Minecraft anticheat guide
FairPlay AutoClicker Detection: CPS, Patterns, and ML Windows
How FairPlay approaches AutoClicker detection through CPS limits, click interval statistics, phase locks, pattern checks, and machine-learning workflows.
CPS alone is not enough
A high CPS number can be suspicious, but a good AutoClicker check also inspects interval stability, delay skewness, spikes, phase locks, rounded CPS, distinct delay counts, hit/miss consistency, outliers, perfect timing, and tick-aligned sequences.
FairPlay includes multiple AutoClicker checks so one noisy signal does not carry the whole decision.
Why pattern windows matter
Click cheats often try to randomize. The question is whether the randomization still has mechanical artifacts. Pattern windows can expose repeated distributions, too-few delay values, synthetic stability, and suspicious click rhythm over time.
ML workflow
FairPlay includes AutoClickerMachineLearning alongside model registry and training workflows. ML should not replace basic evidence, but it can provide another confidence layer for advanced click-window analysis.
FAQ
Does FairPlay detect AutoClickers?
Yes. FairPlay includes AutoClicker checks for CPS, timing, stability, spikes, phase locks, patterns, hit/miss consistency, and ML workflows.
Is CPS the only signal?
No. CPS is only one input. Timing distribution and pattern quality matter more for clean detection.