Detecting anomalies in game data in real-time is crucial for maintaining game integrity and player experience. We're implementing a system to identify unusual patterns such as sudden spikes in virtual currency generation, abnormal player progression rates, suspicious trading patterns, or unexpected server load. The challenge is distinguishing between legitimate anomalies (like viral marketing success or influencer-driven player surges) and actual problems (exploits, bots, server issues). We're experimenting with statistical methods, machine learning models, and rule-based systems, but finding the right balance is tricky. How do you approach baseline establishment for different metrics? What techniques have you found effective for reducing false positives while maintaining quick detection? Do you use different thresholds for different times of day or special events? Also curious about your incident response workflows once an anomaly is detected!