Why does Catboost outperform other boosting algorithms?

I have noticed while working with multiple datasets that catboost with its default parameters tends to outperform lightgbm or xgboost with its default parameters even on a tabular dataset with no categorical features.

I am assuming this has something to do with the way catboost constructs the decision trees but I just wanted to confirm this theory. If anyone could elaborate on why it performs better on non categorical data then that would be great! Thanks in advance!