Soccer

Strasbourg vs Brest Prediction

May 13, 2026

15,000 Monte Carlo simulations

Strasbourg vs Brest prediction for May 13, 2026: Our Monte Carlo simulation ran 15,000 game iterations and projects Brest 1.6 - Strasbourg 1.49. Brest is favored with a 37.5% win probability. Expected total goals: 3.1..

Brest
1.6
Projected Goals
VS 3.1 total
Strasbourg
1.49
Projected Goals
Match Outcome Probabilities
37.5%
36%
26.8%
BrestDrawStrasbourg
Calibrated accuracy at this confidence: 75.5% (1,051 games)

Projected Goals Range 10th – 90th percentile

Strasbourg
0.71.52.3
Brest
0.81.62.4
FINALBrest 1 — Strasbourg 2
Projected
Brest 1.6 — Strasbourg 1.49
Actual
Brest 1 — Strasbourg 2

Expected Goals (xG)

Brest0.92
Strasbourg0.82
22.1Shots21.6

Goal Probabilities

Over 0.5
95.4%
Over 1.5
81.5%
Over 2.5
59.5%
Over 3.5
37.4%
Under 2.5
40.5%
BTTS
67.0%

Match Context

LIGMedium
Brest
3.43
Draw
3.67
Strasbourg
2.16

AI Intelligence Analysis

NEUTRALRED ZONE42.9% WR (n=75)
Market is correct to favor away Strasbourg; model is overweighting home despite better-attacking away team. Draw-heavy fixture reduces ML value. Neither side offers profitable entry.

Key Factors

  • Strasbourg attack superior (1.54 vs Brest 1.22): 0.32-point gap, model xG gap only 0.1
  • Draw probability 35.62% — well above 23-25% league avg, reflects tight matchup
  • Home ML shows 8.4% edge, but market correctly favors away (46.30% implied) reflecting team quality

Risk Factors

  • Home ML in RED zone (42.9% WR) — proven money pit historically
  • High draw rate (35.62%) suppresses home outright win value by ~8-10%
  • Model overconfidence: giving home 37.55% despite inferior attack and high draw likelihood
Sharp MoneyAgainst ModelMarket favors away (46.30%); model overweights home. Sharp action likely with away/draw.
RED ZONEMODEL MARKET CONFLICTDRAW RISKSMALL XG GAP

Edge Analysis

Moneyline
Brest 37.5%
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Total
3.1
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How this prediction was generated: This page shows output from the Olympus Bets Soccer Monte Carlo engine. Each game is simulated 15,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →

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