How to train a model with data with only one label
Sounds to me you need to shuffle that.  The dataset you have have inherent information coded in the structure of the data ( Player 1 wins).  You have no way to recreate this information at runtime.  
What you want is a dataset where the order of the player information is not important , and a label 0/1 determining if player 1 or player 2 will win.
I am trying to build a model to predict the outcome (win or loose) of a tennis match, as an exercise. I am using Python, Pandas and scikit-learn.
The dataset I have has the two players ID and the result of the match, among other quantities.
In my case, the way the database is organized, has always the Player1 as the winner and the Player2 as the looser. So, if I have to label the data, it will always be the same label (1, for instance).
What do you think is better: 

to try to train a model with a single-valued training ser (for instance a 1-label SVM) 
to randomly shuffle the data, in order to place some of the Player2 as the Player1 and viceversa, and so to have another label (0, for instance)?

Thanks a lot!