UPCOMING • 5/16/2026

Eintracht Frankfurt

VS

VfB Stuttgart

💡 Engine Insight

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

DRAW RISK
⚡ SpinBonus EV Engine™
BEST: Under 2.5 +2%
Match Result (1X2)
Home Win
Model: 22.9% • Implied: 23.8%
-3.6%
— NO EDGE
Draw
Model: 28.7% • Implied: 30.3%
-5.2%
— NO EDGE
Away Win
Model: 48.4% • Implied: 48.0%
+0.9%
⚖️ FAIR
📊 Over/Under 2.5
Over 2.5
Model: 35.1% • Implied: 35.3%
-0.7%
⚖️ FAIR
Under 2.5
Model: 64.9% • Implied: 63.7%
+2%
✅ VALUE
🎯 Both Teams to Score
Yes
Model: 40.1% • Implied: 40.1%
0%
⚖️ FAIR
No
Model: 59.9% • Implied: 58.9%
+1.7%
✅ VALUE
💡 +EV Detected: Optimize Your Market Edge

Our model identified a +2% 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 CONFIDENCE82%
📈 4 matches analyzedStrong data foundation: 4 matches analyzed with xG metrics.

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📖 Match Preview & Analysis

The Bundesliga clash between Eintracht Frankfurt and VfB Stuttgart arrives with a clear statistical narrative. Our Poisson regression model projects VfB Stuttgart as a moderate favorite, assigning them a 48.4% probability of winning, versus 22.9% for Eintracht Frankfurt. A draw sits at 28.7%.

Recent form favors VfB Stuttgart. Eintracht Frankfurt has 1 wins and 2 losses in their last 5 fixtures, while VfB Stuttgart records 2 wins and 1 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 Eintracht Frankfurt & VfB Stuttgart stand in Bundesliga
Bundesliga Table →More Fixtures

💬 Match Discussion(0)

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🧮 How was this prediction calculated?

Win probabilities for Eintracht Frankfurt vs VfB Stuttgart 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 · 82%
Matches Analysed
4
xG Trend
0.80
H2H Avg Goals
2.3
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