UPCOMING • 5/16/2026

SC Freiburg

VS

RB Leipzig

💡 Engine Insight

Defensive metrics dominate this fixture. Under 2.5 goals projects as the highest-probability outcome.

LOW SCORING POTENTIALDRAW RISK
⚡ SpinBonus EV Engine™
BEST: Under 2.5 +2.4%
Match Result (1X2)
Home Win
Model: 21.5% • Implied: 22.4%
-4.1%
— NO EDGE
Draw
Model: 32.0% • Implied: 33.4%
-4.2%
— NO EDGE
Away Win
Model: 46.5% • Implied: 46.2%
+0.7%
⚖️ FAIR
📊 Over/Under 2.5
Over 2.5
Model: 25.9% • Implied: 26.6%
-2.6%
— NO EDGE
Under 2.5
Model: 74.1% • Implied: 72.4%
+2.4%
✅ VALUE
🎯 Both Teams to Score
Yes
Model: 32.1% • Implied: 32.5%
-1.2%
⚖️ FAIR
No
Model: 67.9% • Implied: 66.5%
+2.1%
✅ VALUE
💡 +EV Detected: Optimize Your Market Edge

Our model identified a +2.4% 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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🔴 LOW CONFIDENCE39%
📈 3 matches analyzedLimited data available: only 3 matches. Projections carry higher uncertainty.

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📖 Match Preview & Analysis

The Bundesliga clash between SC Freiburg and RB Leipzig arrives with a clear statistical narrative. Our Poisson regression model projects RB Leipzig as a moderate favorite, assigning them a 46.5% probability of winning, versus 21.5% for SC Freiburg. A draw sits at 32.0%.

Recent form favors neither side clearly. SC Freiburg has 2 wins and 1 losses in their last 5 fixtures, while RB Leipzig records 2 wins and 2 losses over a similar span.

Model Insight: Defensive metrics dominate this fixture. Under 2.5 goals projects as the highest-probability outcome.

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 SC Freiburg & RB Leipzig stand in Bundesliga
Bundesliga Table →More Fixtures

💬 Match Discussion(0)

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🧮 How was this prediction calculated?

Win probabilities for SC Freiburg vs RB Leipzig 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 · 39%
Matches Analysed
3
xG Trend
0.70
H2H Avg Goals
2.1
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