Autoclicker Interval Shape Checks Explained

What do interval shape checks look at?

Interval shape checks ignore what a click did. They study the spacing between clicks: how steady the gaps are, whether the same gaps repeat, whether the clicks sit on a tidy grid of game ticks, and whether short bursts appear. FairPlay runs many of these as separate autoclicker checks, so each one answers one narrow question about the same click stream.

Because each check is narrow, a single alert rarely settles anything. Staff should look for several shape checks agreeing on the same play session.

A useful review pairs the shape with what the player was doing. The same gap pattern during a long mining session means something different from the same pattern in a fast duel, so write down the activity before you compare shapes.

Reviewers should also note the client version and the input device, because those change the shape of legitimate clicks in ways that have nothing to do with intent.

Which timing signatures are observed?

The public names in this family include AutoClickerIntervalStability, AutoClickerDistinctDelays, AutoClickerDelaySkewness, AutoClickerOutliers, AutoClickerRoundedCps, AutoClickerCpsSpikes, AutoClickerBurst, AutoClickerTickSequence, AutoClickerPhaseLock, AutoClickerPerfectTiming, AutoClickerPatternStats, AutoClickerCpsLimits and AutoClickerMachineLearning. Grouped by what they see, they cover five kinds of evidence:

• Gaps that stay almost identical across a session, which the stability and perfect-timing checks read.
• Gaps that keep returning to a small set of values, or that lock to a repeating phase, which the distinct-delay and phase-lock checks read.
• Gaps with a lopsided spread or a few extreme values, which the skewness and outlier checks read.
• Click counts that land on round numbers, or sudden jumps in click rate and short runs of very fast clicks, which the rounded, spike and burst checks read.
• Clicks that line up with game ticks in a fixed order, along with broader rate and pattern statistics and a learned model layer.

Which cheat behavior do these checks target?

Clicking scripts tend to produce gaps that are either very even or very random. Neither matches how human fingers settle into a rhythm, which drifts and changes with fatigue and with the fight. A script that adds random jitter tries to hide the even spacing, which is why this family includes pattern and phase checks. Those checks look for a repeated shape that survives small changes.

The machine-learning check is a separate layer. It compares the whole shape of a stream with learned examples rather than a single number. Its output is one more input for a reviewer, not a standalone decision.

What legitimate play can look the same?

Several legal input styles produce the same shapes. Jitter clicking and butterfly clicking create tight, repeating gaps by design, and our separate article on those styles goes into their physical mechanics. A player who taps on a fixed beat while waiting for a cooldown can show a phase pattern without any software.

Hardware and settings matter as well. Some mice and keyboards report on a fixed schedule, and some key-repeat settings quantize the gap. Network timing can squeeze or merge clicks, so a player on a bad connection can show outliers and spikes that come from delivery, not from the hand. Compare the shape against a reviewed legit session from the same player before you act.

How do staff read interval alerts?

Use a fixed order for every interval alert. The steps below keep the review consistent between staff members, and they keep a single noisy check from driving the decision.

• Open the alert and note which interval checks fired together. One check on its own is a weak signal.
• Compare the shape with a previously reviewed legit session from the same player, if you have one.
• Pull the play context: combat, mining or menu use, and the weapon or tool in hand.
• Check the ping history for the same window before deciding the shape is a client or network effect.
• Record the decision, the checks involved and the evidence in your case log.

What should you tune only in staging?

Tune the interval family only with recorded legit play from every input device your players use. Keep the checks alert-only until reviewers agree that the alerts read correctly on your server. Change one check at a time, since several checks look at overlapping evidence and a change to one can shift what another reports.

Each check has its own settings in the reference. Read that page before you edit anything, and do not copy values from another server, because the same numbers mean different things on a different player base and client mix.

What have we not verified?

We have not measured how often each interval check fires, which devices produce which shapes, or how this family compares with other anticheats. The machine-learning layer has its own review path, and this article does not claim how it performs. Confirm the behavior on your own staging server with your own recorded sessions.