How likely is a typical player to hit a parry window of a few frames?

Designers pick a timing window by feel and then learn from reviews that it is either trivial or unfair. If you know how scattered players' timing is, the success rate follows from a bell curve and a half-frame correction that is easy to forget.

Why this works

No one presses at exactly the same moment twice, but a crowd of players clusters around a typical moment and thins out on both sides. Knowing that typical moment and how scattered it is tells you how much of the crowd a window catches, and presses fall on whole steps.

When not to use it

Skip it when the window is not about raw timing, or the player gets other cues such as a sound and a flash that change how they time. Also skip it if you have no real press times: a guessed spread gives a false sense of precision.

In the full entry

• What to build, step by step
• Starting values and the reason for each
• 3 common mistakes and the fix
• 24 lines of example code with 4 checks that pass
• How your AI shows you that it works

Say this to your AI

Use the Spielerando entry "How likely is a typical player to hit a parry window of a few frames?" (read_entry parry-window-success-chance-from-timing-spread) in my game. Build it step by step, and show me how you checked that it works.

Or skip this and connect the MCP: your AI then finds all of this by itself.Connect the MCP