Avispa Fukuoka vs V-varen Nagasaki Sat, Apr 11, 2026 01:00 PM · Score: 1 - 0 AI Verdict Away Win — 52% probability ★★★★☆ Outlook stable Confidence: Decisive · Updated: Sat, Apr 11, 2026 01:00 PM AI Prediction Predicted outcome: Away Win (29% probability) Home Win: 29% Draw: 19% Away Win: 52% Most likely scores: 1-1 (13%), 1-0 (13%), 0-1 (12%) Model Confidence Confidence Rating: Decisive (67%) Clear separation between top 2 outcomes 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 (52% confidence) but the match ended 1-0. Primary factor: Multiple factors diverged from model assumptions. Key Deviations Where the match numbers diverged from model expectations. Error Analysis Primary Reason: Multiple factors diverged from model assumptions Error Categories: No significant deviation 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 Why is Away Win the clear favorite? The AI model assigns 52% probability to Away Win, indicating strong confidence based on historical data, ELO ratings, and recent form analysis. How confident is the model in this ranking (Decisive, 67%)? 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. Why did the AI get this match wrong? The primary reason was: Multiple factors diverged from model assumptions. 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.