Exploratory Data Analysis on dataset divided by winners and losers
So, the question asks that during Exploratory Data Analysis whether it is better to analyse the data with both winning and losing tennis players in one data frame or split them into two. In this question, I am going to assume that you will be using this data in a classification task (do correct me if I am wrong!): to discriminate between winning and losing tennis players.
I believe the best approach would be to analyse with both winning and losing players together. The main reasons for it are:

When you are running this data through a model, you won't split the data by class (winning vs losing players)
More importantly, in the context of feature engineering, the usual rule of thumb normally is the you choose features which can greatly discriminate between the classes. So you can only do that by reviewing the feature distributions by classes to see if adding those features to the model will improve generalisation performance of the model.