at close to full strength.
Switzerland
Switzerland come in at close to full strength.
Bosnia and Herzegovina and Switzerland 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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