Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

TY - UNPB

T1 - Win Prediction in Esports

T2 - Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

AU - Hodge, Victoria J.

AU - Devlin, Sam Michael

AU - Sephton, Nicholas John

AU - Block, Florian Oliver

AU - Drachen, Anders

AU - Cowling, Peter Ivan

PY - 2017/11/17

Y1 - 2017/11/17

N2 - 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.

AB - 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.

UR - http://adsabs.harvard.edu/abs/2017arXiv171106498H

M3 - Preprint

VL - cs:AI

BT - Win Prediction in Esports

PB - arXiv

ER -