Proceedings of the Sixteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20) Trouncing in Dota 2: An Investigation of Blowout Matches Markos Viggiato, Cor-Paul Bezemer Electrical and Computer Engineering Department University of Alberta {viggiato, bezemer}@ualberta.ca Abstract Huge amounts of data have been generated from esports, allowing us to extract important insights, which is often re- With an increasing popularity, Multiplayer Online Battle ferred to as game analytics (El-Nasr, Drachen, and Canossa Arena games where two teams compete against each other, such as Dota 2, play a major role in esports tournaments, 2016). A vast body of work has investigated different game attracting millions of spectators. Some matches (so-called aspects, such as game outcome prediction (Ravari, Bakkes, blowout matches) end extremely quickly or have a very and Spronck 2016; Ravari et al. 2017; Makarov et al. 2017), large difference in scores. Understanding which factors lead recommendation systems (Hanke and Chaimowicz 2017; to a victory in a blowout match is useful knowledge for Looi et al. 2018), automatic extraction of game events (Luo, players who wish to improve their chances of winning and Guzdial, and Riedl 2019), and team encounters (Schubert, for improving the accuracy of recommendation systems for Drachen, and Mahlmann 2016). heroes. In this paper, we perform a comparative study be- In this work, we focus on the Dota 2 game, where each tween blowout and regular matches. We study 55,287 past team of 5 players must choose one side (Radiant or Dire). professional Dota 2 matches to (1) investigate how accurately we can predict victory using only pre-match features and (2) By inspecting professional Dota 2 matches, we note that explain the factors that are correlated with the victory. We some of them end very quickly or have a very large dif- investigate three machine learning algorithms and find that ference in teams’ final scores. We refer to these types of Gradient Boosting Machines (XGBoost) perform best with an matches as time blowout matches and score blowout matches Area Under the Curve (AUC) of up to 0.86. Our results show (vs. regular matches). Although a blowout match might be that the experience of the player with the picked hero has a seen as “expected” (e.g., because one team is considered different importance for blowout and regular matches. Also, stronger than the other one), it might also indicate imbal- hero attributes are more important for blowouts with a large ances in the game’s gameplay, which can, for example, fa- score difference. Based on our results, we suggest that players vor players who chose one team or another (Radiant or (1) pick heroes with which they achieved a high performance Dire) (Gopya 2020). Understanding how blowouts differ in previous matches to increase their chances of winning and (2) focus on heroes’ attributes such as intelligence to win with from regular matches can be useful knowledge for players a large score difference. who wish to increase their chances of winning. In this paper, we perform a comparative analysis between blowout and regular matches with regard to the following Introduction two aspects: (1) the performance of win prediction models The gaming industry has become a multi-billion dollar in- and (2) the explanation of which factors are correlated with dustry in recent years, experiencing a sharp growth and an a victory. We study 55,287 past Dota 2 professional matches expected revenue of $196 billion dollars by 2022 (Webb and seek to answer the following research questions (RQs): 2019). Esports (or electronic sports) is an organized form RQ1: How well can we predict victory in blowout and of competitively playing video games, which has played a regular Dota 2 matches? major role within the gaming industry. Multiplayer Online We first investigate whether we can find high-performing Battle Arena (MOBA) games, where two teams compete models to predict victory in different types of Dota 2 against each other, is a popular genre in esports, with tour- matches before identifying the factors associated with vic- naments that offer multi-million dollar prize pools and are tory. We found that XGBoost provides the best performance watched by millions of spectators (Schubert, Drachen, and in blowout and regular matches, with an Area Under the Mahlmann 2016; Block et al. 2018). Examples of very pop- Curve (AUC) of up to 0.86. ular MOBA games are Defense of the Ancients 2 (Dota 2) RQ2: Which factors are correlated with victory in and League of Legends (LoL). blowout and regular Dota 2 matches? Copyright c 2020, Association for the Advancement of Artificial Comparing which factors are associated with the victory in Intelligence (www.aaai.org). All rights