Wales vs Bosnia & Herzegovina Fri, Mar 27, 2026 03:45 AM · Score: 1 - 1 AI Verdict Away Win — 40% probability ★☆☆☆☆ Outlook stable Confidence: Uncertain · Updated: Fri, Mar 27, 2026 03:45 AM AI Prediction Predicted outcome: Away Win (39% probability) Home Win: 39% Draw: 20% Away Win: 40% Most likely scores: 1-1 (13%), 0-1 (13%), 1-0 (12%) Model Confidence Confidence Rating: Uncertain (3%) Very close race — model is uncertain about the ranking Match Pulse ➡️ Outlook stable (LOW CONFIDENCE) No significant events detected Explainable AI Review After the match, the AI explains why its prediction succeeded or failed. Predicted: [object Object] Actual: [object Object] Prediction Correct? ❌ No What Went Wrong — And Why The model's prediction was off — it expected a away win (40% confidence) but the match ended 1-1. Primary factor: Pre-match injuries affected team performance. Key Deviations Where the match numbers diverged from model expectations. Error Analysis Primary Reason: Pre-match injuries affected team performance Error Categories: Injury Impact Model Performance How the AI model has performed historically, so you can calibrate your trust in its predictions. This match: ❌ Incorrect — Predicted [object Object], Actual [object Object] Track record: The model is evaluated continuously. Visit the Tracking page for Brier scores, calibration curves, and accuracy by league. How predictions are made: Our ensemble combines Gradient Boosting + Random Forest with Poisson-based score distributions, trained on historical match data, ELO ratings, and recent form. Advanced Details Frequently Asked Questions Is Away Win really likely to win? Away Win has a 40% edge — the model sees a slight advantage but recognizes the matchup is competitive. Draw (20%) and the other outcome are both realistic possibilities. How confident is the model in this ranking (Uncertain, 3%)? 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. Why did the AI get this match wrong? The primary reason was: Pre-match injuries affected team performance. The Explainable AI Review above breaks down exactly which metrics (xG, possession, shots) diverged from what the model expected. 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.