  Competition is a primary driver of player satisfaction and engagement in
multiplayer online games. Traditional matchmaking systems aim at creating
matches involving teams of similar aggregated individual skill levels, such as
Elo score or TrueSkill. However, team dynamics cannot be solely captured using
such linear predictors. Recently, it has been shown that nonlinear predictors
that target to learn probability of winning as a function of player and team
features significantly outperforms these linear skill-based methods. In this
paper, we show that using final score difference provides yet a better
prediction metric for competitive balance. We also show that a linear model
trained on a carefully selected set of team and individual features achieves
almost the performance of the more powerful neural network model while offering
two orders of magnitude inference speed improvement. This shows significant
promise for implementation in online matchmaking systems.
