UPCOMING • 5/25/2026

Coritiba FBC

VS

EC Bahia

💡 Engine Insight

Model projects a balanced fixture with moderate goal expectancy. No extreme value detected across primary markets.

⚡ SpinBonus EV Engine™
BEST: Yes +1.3%
Match Result (1X2)
Home Win
Model: 32.5% • Implied: 32.9%
-1.1%
⚖️ FAIR
Draw
Model: 25.8% • Implied: 27.5%
-6.2%
— NO EDGE
Away Win
Model: 41.7% • Implied: 41.6%
+0.2%
⚖️ FAIR
📊 Over/Under 2.5
Over 2.5
Model: 50.3% • Implied: 49.8%
+1%
✅ VALUE
Under 2.5
Model: 49.7% • Implied: 49.2%
+1%
✅ VALUE
🎯 Both Teams to Score
Yes
Model: 54.3% • Implied: 53.6%
+1.3%
✅ VALUE
No
Model: 45.7% • Implied: 45.4%
+0.6%
⚖️ FAIR
💡 +EV Detected: Optimize Your Market Edge

Our model identified a +1.3% edge on Yes. To effectively capture this mathematical value without losing returns to bookmaker margins (vig), this transaction should strictly be executed on a low-commission exchange.

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🟢 HIGH CONFIDENCE72%
📈 9 matches analyzedStrong data foundation: 9 matches analyzed with basic metrics.

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📖 Match Preview & Analysis

The Campeonato Brasileiro Série A clash between Coritiba FBC and EC Bahia arrives with a clear statistical narrative. Our Poisson regression model projects EC Bahia as a slight edge, assigning them a 41.7% probability of winning, versus 32.5% for Coritiba FBC. A draw sits at 25.8%.

Recent form favors neither side clearly. Coritiba FBC has 2 wins and 1 losses in their last 5 fixtures, while EC Bahia records 2 wins and 2 losses over a similar span.

Model Insight: Model projects a balanced fixture with moderate goal expectancy. No extreme value detected across primary markets.

This match profiles as a tight contest. Double chance and BTTS markets often yield better Expected Value than 1X2 in matches with this probability spread.

Explore More
See where Coritiba FBC & EC Bahia stand in Campeonato Brasileiro Série A
Campeonato Brasileiro Série A Table →More Fixtures

💬 Match Discussion(0)

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🧮 How was this prediction calculated?

Win probabilities for Coritiba FBC vs EC Bahia are produced by a Poisson regression model using each side's attack and defence rates against league average. The expected goal counts (λ values) feed a 7×7 score matrix; we then sum the cells to get 1X2, Over/Under and BTTS probabilities. Expected Value is calculated by comparing model probability to the bookmaker implied probability after stripping the overround.

# Poisson per-team expected goals
λ_home = avg_home_goals × home_attack × away_defence
λ_away = avg_away_goals × away_attack × home_defence
# Probability of exact score (h, a)
P(h, a) = (e^-λ_home · λ_home^h / h!) · (e^-λ_away · λ_away^a / a!)
# Expected Value
EV % = (model_probability × decimal_odds − 1) × 100
Confidence
HIGH · 72%
Matches Analysed
9
xG Trend
1.20
H2H Avg Goals
2.5
📊
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