Predicting winning team and probabilistic ratings in “Dota 2” and “Counter-Strike: Global Offensive” video games
I Makarov, D Savostyanov, B Litvyakov… - Analysis of Images …, 2018 - Springer
I Makarov, D Savostyanov, B Litvyakov, DI Ignatov
Analysis of Images, Social Networks and Texts: 6th International Conference …, 2018•SpringerIn this paper, we present novel winning team predicting models and compare the accuracy
of the obtained prediction with TrueSkill model of ranking individual players impact based on
their impact in team victory for the two most popular online games:“Dota 2” and “Counter-
Strike: Global Offensive”. In both cases, we present game analytics for predicting winning
team based on game statistics and TrueSkill.
of the obtained prediction with TrueSkill model of ranking individual players impact based on
their impact in team victory for the two most popular online games:“Dota 2” and “Counter-
Strike: Global Offensive”. In both cases, we present game analytics for predicting winning
team based on game statistics and TrueSkill.
Abstract
In this paper, we present novel winning team predicting models and compare the accuracy of the obtained prediction with TrueSkill model of ranking individual players impact based on their impact in team victory for the two most popular online games: “Dota 2” and “Counter-Strike: Global Offensive”. In both cases, we present game analytics for predicting winning team based on game statistics and TrueSkill.
Springer
