Outcome prediction of DOTA2 using machine learning methods

Abstract

With the wide spreading of network and capital inflows, Electronic Sport (ES) is developing rapidly in recent years and has become a competitive sport that cannot be ignored. Compared with traditional sports, the data of this industry is large in size and has the characteristics of easy-accessing and normalization. Based on these, data mining and machine learning methods can be applied to improve players' skills and help players make strategies. In this paper, a new approach predicting the outcome of an electronic sport DOTA2 was proposed. In earlier studies, the heroes' draft of a team was represented by unit vectors or its evolution, so the complex interactions among heroes were not captured. In our approach, the outcome prediction was performed in two steps. In the first step, Heroes in DOTA2 were quantified from 17 aspects in a more accurate way. In the second step, we proposed a new method to represent a heroes' draft. A priority table of 113 heroes was created based on the prior knowledge to support this method. The evaluation indexes of several machine learning methods on this task have been compared and analyzed in this paper. Experimental results demonstrate that our method was more effective and accurate than previous methods.