DR Congo vs Colombia prediction for June 24, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Colombia 2.16 - DR Congo 1.16. Colombia is favored with a 57.5% win probability. Expected total goals: 3.3..
Colombia
2.16
Projected Goals
VS
3.3 total
DR Congo
1.16
Projected Goals
Match Outcome Probabilities
ColombiaDrawDR Congo
Calibrated accuracy at this confidence: 66.6% (1,107 games)
Projected Goals Range 10th – 90th percentile
DR Congo
0.41.21.9
Colombia
1.42.22.9
Expected Goals (xG)
Colombia2.16
DR Congo1.16
23.6Shots16.6
8.8On Target5.8
6.7Corners5.8
Goal Probabilities
Over 0.5
98.2%
Over 1.5
88.7%
Over 2.5
60.2%
Over 3.5
46.1%
Under 2.5
39.8%
BTTS
65.4%
Most Likely Scores
1-1
10.0%
2-1
10.0%
2-0
8.7%
1-0
7.3%
3-1
7.2%
Match Context
WCMedium
Colombia
1.56
Draw
4.15
DR Congo
7.20
AI Intelligence Analysis
NEUTRAL -1RED ZONE42.6% WR (n=50)
Home ML in RED ZONE (42.6% WR) with 24.22% draw risk swallowing modest +4.84% probability edge; market efficiency prevents profitable wagering.
Key Factors
- Home ML RED ZONE: 42.6% WR across 50 tracked bets (z=-1.13) — proven losing category
- Draw probability: 24.22% in 3-way ML means Colombia bet loses ~1 in 4 outcomes regardless of competitive result
- xG gap: Colombia 2.16 vs DR Congo 1.16 = +1.00 xG/90 advantage, but insufficient vs zone underperformance
- Market efficiency: +4.84% edge (57.48% model vs 52.64% market) is modest; market respects Colombia as favorite
- BTTS risk: 65.38% BTTS probability means high-scoring, volatile outcomes despite model dominance signal
Risk Factors
- Draw outcome swallows home favorite premium: 24.22% draw probability kills ML value regardless of xG edge
- Home bias trap: Market -111 reflects public perception of home advantage, but home ML historically 42.6% WR — proven overvalued
- Total volatility: 3.31 avg goals suggests 4-5 goal games possible, increasing outcome variance
HOME ML TRAPDRAW RISKRED ZONE
Edge Analysis
Moneyline
Colombia 57.5%
--
Total
3.3
+27.2 pts
How this prediction was generated: This page shows output from the Olympus Bets Soccer Monte Carlo engine. Each game is simulated 5,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →