Pattern Failure Statistics

What are Pattern Failure Statistics?

Pattern failure statistics are measured base rates for how often a chart pattern actually does what the textbook says. Instead of treating a formation as a picture, the approach defines an outcome in advance (does the breakout follow through, how far does price travel versus the measure rule target, how often does it reverse immediately) and counts results across a large historical sample. Thomas Bulkowski's catalog work popularized the method, including the widely used benchmark that counts a breakout as failed when price reverses before moving even five percent in the breakout direction.

The findings that recur across studies are structural rather than numeric: failure rates differ by pattern, by breakout direction, and by market regime; average moves usually fall short of textbook targets; and timing matters, for example triangle breakouts that arrive very late, near the apex, tend to be weaker than ones that leave earlier. A pattern that exceeds its typical duration without triggering is treated as stale. Published numbers shift with the sample and the era, so they are priors, not promises.

How traders use it

• To predefine failure: a false breakout threshold (a reversal before some minimal move, or a close back inside the pattern) is set before entry, so stop placement follows from the statistic rather than from hope.
• To temper targets: measure-rule projections are discounted toward the documented average move instead of being taken at face value.
• To filter setups: contexts with better documented base rates, such as breakouts aligned with the prevailing trend or backed by breakout confirmation, are preferred over raw pattern matches.