Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games
This provides a practical solution for esports analytics by enabling win prediction for professional matches, addressing the limited availability of professional data, though it is incremental in nature.
The paper tackled the problem of predicting winners in professional esports matches using mixed-rank datasets, showing that despite a slight reduction in accuracy, these datasets can be effectively used with optimized configurations.
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.