According to Wikipedia:

In statistics, regression toward (or to) the mean is the phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement—and, paradoxically, if it is extreme on its second measurement, it will tend to have been closer to the average on its first. To avoid making incorrect inferences, regression toward the mean must be considered when designing scientific experiments and interpreting data.

When we are looking at data, the data has an average (mean) and other ranges that define its highs and lows. If use the numbers 1 to 100 as an example, the average is 50. That necessarily means half of the numbers will be above and below 50. The more numbers we draw out that are above 50, the more of those numbers we have removed from the “above 50” pile, so the probability is higher we draw a lower number for the simple reason: there are more of them left to choose.

Francis Galton created the statistical concept of correlation and widely promoted regression toward the mean.