Belgium vs Senegal
Group X · Wednesday, July 1, 2026, 20:00 UTC · Seattle
The model's read
Belgium hold a narrow edge at 37%, with Senegal at 27% and 36% for the draw. Goals project around 1.7–1.4 in an open game (both teams to score 59%, over 2.5 58%). The biggest single factor is Injuries & suspensions, favouring Senegal. Confidence sits at 47/100 but upset risk is high — variance and form keep the underdog live.
Auto-updated every hour as ratings, odds and news change.
Match outcome
oracle-v1.0.0
Belgium win
36.7%
Draw
36.3%
Senegal win
27.0%
Expected goals
1.66 – 1.37
Scoreline cluster
1-0 / 2-1 / 1-1
top exact 1-0 · 10.0%
Confidence
47/100
Result lean
Draw-leaning
Score band
Open 1-1 / 2-1 type game
Goal environment
Open
If it opens up — high-scoring alternative
2-2 at 6.3% — most likely scoreline with 3+ goals total.
no exact score is dominant; BTTS 59% and Over 2.5 58% both elevated; top three scorelines within 2 pp
Risk & uncertainty
Upset risk
49/100
Data quality
85/100
Uncertainty band
31 – 62%
Most likely scorelines
Top 12 from a Dixon-Coles adjusted Poisson matrix.
Top 12 most likely scorelines. Heat is relative to the most likely outcome. Belgium vs Senegal.
Team comparison
Rating profile across the key dimensions.
Ensemble breakdown
Final probabilities blend the technical model with every available source layer. Persisted 2026-07-11 02:02:38 UTC.
Final (blended) home
36.7%
Final draw
36.3%
Final away
27.0%
Source weights applied
- technical38.5%
- learned16.1%
- market19.3%
- crowd7.5%
- external0.0%
- intel8.6%
- fundamental7.0%
- sentimentMomentum3.0%
Source presence
- technical1 signal
- learned1 signal
- market46 signals
- crowd1 signal
- externalnot used
- intel77 signals
- fundamental33 signals
Why this prediction
High-coverage prediction — most signal classes are present and aligned.
Technical
73/100
Fundamentals
84/100
Betting
80/100
Prediction market
77/100
Sentiment
0/100
Top positive drivers
- · crowd signal favours home (34.8 pp above uniform)
- · technical signal favours home (13.0 pp above uniform)
- · learned signal favours home (5.2 pp above uniform)
Top risk drivers
- · Source conflict: market vs crowd disagree (conflict score 45)
- · market signal is against home (-13.9 pp below uniform)
- · Top outcome only 36.7% — very open match
Missing: external prediction sites
Learned model
The learned model contributes to the public ensemble blend, alongside ratings, market signals and news intelligence. Performance is validated against an Elo baseline.
Belgium win
38.6%
Draw
43.2%
Senegal win
18.2%
Total goals (λ)
2.50
Model confidence
30/100
Logistic regression model — version wdl-v1, trained on 19k historical internationals.
Article intelligence
Recent news, injury alerts and sentiment extracted by the NLP layer.
Belgium
50 articles / 14d
Injury alerts
· Belgium (out — out for several weeks)
· Amadou Onana (out — ruled out)
· World Cup (out — sidelined)
Senegal
27 articles / 14d
Goals markets
BTTS yes
58.5%
Over 1.5
78.6%
Over 2.5
58.5%
Over 3.5
36.1%
Under 2.5
41.5%
Market intelligence
Model probability
36.7%
Market probability
19.4%
Gap: +26.9 percentage points.
Market confidence: 0/100 across 46 bookmakers.
Odds shown for analysis only — CupCastLab does not place bets, list bookmakers, or recommend wagers.
Explanation
Why the model sees the match this way.
Model prediction: Belgium 46% to win, 34% for Senegal, 20% draw. Expected goals: Belgium 1.66 vs Senegal 1.37. Scoreline cluster: 1-0 / 2-1 / 1-1. Top exact 1-0 only 10.0%. Goal environment open (BTTS 59%, Over 2.5 58%). Open 1-1 / 2-1 type game. Primary factor: injuries & suspensions difference is the dominant driver. Confidence: 47/100. This is a low-confidence call — uncertainty is wide.
Factor contributions
Risk factors
- Belgium carry injury concerns into the match.
Uncertainty factors
- No major uncertainty flags.
Tactical read
No standout tactical mismatch — the stylistic edge is modest.
Upset risk
High upset risk — variance, tactical fit, or recent form suggests the underdog is live.
Market divergence
Notable gap: the model believes the implied probability is higher than the market suggests. This can reflect data the market is under-pricing — or model over-confidence.
These predictions are probabilistic and not guarantees. Not betting advice.