UPCOMING • 5/16/2026

FC St. Pauli 1910

VS

VfL Wolfsburg

💡 Engine Insight

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

⚡ SpinBonus EV Engine™
BEST: Under 2.5 +1.4%
Match Result (1X2)
Home Win
Model: 24.1% • Implied: 24.9%
-3.2%
— NO EDGE
Draw
Model: 26.0% • Implied: 27.7%
-6.1%
— NO EDGE
Away Win
Model: 49.9% • Implied: 49.4%
+1%
✅ VALUE
📊 Over/Under 2.5
Over 2.5
Model: 44.8% • Implied: 44.6%
+0.5%
⚖️ FAIR
Under 2.5
Model: 55.2% • Implied: 54.4%
+1.4%
✅ VALUE
🎯 Both Teams to Score
Yes
Model: 48.1% • Implied: 47.7%
+0.8%
⚖️ FAIR
No
Model: 51.9% • Implied: 51.3%
+1.2%
✅ VALUE
💡 +EV Detected: Optimize Your Market Edge

Our model identified a +1.4% edge on Under 2.5. 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 CONFIDENCE85%
📈 5 matches analyzedStrong data foundation: 5 matches analyzed with xG metrics.

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📖 Match Preview & Analysis

The Bundesliga clash between FC St. Pauli 1910 and VfL Wolfsburg arrives with a clear statistical narrative. Our Poisson regression model projects VfL Wolfsburg as a moderate favorite, assigning them a 49.9% probability of winning, versus 24.1% for FC St. Pauli 1910. A draw sits at 26.0%.

Recent form favors FC St. Pauli 1910. FC St. Pauli 1910 has 2 wins and 2 losses in their last 5 fixtures, while VfL Wolfsburg records 1 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 FC St. Pauli 1910 & VfL Wolfsburg stand in Bundesliga
Bundesliga Table →More Fixtures

💬 Match Discussion(0)

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🧮 How was this prediction calculated?

Win probabilities for FC St. Pauli 1910 vs VfL Wolfsburg 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 · 85%
Matches Analysed
5
xG Trend
1.00
H2H Avg Goals
2.5
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