Suppose you know a golfer's score on day 1 and are asked to predict his score on day 2. You expect the golfer to retain the same level of talent on the second day, so your best guesses will be "above average" for the [better-scoring] player and "below average" for the [worse-scoring] player. Luck, of course, is a different matter. Since you have no way of predicting the golfers' luck on the second (or any) day, your best guess must be that it will be average, neither good nor bad. This means that in the absence of any other information, your best guess about the players' score on day 2 should not be a repeat of their performance on day 1. ...

The best predicted performance on day 2 is more moderate, closer to the average than the evidence on which it is based (the score on day 1). This is why the pattern is called regression to the mean. The more extreme the original score, the more regression we expect, because an extremely good score suggests a very lucky day. The regressive prediction is reasonable, but its accuracy is not guaranteed. A few of the golfers who scored 66 on day 1 will do even better on the second day, if their luck improves. Most will do worse, because their luck will no longer be above average.

Now let us go against the time arrow. Arrange the players by their performance on day 2 and look at their performance on day 1. You will find precisely the same pattern of regression to the mean. ... The fact that you observe regression when you predict an early event from a later event should help convince you that regression does not have a causal explanation.

• Daniel Kahneman, Thinking, Fast and Slow