I recently came across one of the old Kaggle challenges, where the participants found out that there were numerous highly correlated variables (>0.95) and had to delete one of them from data modeling. However, there was a twist in this competition. The difference between the two highly correlated variables (both standardized), turned out to be a very good predictor as well. The participants called it "Golden Feature". You can read more in the discussion forum but aforementioned is basically what they did.
Now, after going through all the discussion in Kaggle, I couldn't find out any underlying statistical theory behind it. On the other hand, it seems to me a valid way to handle multicollinearity issue. Because keeping the difference would mean we are not losing any information by deleting original correlated variable.
I can't find any scientific article on this, which implies I am most probably wrong. Can anyone help me out pointing out my mistake? Also, how to deal with highly correlated variables then?