Japan vs Tunisia prediction for June 21, 2026: Our Monte Carlo simulation ran 5,000 game iterations and projects Tunisia 1.18 - Japan 2.1. Japan is favored with a 56.9% win probability. Expected total goals: 3.3..
Tunisia
1.18
Projected Goals
VS
3.3 total
Japan
2.1
Projected Goals
Match Outcome Probabilities
TunisiaDrawJapan
Calibrated accuracy at this confidence: 81.8% (1,098 games)
Projected Goals Range 10th – 90th percentile
Japan
1.32.12.9
Tunisia
0.41.22.0
Projected
Tunisia 1.18 — Japan 2.1
Actual
Tunisia 0 — Japan 4
Expected Goals (xG)
Tunisia1.18
Japan2.10
21.7Shots18.2
7.9On Target6.6
6.5Corners5.9
Goal Probabilities
Over 0.5
98.1%
Over 1.5
87.2%
Over 2.5
59.5%
Over 3.5
47.1%
Under 2.5
40.5%
BTTS
66.0%
Most Likely Scores
1-1
10.3%
1-2
10.0%
0-2
8.5%
0-1
7.4%
1-3
7.0%
Match Context
WCMedium
Tunisia
7.15
Draw
4.20
Japan
1.56
AI Intelligence Analysis
NEUTRALRED ZONE36.1% WR (n=None)
Match already resolved (Japan 4, Tunisia 0 on 2026-06-21). Betting window is closed. Game is dead for wagering purposes.
Key Factors
- Match result: Tunisia 0, Japan 4 (FINAL) — confirms model directional accuracy
- xG profile: Japan 2.1 vs Tunisia 1.18 (0.92 xG advantage — validated)
- Model away win prob: 56.94% vs market 64.10% = model slightly pessimistic but directionally correct
- Actual away margin: Japan 4-0 (decisive) validates xG quality gap
- Market overconfidence: 64.10% market prob vs 56.94% model = 7.16% gap (market too confident, but Japan won decisively)
Risk Factors
- MATCH RESOLVED — no betting window open
- Historical note: Away ML red zone (36.1% WR) even though Japan won decisively — suggests market pricing correct despite model edge
- Caution for future away ML recommendations: degraded performance vs historical 54.6% suggests market has adapted or edge closed
Edge Analysis
Moneyline
Japan 56.9%
--
Total
3.3
+22.9 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 →