Trouncing in Dota 2: An Investigation of Blowout Matches

Abstract

compete against each other, such as Dota 2, play a major role in esports tournaments, attracting millions of spectators. Some matches (so-called blowout matches) end extremely quickly or have a very large difference in scores. Understanding which factors lead to a victory in a blowout match is useful knowledge for players who wish to improve their chances of winning and for improving the accuracy of recommendation systems for heroes. In this paper, we perform a comparative study between blowout and regular matches. We study 55,287 past professional Dota 2 matches to (1) investigate how accurately we can predict victory using only pre-match features and (2) explain the factors that are correlated with the victory. We investigate three machine learning algorithms and find that Gradient Boosting Machines (XGBoost) perform best with an Area Under the Curve (AUC) of up to 0.86. Our results show that the experience of the player with the picked hero has a different importance for blowout and regular matches. Also, hero attributes are more important for blowouts with a large score difference. Based on our results, we suggest that players (1) pick heroes with which they achieved a high performance in previous matches to increase their chances of winning and (2) focus on heroes’ attributes such as intelligence to win with a large score difference.