What's complicated about regression to the mean? Note: I am a bit of a novice when it comes to statistics and data analysis.
Reading the chapter on regression to the mean in Kahneman's Thinking Fast and Slow, I came across the following passage:

The very idea of regression to the mean is
alien and difficult to communicate and comprehend. Galton had a hard time before he
understood it. Many statistics teachers dread the class in which the topic comes up, and
their students often end up with only a vague understanding of this crucial concept.

I really don't see what's difficult/complicated to explain or counter-intuitive, only that the concept might "hide" and be easy to miss in certain situations. But, the statement "Many statistics teachers dread the class..." make me actually believe that I have misunderstood something.
Made simple, here's my intuitive understanding:
If we observed an outcome that had a very low probability of happening, then the next time we do the same thing again, the outcome will not be as extreme. To me this is self-evident, since by definition, the first event was unlikely. If a bunch of people throw a "gaussian die" with values 1-10, and some throw 1 or 10, you don't expect them to repeat it the next time for the same reason you didn't expect them to do it the first time. This will then of course be more or less prominent depending on how much randomness (luck) is involved.
Question: Is my intuitive understanding correct or am I missing something?
I don't want to sound like I think I'm smarter than e.g. any statistics teachers, quite the contrary; I genuinely believe I've missed something here.

I think part of the problem is the phrase "regression to the mean".  It encourages people to believe the gambler's fallacy.  Suppose you flip a coin 10 times and get heads every time.  It is an unlikely event, 1/1024 but quite possible.  People think regression to the mean means that heads are less likely going forward.  In fact it means this excess of heads gets swamped as we increase the number of trials.  As long as we still believe it is a fair coin, we expect 5 heads in the next 10 trials.  We expect 500 in the next 1000.  If we flip another 10,000 times, the most likely total number of heads is 5,010.  The standard deviation is 50, so the excess of 10 is nothing.
The hard part is after the first 10 flips do you still believe the coin is fair?
