Personalization applications need prior information about a user, and popular techniques that use neural networks need data in a numerical format. This work explores three node embedding techniques to represent player behaviour in a compact vector form. I used data from the game DOTA 2 to produce vectors using three graph embedding methods, developed a testing framework, and conducted 270 experiments to explore the effect of different parameters on vector quality. I explored different values for parameters, including the number of vector dimensions, the number of games, the types of interactions used in training, and step size for updating vectors over time. The results show that using node2vec and a vector dimension of 512 outperforms the other methods in 13 of the 15 parameter variations that I tested. node2vec also computed the vectors 60 and 10 times faster than LINE and TGN, respectively, on the same amount of data.