Group B · Matchday 3
Bosnia and HerzegovinavsQatar
2026-06-24·12:00 localPredictions finalised
The forecast
Match-outcome probability
- Bosnia and Herzegovina win45.4%
- Draw28.6%
- Qatar win26.1%
A clash of identities: Bosnia and Herzegovina's balanced approach meets Qatar's low-block style in a fixture the model gives to Bosnia and Herzegovina at 52%.
Why the model says this
Favoring Bosnia and Herzegovina
- ·Bosnia and Herzegovina holds a significant Elo rating advantage of 169 points over Qatar.
- ·The model projects Bosnia and Herzegovina to generate a higher expected goals (xG) total of 1.27 compared to Qatar's 0.99.
- ·Bosnia and Herzegovina's recent form includes 2 wins, 3 draws, and 1 loss in their last six matches, a stronger record than Qatar's.
- ·The ensemble model assigns Bosnia and Herzegovina a 45.3% probability of victory, substantially higher than Qatar's 26.1%.
Favoring Qatar
- ·Qatar has a positive head-to-head record against Bosnia and Herzegovina, with 1 win and 1 draw in 2 previous encounters.
- ·Qatar's only victory in this fixture was a 2-0 away win against Bosnia and Herzegovina in 2000.
What the model can't fully price
- ·The model does not fully adjust for squad availability, with 3 players across both squads carrying fitness doubts, including 1 projected starter.
- ·With no specific venue information provided, the model cannot factor in any potential home advantage for Bosnia and Herzegovina.
Form check
Bosnia and Herzegovina
SteadyBosnia and Herzegovina's recent form shows consistency, with 2 wins and 3 draws in their last six matches, including three consecutive 1-1 draws in World Cup qualifiers.
Three consecutive 1-1 draws in their last FIFA World Cup qualification matches.
Qatar
DecliningQatar's form has been declining, with 3 losses and 2 draws in their last six matches, including a 0-3 defeat in the Arab Cup.
Three losses in their last four matches across all competitions.
Analysis
How it plays out
Qatar defend deep and give Bosnia and Herzegovina the ball. The question is whether Bosnia and Herzegovina's balanced approach generates enough final-third creativity to break through.
What decides it
Qatar defend deep and limit space. Set pieces and individual errors become the most likely routes to goal. The scoring threat is evenly split: Ermin Bičakčić (4.5%) and Akram Afif (6.7%).
Off the pitch
No major off-pitch asymmetries. This one is decided by the football.
The angle
Likely the last World Cup for Edin Džeko. Tournament experience at this level is hard to quantify but hard to replace.
▸Goals & scorelines
Likeliest score 1–1 (13.6%) · xG 1.4 - 1.0
Expected goals
Mean of the Dixon-Coles joint goal distribution. Same fit that produces the most-likely-scoreline list below.
Most likely scorelines
- 1–113.6%
- 1–012.6%
- 0–010.6%
- 2–09.2%
- 2–18.8%
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
- 0–031.9%
- 1–020.8%
- 0–114.3%
- 1–110.8%
- 2–07.4%
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 goals89.4%
- More than 1.5 goals68.3%
- More than 2.5 goals41.1%
- More than 3.5 goals20.6%
- More than 4.5 goals8.7%
- More than 5.5 goals3.1%
- Both teams score46.7%
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
- Bosnia and Herzegovina clean sheetOpposing team scores zero38.6%
- Qatar clean sheetOpposing team scores zero25.4%
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+2.5%
- Bosnia and Herzegovina by 3+8.3%
- Bosnia and Herzegovina by 2+22.2%
- Bosnia and Herzegovina by 1+45.7%
- Draw29.0%
- Qatar by 1+25.3%
- Qatar by 2+9.2%
- Qatar by 3+2.5%
- Qatar by 4+0.5%
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 41.1% · BTTS 46.7%
Game state through the match
- Bosnia and Herzegovina ahead46.5%
- Level27.4%
- Qatar ahead26.1%
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
- 0–1532.1%
- 15–3021.8%
- 30–4514.8%
- 45–6010.0%
- 60–756.8%
- 75–904.6%
- No goal9.8%
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 | AQatar win |
|---|---|---|---|
| HBosnia and Herzegovina ahead | 29.1% | 4.6% | 1.2% |
| DLevel | 15.5% | 18.5% | 9.6% |
| AQatar ahead | 1.8% | 4.6% | 15.1% |
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 FT6.4%
- Qatar trail at HT, avoid defeat at FT5.8%
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.
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: Afif (6.7%)
Match detail
Bosnia and Herzegovina
Model-rated key players: Ermin Bičakčić (DF) — P(scores) 4.5%; Edin Džeko (FW) — P(scores) 4.4%; Ermedin Demirović (FW) — P(scores) 4.3%.
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.
Qatar
Model-rated key players: Akram Afif (FW) — P(scores) 6.7%; Almoez Ali (FW) — P(scores) 6.7%; Ismaeel Mohammad (FW) — P(scores) 6.7%.
Qatar under Julen Lopetegui play a low block game, with just 43% possession — among the lowest in the field. Their likely shape is a 5-3-2, though they have also used other. They sit deeper and pick their moments to press (PPDA 35.0). They are selective in their shooting (6.2 per 90).
Qatar will look to stay compact and frustrate opponents, limiting space and hitting on the break. Set-piece proficiency — both attacking and defending — becomes critical when open-play chances are limited by design. Managing minutes for Hassan Al-Haydos across what could be seven matches will test the coaching staff's rotation planning.
Bosnia and Herzegovina historically converts 8.1% of xG from set-pieces, contributing 0.11 expected set-piece goals in this fixture. Combined, the model expects 0.11 set-piece goals across the 90 minutes.
