PREDICTIVE ANALYTICS
Risk Forecasting & Predictions
VIRCON's forecasting system uses historical data and trend analysis to predict future moderation needs, identify high-risk periods, and flag users likely to require escalated intervention. Predictions help admins optimize staffing and take proactive measures before issues escalate.
Forecasting Commands
Commands for generating predictions, checking accuracy, and viewing risk assessments.
- ,predict forecast [days_ahead] - Generate staffing and activity forecast
- ,predict list - View all active predictions
- ,predict verify <prediction_id> - Check prediction accuracy
- ,predict accuracy - View overall prediction accuracy metrics
- ,risk check - Calculate current community risk level
- ,risk update - Recalculate risk scores immediately
- ,risk high - Show users flagged as high-risk
- ,risk refresh - Update risk forecast baselines
Forecast Types
Categories of predictions with accuracy ranges and forecast horizons.
| Type | Accuracy | Horizon |
|---|---|---|
Staffing Needs Predicted staff coverage required based on activity trends | 75-85% | 7-14 days |
Incident Volume Expected number of moderation actions per day | 70-80% | 3-7 days |
High-Risk Periods Time windows with elevated moderation likelihood | 80-90% | 24-72 hours |
User Risk Escalation Users likely to require escalated moderation | 65-75% | 1-3 days |
Risk Calculation Factors
Variables considered when calculating user risk scores and escalation likelihood.
Forecasting Workflow
Machine learning pipeline from data collection through model training to prediction generation.
- 1. System collects historical data: incidents, staff activity, time patterns.
- 2. Machine learning model trained on 90+ days of historical trends.
- 3. Daily forecast generated at 00:00 UTC for next 7 days.
- 4. Predictions include: expected incidents, recommended staff count, risk periods.
- 5. Forecasts sent as summary report to admin channel.
- 6. Staff uses predictions to optimize shift coverage and resource allocation.
- 7. Actual outcomes compared against predictions to measure accuracy.
- 8. Model retrains weekly incorporating new data and accuracy feedback.
Prediction Database Schema
Database structure for storing forecasts, confidence scores, and accuracy verification.
| Field | Description |
|---|---|
| prediction_id | Unique prediction identifier. |
| prediction_type | staffing_needs, incident_volume, high_risk_periods. |
| created_at | Timestamp when prediction was generated. |
| forecast_date | Target date for the prediction. |
| predicted_value | Numeric forecast (e.g., 15 incidents expected). |
| confidence_score | 0-100 confidence level in prediction. |
| actual_value | Observed actual outcome (NULL until date passes). |
| accuracy_score | Calculated match between predicted and actual. |
| verified_at | Timestamp when outcome was verified. |
Risk Assessment Database Schema
Database structure for user risk levels, contributing factors, and trend analysis.
| Field | Description |
|---|---|
| risk_id | Unique risk assessment identifier. |
| user_vrc_id | VRChat user being assessed. |
| risk_level | low, medium, high, critical. |
| risk_score | 0-100 numeric risk score. |
| contributing_factors | JSON array of factors that elevated risk. |
| last_calculated | Timestamp of most recent risk calculation. |
| trend | increasing, stable, decreasing. |
| recommended_action | watch, warn, escalate_to_admin. |
Automation Features
Scheduled forecast generation, adaptive model retraining, and proactive alerting.
- Daily forecast generation at 00:00 UTC for next week.
- Auto-alerts when high-risk period predicted within 48 hours.
- Risk scores auto-recalculated after any moderation action.
- Trend detection: users with rising risk get auto-flagged.
- Weekly accuracy report showing model performance metrics.
- Model auto-retraining when accuracy drops below 70%.
Real-World Use Cases
Practical examples of predictive analytics improving staffing and preemptive moderation.
Case 1
Weekend community event scheduled
Forecast: System predicts 2.5x normal incident volume on Saturday
Action: Head admin schedules extra staff for event coverage
Outcome: Actual volume 2.3x normal - staffing was adequate
Case 2
User with declining trust score and recent warnings
Forecast: Risk model flags user as 85% likely to escalate within 48h
Action: Staff monitors user activity, provides warning about policy
Outcome: User moderates behavior, risk score decreases
Case 3
Weekly forecast shows low activity Tuesday-Thursday
Forecast: Predicted 3-5 incidents per day, recommend 2 staff
Action: Coverage adjusted, staff schedules lighter shifts
Outcome: Actual 4 incidents Tue, 3 Wed, 5 Thu - forecast accurate
Best Practices
Guidelines for leveraging forecasts effectively while maintaining human judgment.
- Review daily forecasts to optimize shift scheduling.
- Use high-risk period predictions to increase staff coverage preemptively.
- Monitor model accuracy weekly - retrain if performance degrades.
- Cross-reference risk scores with trust scores for comprehensive view.
- Don't use predictions as sole basis for action - verify before acting.
- Document when predictions were accurate or inaccurate for model improvement.