Skill-based differences in spatio-temporal team behaviour in defence of the Ancients 2 (DotA 2)

Drachen, Anders; Yancey, Matthew; Maguire, John David; Chu, Derrek; Wang, Iris Yuhui; Mahlmann, Tobias; Schubert, Matthias; Klabajan, Diego

doi:10.1109/gem.2014.7048109

Cited by 84 publications

(75 citation statements)

References 17 publications

(35 reference statements)

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“…In particular, we analyze a well-known MOBA game: Defense of the ancient 2 (Dota 2). This type of game provides indeed key insights on how users behave and improve their skills over time to reach success [19]- [22]. By studying individual matches over time, we not only explore how players improve while playing consecutive matches, but also monitor how their performance changes based on the role they are impersonating [23].…”

Section: Introductionmentioning

confidence: 99%

Performance Dynamics and Success in Online Games

Sapienza

Peng

Ferrara

2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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Abstract-Online data provide a way to monitor how users behave in social systems like social networks and online games, and understand which features turn an ordinary individual into a successful one. Here, we propose to study individual performance and success in Multiplayer Online Battle Arena (MOBA) games. Our purpose is to identify those behaviors and playing styles that are characteristic of players with high skill level and that distinguish them from other players. To this aim, we study Defense of the ancient 2 (Dota 2), a popular MOBA game. Our findings highlight three main aspects to be successful in the game: (i) players need to have a warm-up period to enhance their performance in the game; (ii) having a long in-game experience does not necessarily translate in achieving better skills; but rather, (iii) players that reach high skill levels differentiate from others because of their aggressive playing strategy, which implies to kill opponents more often than cooperating with teammates, and trying to give an early end to the match.

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Section: Introductionmentioning

confidence: 99%

Performance Dynamics and Success in Online Games

Sapienza

Peng

Ferrara

2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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“…Although this attribute might depend on skill as is indicated by Drachen et al [3], this could also be caused by active play styles being more prevalent in higher-skilled matches. Unfortunately, the movement activity cannot simply be determined by tracking the total movement of players, as all characters are usually running back and forth even when they do not change their general position.…”

Section: Lane Partners and Solo Lanementioning

confidence: 89%

“…Yang et al [20] identify combat patterns to predict game outcomes with an accuracy of 80%. More recently, another contribution by Drachen et al [3] investigated skill-based differences in the spatio-temporal team behavior of Dota 2 matches. They find higher-skilled players to move more actively and closer to their teammates around the map.…”

Section: Related Workmentioning

confidence: 99%

Classification of Player Roles in the Team-Based Multi-player Game Dota 2

Eggert

Herrlich

Smeddinck

et al. 2015

Entertainment Computing - ICEC 2015

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“…MOBA players impersonate a specific character in the battle (a.k.a., hero), which has special abilities and powers based on its role, e.g., supporting roles, action roles, etc. The cooperation of teammates in MOBA games is essential to achieve the shared goal, as shown by prior studies [17,51]. Thus, teammates might strongly influence individual players' behaviors over time.…”

Section: Introductionmentioning

confidence: 89%

Deep Neural Networks for Optimal Team Composition

Sapienza¹,

Goyal²,

Ferrara³

2019

Front. Big Data

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Cooperation is a fundamental social mechanism, whose effects on human performance have been investigated in several environments. Online games are modern-days natural settings in which cooperation strongly affects human behavior. Every day, millions of players connect and play together in team-based games: the patterns of cooperation can either foster or hinder individual skill learning and performance. This work has three goals: (i) identifying teammates' influence on players' performance in the short and long term, (ii) designing a computational framework to recommend teammates to improve players' performance, and (iii) setting to demonstrate that such improvements can be predicted via deep learning. We leverage a large dataset from Dota 2, a popular Multiplayer Online Battle Arena game. We generate a directed co-play network, whose links' weights depict the effect of teammates on players' performance. Specifically, we propose a measure of network influence that captures skill transfer from player to player over time. We then use such framing to design a recommendation system to suggest new teammates based on a modified deep neural autoencoder and we demonstrate its state-of-the-art recommendation performance. We finally provide insights into skill transfer effects: our experimental results demonstrate that such dynamics can be predicted using deep neural networks.

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Skill-based differences in spatio-temporal team behaviour in defence of the Ancients 2 (DotA 2)

Cited by 84 publications

References 17 publications

Performance Dynamics and Success in Online Games

Performance Dynamics and Success in Online Games

Classification of Player Roles in the Team-Based Multi-player Game Dota 2

Deep Neural Networks for Optimal Team Composition

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