- P(Bosnia and Herzegovina scores set-piece goal) 10.5%
- P(set-piece goal in match) 10.5%
Bosnia and Herzegovina: Amer Gojak on corners (14 corners) (per fbref 2020 21) · Qatar: Guilherme on corners (14 corners) (per fbref 2017 18)
If a penalty is awarded to Bosnia and Herzegovina, the model gives 72.0% conversion, 72.0% for Qatar.
Bosnia and Herzegovina primary PK: Ermin Bičakčić (0/1 in 2013-14, 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.
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
- 35.0
- Possession
- 43%
- Directness (yds/pass)
- 5.9
- Long balls/90
- 38
- Set-piece xG
- —
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
Qatar
- Almoez AliStrikerCover: Ahmed Alaaeldin · 0.130.36gap
- Lucas MendesCentre-backCover: Al-Hashmi Al-Hussain · 0.020.26gap
- Meshaal BarshamGoalkeeperCover: Salah Zakaria · 0.300.17gap
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 level16 m
- Avg temperatureFive-year mean over the tournament window18.0 °C
- Avg humidity68%
- Heat stressShade WBGT ~19.6 °CLow heat stress
- Pitch surfacetemporary natural grass over artificial turf
Artificial-turf NFL stadium laying 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)
- Ermin BičakčićPKDF4.5%
- Edin DžekoFW4.4%
- Ermedin DemirovićFW4.3%
- Akram AfifFW6.7%
- Almoez AliFW6.7%
- Ismaeel MohammadFW6.7%
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.
Qatar
vs Canada · avg 2.0
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.
9Ermedin Demirović75'–81'Scored two crucial goals, including a penalty, but received a yellow card for excessive celebration.
2goals1 yellow▼
Scored two crucial goals, including a penalty, but received a yellow card for excessive celebration.
Match timeline
8Ivan Bašić12'–12'Scored the opening goal with a composed finish, setting the tone for his team's strong attacking performance.
1goals▼
Scored the opening goal with a composed finish, setting the tone for his team's strong attacking performance.
Match timeline
8Ivan Šunjić19'–45'Scored a spectacular long-range goal before being substituted at half-time, showcasing excellent striking ability.
1goals▼
Scored a spectacular long-range goal before being substituted at half-time, showcasing excellent striking ability.
Match timeline
8Nikola Katić25'–25'Scored a crucial header from a corner kick, demonstrating his aerial prowess and contributing to the team's attacking output.
1goals▼
Scored a crucial header from a corner kick, demonstrating his aerial prowess and contributing to the team's attacking output.
Match timeline
6T. TahirovićSubstituted in at half-time but had no specific notable actions recorded.
Substituted in at half-time but had no specific notable actions recorded.
10Akram Afif38'–90'Scored a hat-trick with clinical finishing, proving to be Qatar's most dangerous player and a constant threat on the counter-attack.
3goals▼
Scored a hat-trick with clinical finishing, proving to be Qatar's most dangerous player and a constant threat on the counter-attack.
Match timeline
8Hassan Al-Haydos42'–75'Scored a goal from close range, demonstrating good positioning and contributing to Qatar's first-half comeback.
1goals▼
Scored a goal from close range, demonstrating good positioning and contributing to Qatar's first-half comeback.
Match timeline
8Pedro Miguel47'–47'Scored a powerful goal from inside the box early in the second half, significantly boosting Qatar's comeback efforts.
1goals▼
Scored a powerful goal from inside the box early in the second half, significantly boosting Qatar's comeback efforts.
Match timeline
5Ahmed Fathy75'–75'Received a yellow card for a foul, which was his only notable action in the match.
1 yellow▼
Received a yellow card for a foul, which was his only notable action in the match.
Match timeline
4Mahmoud AbunadaConceded five goals despite an early save, indicating a challenging match for the goalkeeper.
Conceded five goals despite an early save, indicating a challenging match for the goalkeeper.
Match observations
- This video captures a training session for the Bosnia and Herzegovina national football team.
- The session begins with a comprehensive dynamic warm-up, featuring various stretches and jogging drills.
- Following the warm-up, the players transition into close-control ball work, emphasizing quick touches and coordination in small groups.
▸Under the hood
Model-by-model comparison
Bosnia and Herzegovina vs Qatar
| Model | Weight | Home | Draw | Away |
|---|---|---|---|---|
| EloRating-based strength estimate | 32% | 61.7% | 22.0% | 16.3% |
| Dixon-ColesGoal-process model with low-score correction | 63% | 45.8% | 28.4% | 25.8% |
| Hierarchical PoissonBayesian model with confederation pooling | 6% | 46.4% | 27.8% | 25.8% |
| Bayesian stackingLearned-weight combination | — | 54.0% | 27.6% | 18.4% |
| Ensemble (published)Uniform average + isotonic calibration | — | 52.4% | 25.7% | 21.9% |
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)44.7%
- + Lineup contribution0.0pp
- + Style-matchup contribution0.0pp
- Published P(Bosnia and Herzegovina win)44.7%
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 |
|---|---|---|---|---|---|
| 10 Aug 2010 | Friendly | HSarajevo | 1–1 | D | — |
| 24 Jan 2000 | Friendly | ADoha | 0–2 | L | — |
Bosnia and Herzegovina vs Qatar, 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 Qatar.
- Stage:
- Group B · Matchday 3
- Date:
- 24 Jun
Bosnia and Herzegovina
Bosnia and Herzegovina come in at close to full strength.
Qatar
Qatar come in at close to full strength.
Bosnia and Herzegovina and Qatar 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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