How to predict the player using random forest ML
To address such a problem, I would suggest creation of a custom target variable. 
Firstly, the transformation of names of players into dummy variables seems reasonable (Just make sure, the unique player is identified with the same first and last name combinations thereby, avoiding duplications and thus, having the correct dummy code for the player name).
Now, to create the target variable "wins" - 

Use the two player names - P1, P2 of the match as input features for your model.
Define the "wins" as 1 if P1 wins and 0 if P2 wins.
Run your model with this set up. 
When you want to create a tournament and predict the winner, the inputs will be your 2 players and other match features. If, "wins" is close to 1, it means your P1 wins and output that player name.
I have to predict the winner of the Australian Open 2020. My dataset has these features: Location / Tournament / Date / Series / Court / Surface / Round / Winner / Loser etc. 
I trained my model using just these features 'Victory','Series','Court','Surface','WinRank','LoseRank','WPts','LPts','Wsets','Lsets','Weather' and I have a 0.93 accuracy but now I have to predict the name of the winner and I don't have any idea how to do it based on the model that I trained. 
Example:  If I have Dimitrov G. vs Simion G using random forest the model has to give me one of them as the winner of the match. 
I transformed the names of the players in dummy variables but after that, I don't know what to do? 
Can anyone give me just an idea of how could I predict the winner? so I can create a Tournament, please?