USA vs Belgium Tue, Jul 7, 2026 08:00 AM AI Verdict Home Win — 38% probability ★☆☆☆☆ M. McKenzie (USA) — Injury default F. Balogun (USA) — Injury Suspended C. Pulišić (USA) — Injury Calf Confidence: Uncertain · Updated: Tue, Jul 7, 2026 08:00 AM AI Prediction Predicted outcome: Home Win (38% probability) Home Win: 38% Draw: 28% Away Win: 35% Most likely scores: 1-1 (13%), 1-0 (13%), 0-0 (13%) Model Confidence Confidence Rating: Uncertain (9%) Very close race — model is uncertain about the ranking Prediction Evolution (13 snapshots) 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 14:49 — Home 58% / Draw 17% / Away 25% 16:19 — Home 38% / Draw 28% / Away 35% (Δ Home -20.2%, Draw +10.5%, Away +9.7%) 16:49 — Home 38% / Draw 28% / Away 35% 16:49 — Home 38% / Draw 28% / Away 35% Advanced Details Match Signals 🩹 M. McKenzie (USA) — Injury default ✅ confirmed [Impact: Home -2%, Draw +1%, Away +1%] 🩹 F. Balogun (USA) — Injury Suspended ✅ confirmed [Impact: Home -2%, Draw +1%, Away +1%] 🩹 C. Pulišić (USA) — Injury Calf ✅ confirmed 🩹 N. Ngoy (Belgium) — Injury Suspended ✅ confirmed 🩹 J. Doku (Belgium) — Injury Illness ✅ confirmed 🩹 Z. Debast (Belgium) — Injury default ✅ confirmed 🩹 C. Roldan (USA) — Injury Muscle ✅ confirmed Frequently Asked Questions Why is this match so hard to predict? With probabilities of Home 38%, Draw 28%, Away 35%, the model considers all outcomes to be closely balanced — this is a genuinely unpredictable matchup. What is the biggest factor affecting this prediction? M. McKenzie (USA) — Injury default How confident is the model in this ranking (Uncertain, 9%)? 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.