Nashville SC vs Inter Miami Sun, Aug 16, 2026 08:30 AM AI Verdict Away Win — 44% probability ★☆☆☆☆ S. Surridge (Nashville SC) — Minor Missing Fixture P. Yazbek (Nashville SC) — Minor Missing Fixture E. Tagseth (Nashville SC) — Minor Missing Fixture Confidence: Uncertain · Updated: Sun, Aug 16, 2026 08:30 AM AI Prediction Predicted outcome: Away Win (38% probability) Home Win: 38% Draw: 18% Away Win: 44% Most likely scores: 1-1 (12%), 1-0 (11%), 0-1 (11%) Model Confidence Confidence Rating: Uncertain (15%) Very close race — model is uncertain about the ranking Prediction Evolution (8 snapshots) 17:10 — Home 0% / Draw 0% / Away 0% 17:10 — Home 1% / Draw 0% / Away 0% (Δ Home +0.2%, Away -0.2%) 17:10 — Home 0% / Draw 0% / Away 0% (Δ Home -0.2%, Away +0.2%) 17:10 — Home 0% / Draw 0% / Away 0% 17:26 — Home 35% / Draw 23% / Away 42% (Δ Home +34.4%, Draw +22.6%, Away +42.0%) 17:26 — Home 35% / Draw 23% / Away 42% 17:26 — Home 35% / Draw 23% / Away 42% 17:44 — Home 35% / Draw 23% / Away 42% Advanced Details Match Signals 🩹 S. Surridge (Nashville SC) — Minor Missing Fixture ✅ confirmed [Impact: Home -1%, Away -1%] 🩹 P. Yazbek (Nashville SC) — Minor Missing Fixture ✅ confirmed [Impact: Home -1%, Away -1%] 🩹 E. Tagseth (Nashville SC) — Minor Missing Fixture ✅ confirmed [Impact: Home -1%, Away -1%] 🩹 A. Najar (Nashville SC) — Minor Missing Fixture ✅ confirmed [Impact: Home -1%, Away -1%] Frequently Asked Questions Is Away Win really likely to win? Away Win has a 44% edge — the model sees a slight advantage but recognizes the matchup is competitive. Draw (18%) and the other outcome are both realistic possibilities. What is the biggest factor affecting this prediction? S. Surridge (Nashville SC) — Minor Missing Fixture How confident is the model in this ranking (Uncertain, 15%)? 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.