reserved. blowout and regular matches is important as it can help play- 294 ers focus on specific aspects that increase their chance of heroes can use, which can be in different forms, such as winning a match and support new recommendation systems to damage the opponent or help allies (Demediuk et al. for heroes. Our models show that the up-to-date win rate of 2019). Abilities can be developed along the match as the the players is an important factor for victory in blowout and player gains experience points and gold (Eggert et al. 2015; regular matches. However, only for score blowouts (matches Drachen et al. 2014). Thus, as the player advances levels, with a large score difference), heroes’ features (e.g., hero’s their hero abilities can be improved or even new abilities role and the intelligence attribute) are important factors. might appear (Drachen et al. 2014). Each hero has one or Our study makes three major contributions: more roles in the match and the player should be aware of • We provide a practical, high-achieving model to predict the chosen hero’s role to make the most effective use of it. victory in Dota 2 using only pre-match information (i.e., There is a total of 9 roles, such as: Carry (heroes that have information available right before the match begins). the greatest increase in power throughout the match, be- ing responsible, many times, for the team victory); Support • We identified the most important factors that are cor- (heroes that are responsible for supporting their partners by related with victory in blowout and regular matches in keeping them alive and allowing them to earn more experi- Dota 2. ence points and gold); and Durable (heroes that are able to • We provide access to the data analysis code1 and the data2 resist to a lot of damage from the enemy and usually have with the up-to-date changelog and historical attribute val- large amounts of health generation) (Semenov et al. 2016). ues of each hero, historical statistics of heroes and players, Dota 2 studies. Several studies addressed different aspects and the computed features. of Dota 2. Katona et al. (2019) built a deep neural network to predict a hero’s death in a Dota 2 match within a window Background and Related Work of 5 seconds using gameplay features and professional/semi- In this section, we provide a background on the Dota 2 professional matches. Their findings show the model has a gameplay and outline prior work on Dota 2. precision of 0.377 and a recall of 0.725 when predicting the Dota 2 Gameplay. Dota (Defence of the Ancients)2isan death of any of the 10 players within the next 5 seconds. Luo, action Real-Time Strategy (RTS) game,3 sometimes referred Guzdial, and Riedl (2019) proposed an accessible method to as a Multiplayer Online Battle Arena (MOBA) game be- to extract events from Dota 2 gameplay videos with a Con- cause it combines elements from the RTS genre with tower volution Neural Network (CNN). Using techniques such as defense elements (Rondina 2018). Dota 2 is the successor of transfer learning, zero-shot and network pruning, the method Defense of the Ancients, a mod for Warcraft 3. is capable of extracting 10 events, such as the use of the A Dota 2 match consists of a battle between two teams Black King Bar item and tower destructions. Demediuk et (Radiant and Dire). Each team is composed of 5 players, al. (2019) provided a method to classify and label individual each one controlling a unique character (hero). The ultimate roles for each hero in Dota 2 using non-performance met- goal of the game is to destroy the opponent team’s ancient, rics of the types: map movement, resource priority, and abil- which is the main structure in Dota 2. The idea is that each ity prioritisation. Hanke and Chaimowicz (2017) proposed team should move along three lanes to reach the enemy’s an- a recommendation system to support hero selection. The cient while facing battles along the way with different crea- authors used association rules to suggest heroes and evalu- tures and having to destroy the opponent team’s towers be- ated the system with a neural network capable of predicting side the battles with opponent’s heroes and other creatures. the winner team. Their recommendation system presented The score of each team corresponds to the death count of 74.9% success rate. Looi et al. (2018) also proposed a rec- that team, i.e., the number of times all of a team’s entities, ommendation system but for Dota 2 items. The system is including its heroes and non-playable characters, killed an based on commonly used purchasing strategies. The authors opponent’s character. The Dota 2 match can be played in dif- used 3 recommender systems, based on rules, logistic re- ferent modes, which affect the way the players pick heroes.