AI Verdict
Home Win — 43% probability ★☆☆☆☆
- L. Barroso (Chicago Fire) — Injury default
- J. Bamba (Chicago Fire) — Injury default
- A. Franco (Chicago Fire) — Injury Knee
Confidence: Uncertain · Updated: Thu, Aug 20, 2026 07:30 AM
AI Prediction
Predicted outcome: Home Win (43% probability)
- Home Win: 43%
- Draw: 17%
- Away Win: 41%
Most likely scores: 1-1 (12%), 1-0 (11%), 0-1 (11%)
Model Confidence
Confidence Rating: Uncertain (7%)
Very close race — model is uncertain about the ranking
Prediction Evolution (9 snapshots)
- 17:10 — Home 0% / Draw 0% / Away 0%
- 17:10 — Home 0% / Draw 0% / Away 0%
- 17:10 — Home 0% / Draw 0% / Away 0%
- 17:10 — Home 0% / Draw 0% / Away 0%
- 18:56 — Home 47% / Draw 23% / Away 30% (Δ Home +46.1%, Draw +22.8%, Away +30.1%)
- 18:56 — Home 47% / Draw 23% / Away 30%
- 08:07 — Home 47% / Draw 23% / Away 31% (Δ Home +0.1%, Draw -0.3%, Away +0.2%)
- 08:07 — Home 47% / Draw 23% / Away 31%
- 08:07 — Home 47% / Draw 23% / Away 31%
Advanced Details
Match Signals
- 🩹 L. Barroso (Chicago Fire) — Injury default ✅ confirmed
- 🩹 J. Bamba (Chicago Fire) — Injury default ✅ confirmed
- 🩹 A. Franco (Chicago Fire) — Injury Knee ✅ confirmed [Impact: Home +1%, Draw +1%, Away -2%]
- 🩹 N. Miller (Orlando City SC) — Minor Missing Fixture ✅ confirmed
- 🩹 J. Gerbet (Orlando City SC) — Minor Missing Fixture ✅ confirmed [Impact: Home -1%, Away +1%]
Frequently Asked Questions
- Is Home Win really likely to win?
- Home Win has a 43% edge — the model sees a slight advantage but recognizes the matchup is competitive. Draw (17%) and the other outcome are both realistic possibilities.
- What is the biggest factor affecting this prediction?
- L. Barroso (Chicago Fire) — Injury default
- How confident is the model in this ranking (Uncertain, 7%)?
- Model Confidence measures how certain the AI is about the order of outcomes, not any single probability. This is a toss-up — the model struggles to separate the outcomes confidently.
- 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.