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 -