  Esports has emerged as a popular genre for players as well as spectators,
supporting a global entertainment industry. Esports analytics has evolved to
address the requirement for data-driven feedback, and is focused on
cyber-athlete evaluation, strategy and prediction. Towards the latter, previous
work has used match data from a variety of player ranks from hobbyist to
professional players. However, professional players have been shown to behave
differently than lower ranked players. Given the comparatively limited supply
of professional data, a key question is thus whether mixed-rank match datasets
can be used to create data-driven models which predict winners in professional
matches and provide a simple in-game statistic for viewers and broadcasters.
Here we show that, although there is a slightly reduced accuracy, mixed-rank
datasets can be used to predict the outcome of professional matches, with
suitably optimized configurations.
