What Contributes to Success in MOBA Games? An Empirical Study of Defense of the Ancients 2

Xia, Bang; Wang, Huiwen; Zhou, Ronggang

doi:10.1177/1555412017710599

Cited by 36 publications

(19 citation statements)

References 42 publications

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“…Second is research on other MOBA games and how individual performance relates to team outcome. Xia, Wang, and Zhou (2017) conclude that the multiplayer killing indicator effectively predicts game outcomes. But they use the standard measure of kills (and deaths), rather than the win-probability-filtered ones we introduce in this paper.…”

Section: Literature Reviewmentioning

confidence: 85%

Smart kills and worthless deaths: eSports analytics for League of Legends

Maymin

2020

Journal of Quantitative Analysis in Sports

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Vast data on eSports should be easily accessible but often is not. League of Legends (LoL) only has rudimentary statistics such as levels, items, gold, and deaths. We present a new way to capture more useful data. We track every champion’s location multiple times every second. We track every ability cast and attack made, all damages caused and avoided, vision, health, mana, and cooldowns. We track continuously, invisibly, remotely, and live. Using a combination of computer vision, dynamic client hooks, machine learning, visualization, logistic regression, large-scale cloud computing, and fast and frugal trees, we generate this new high-frequency data on millions of ranked LoL games, calibrate an in-game win probability model, develop enhanced definitions for standard metrics, introduce dozens more advanced metrics, automate player improvement analysis, and apply a new player-evaluation framework on the basic and advanced stats. How much does an individual contribute to a team’s performance? We find that individual actions conditioned on changes to estimated win probability correlate almost perfectly to team performance: regular kills and deaths do not nearly explain as much as smart kills and worthless deaths. Our approach offers applications for other eSports and traditional sports. All the code is open-sourced.

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Section: Literature Reviewmentioning

confidence: 85%

Smart kills and worthless deaths: eSports analytics for League of Legends

Maymin

2020

Journal of Quantitative Analysis in Sports

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“…• Winning team prediction [46]. This task aims to predict which team will win before the game starts.…”

Section: Experiments Setupmentioning

confidence: 99%

What Makes a Good Team? A Large-scale Study on the Effect of Team Composition in Honor of Kings

Cheng

Yang

Tan

et al. 2019

The World Wide Web Conference

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Team composition is a central factor in determining the effectiveness of a team. In this paper, we present a large-scale study on the effect of team composition on multiple measures of team effectiveness. We use a dataset from the largest multiplayer online battle arena (MOBA) game, Honor of Kings, with 96 million matches involving 100 million players. We measure team effectiveness based on team performance (whether a team is going to win), team tenacity (whether a team is going to surrender), and team rapport (whether a team uses abusive language). Our results confirm the importance of team diversity and show that diversity has varying effects on team effectiveness: although diverse teams perform well and show tenacity in adversity, they are more likely to abuse when losing than less diverse teams. Our study also contributes to the situation vs. personality debate and show that abusive players tend to choose the leading role and players do not become more abusive when taking such roles. We further demonstrate the predictive power of features based on team composition in prediction experiments. 1 CCS CONCEPTS• Applied computing → Law, social and behavioral sciences.

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“…On the one hand, this research is focused on the cognitive aspects that are triggered and affected when playing online games, including but not limited to gamer motivations to play (Choi and Kim, 2004 ; Yee, 2006 ; Jansz and Tanis, 2007 ; Tyack et al, 2016 ), learning mechanisms (Steinkuehler, 2004 , 2005 ), and player performance and acquisition of expertise (Schrader and McCreery, 2008 ). On the other hand, players and their performance are classified in terms of in-game specifics, such as combat patterns (Drachen et al, 2014 ; Yang et al, 2014 ), roles (Eggert et al, 2015 ; Lee and Ramler, 2015 ; Sapienza et al, 2017 ), and actions (Johnson et al, 2015 ; Xia et al, 2017 ; Sapienza et al, 2018a ).…”

Section: Related Workmentioning

confidence: 99%

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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What Contributes to Success in MOBA Games? An Empirical Study of Defense of the Ancients 2

Cited by 36 publications

References 42 publications

Smart kills and worthless deaths: eSports analytics for League of Legends

Smart kills and worthless deaths: eSports analytics for League of Legends

What Makes a Good Team? A Large-scale Study on the Effect of Team Composition in Honor of Kings

Deep Neural Networks for Optimal Team Composition

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