[gen220]

You’re talking about reversion to the mean, which is a phenomenon that’s related to the law of large numbers.

Law of Large Numbers says that, over an arbitrarily large random sampling size, you will eventually end up with a sample that perfectly fits the probability distribution.

But the probability of each individual sample is random. This means that, if each sample is randomly-selected and independent, your history of N samples does not affect your N+1th sample.

The regression to mean curve is only predictable in the big picture, each bump is 50/50 (or 60/40 in this case).