Improved balance in multiplayer online battle arena games

Abstract

The Multiplayer Online Battle Arena (MOBA) game is a popular type for its competition between players. Due to the high complexity, balance is the most important factor to secure a fair competitive environment. The common way to achieve dynamic data balance is by constant updates. The traditional method of finding unbalanced factors is mostly based on professional tournaments, a small minority of all the games and not real time. We develop an evaluation system for the DOTA2 based on big data with clustering analysis, neural networks, and a small-scale data collection as a sample. We then provide an ideal matching system based on the Elo rating system and an evaluation system to encourage players to try more different heroes for a diversified game environment and more data supply, which makes for a virtuous circle in the evaluation system.