UPCOMING • 5/16/2026

1. FC Union Berlin

VS

FC Augsburg

💡 Engine Insight

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

⚡ SpinBonus EV Engine™
BEST: Over 2.5 +1.4%
Match Result (1X2)
Home Win
Model: 25.1% • Implied: 25.8%
-2.9%
— NO EDGE
Draw
Model: 23.8% • Implied: 25.6%
-7.1%
— NO EDGE
Away Win
Model: 51.2% • Implied: 50.6%
+1.1%
✅ VALUE
📊 Over/Under 2.5
Over 2.5
Model: 54.6% • Implied: 53.9%
+1.4%
✅ VALUE
Under 2.5
Model: 45.4% • Implied: 45.1%
+0.6%
⚖️ FAIR
🎯 Both Teams to Score
Yes
Model: 55.9% • Implied: 55.1%
+1.4%
✅ VALUE
No
Model: 44.1% • Implied: 43.9%
+0.5%
⚖️ FAIR
💡 +EV Detected: Optimize Your Market Edge

Our model identified a +1.4% edge on Over 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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🔴 LOW CONFIDENCE33%
📈 6 matches analyzedLimited data available: only 6 matches. Projections carry higher uncertainty.

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📖 Match Preview & Analysis

The Bundesliga clash between 1. FC Union Berlin and FC Augsburg arrives with a clear statistical narrative. Our Poisson regression model projects FC Augsburg as a moderate favorite, assigning them a 51.2% probability of winning, versus 25.1% for 1. FC Union Berlin. A draw sits at 23.8%.

Recent form favors neither side clearly. 1. FC Union Berlin has 2 wins and 1 losses in their last 5 fixtures, while FC Augsburg 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 1. FC Union Berlin & FC Augsburg stand in Bundesliga
Bundesliga Table →More Fixtures

💬 Match Discussion(0)

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🧮 How was this prediction calculated?

Win probabilities for 1. FC Union Berlin vs FC Augsburg 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
LOW · 33%
Matches Analysed
6
xG Trend
1.10
H2H Avg Goals
2.7
📊
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