AI Verdict
Home Win — 77% probability ★★★★★
- E. Sabbi (Vancouver Whitecaps) — Injury Groin
- C. Sabaly (Vancouver Whitecaps) — Injury Hamstring
- R. Priso (Vancouver Whitecaps) — Injury Muscle
Confidence: Decisive · Updated: Thu, Aug 20, 2026 10:30 AM
AI Prediction
Predicted outcome: Home Win (77% probability)
- Home Win: 77%
- Draw: 11%
- Away Win: 12%
Most likely scores: 1-1 (12%), 1-0 (11%), 0-1 (11%)
Model Confidence
Confidence Rating: Decisive (100%)
Clear separation between top 2 outcomes
Prediction Evolution (10 snapshots)
- 17:10 — Home 1% / Draw 0% / Away 0%
- 17:10 — Home 1% / Draw 0% / Away 0%
- 17:10 — Home 1% / Draw 0% / Away 0% (Δ Home -0.3%, Draw +0.1%, Away +0.1%)
- 17:10 — Home 1% / Draw 0% / Away 0%
- 08:07 — Home 56% / Draw 23% / Away 21% (Δ Home +55.4%, Draw +22.8%, Away +20.8%)
- 08:07 — Home 56% / Draw 23% / Away 21%
- 08:07 — Home 56% / Draw 23% / Away 21%
- 08:45 — Home 56% / Draw 23% / Away 21%
- 08:45 — Home 56% / Draw 23% / Away 21%
- 08:45 — Home 56% / Draw 23% / Away 21%
Advanced Details
Match Signals
- 🩹 E. Sabbi (Vancouver Whitecaps) — Injury Groin ✅ confirmed
- 🩹 C. Sabaly (Vancouver Whitecaps) — Injury Hamstring ✅ confirmed
- 🩹 R. Priso (Vancouver Whitecaps) — Injury Muscle ✅ confirmed
- 🩹 B. Halbouni (Vancouver Whitecaps) — Injury Knee ✅ confirmed
- 🩹 K. G. Cabrera Nakamura (Vancouver Whitecaps) — Injury default ✅ confirmed
- 🩹 S. Berhalter (Vancouver Whitecaps) — Injury default ✅ confirmed
Frequently Asked Questions
- Why is Home Win the clear favorite?
- The AI model assigns 77% probability to Home Win, indicating strong confidence based on historical data, ELO ratings, and recent form analysis.
- What is the biggest factor affecting this prediction?
- E. Sabbi (Vancouver Whitecaps) — Injury Groin
- How confident is the model in this ranking (Decisive, 100%)?
- Model Confidence measures how certain the AI is about the order of outcomes, not any single probability. A clear gap between the top two outcomes gives the model high conviction in its ranking.
- How does the AI prediction model work?
- Our ensemble combines Gradient Boosting and Random Forest models trained on historical match data, ELO team ratings, recent form, and statistical metrics. Score distributions use Poisson-based simulations for the most likely scorelines.