The reason is that given any dataset, you can come up with thousands of stories that explain the data perfectly, but have no predictive power. This is also called 'overfitting' in machine learning contexts.
However, a prediction made before events can use those events as evidence of the correctness of the theory.
What is the evidence for the theory in this article? Well, it is that it explains past events. But those events can't be used as evidence for the theory, and if that is all there is, this is a 'just so story'.
Another way to say this is: "The fact that a theory does not conflict with existing observations is not evidence that it is correct."