Popular Multiplayer Online Battle Arena Research Articles

Behavioral dedication, constraint or obligation? A tripartite model of active participation in multiplayer online battle arena game community

PurposeActive participation is critical to the survival and development of the multiplayer online battle arena (MOBA) game community. However, this issue has not received much attention in the information systems literature. To address this issue, we develop a tripartite model that accounts for the roles of behavioral dedication, constraint, obligation mechanisms on active participation in the MOBA community.Design/methodology/approachThe research model is empirically validated by online survey data among 971 users of a popular MOBA community.FindingsThe results show that perceived enjoyment, perceived escapism, and affective commitment are key behavioral dedication factors, which further promote active participation in the MOBA community. In addition, past investment, self-efficacy for change, and calculative commitment are important behavioral constraint factors, which ultimately influence active participation in the MOBA community. Finally, subjective norm, group norm, social identity, and normative commitment are influential behavioral obligation factors, which in turn facilitate active participation in the MOBA community.Originality/valueThis study contributes to the theoretical understanding of active participation in the MOBA community and offers practical guidance for promoting active participation in the community.

Predicting Events in MOBA Games: Prediction, Attribution, and Evaluation

The multiplayer online battle arena (MOBA) games have become increasingly popular in recent years. Consequently, many efforts have been devoted to providing pre-game or in-game predictions for them. However, these works are limited in the following two aspects: 1) the lack of sufficient in-game features; 2) the absence of interpretability in the prediction results. These two limitations greatly restrict the practical performance and industrial application of the current works. In this work, we collect and release a large-scale dataset containing rich in-game features for the popular MOBA game Honor of Kings. We then propose to predict four types of important events in an interpretable way by attributing the predictions to the input features using two gradient-based attribution methods: Integrated Gradients and SmoothGrad. To evaluate the explanatory power of different models and attribution methods, a fidelity-based evaluation metric is further proposed. Finally, we evaluate the accuracy and Fidelity of several competitive methods on the collected dataset to assess how well machines predict events in MOBA games.

What makes an ideal team? Analysis of Popular Multiplayer Online Battle Arena (MOBA) games

A good solo player is not necessarily a good team player. Team games add an extra aspect to games: teamwork, which may increase the skill ceiling to master the game. The responsibility towards the outcome of the game will be divided among all the players in the team, where having more players might make the game easier as the responsibility of each player towards the outcome of the game is lower. Multiplayer online battle arena (MOBA) games are one of the most popular and competitive e-sports available today, with multi-million dollar tournaments being held annually. MOBA games are often played with two teams of five players. In this paper, we would like to study various MOBA games to see how different numbers of players on each side can affect the game’s competitiveness and difficulty. To analyze the difficulty of the game, we will be evaluating the games using motion in mind measures and implementing weightage to the measures in order to understand the motives of the players. This can help to answer why five players are the optimal setting for MOBA games. We predict that the ideal number of players ensures the game does not compromise its difficulty and fairness to ensure that it stays competitive.

The Influence of Social Ties on Performance in Team-Based Online Games

Social ties are the invisible glue that keeps together human ecosystems. Despite the massive amount of research studying the role of social ties in communities (groups, teams, etc.) and society at large, little attention has been devoted to study their interplay with other human behavioral dynamics. Of particular interest is the influence that social ties have on human performance in collaborative team-based settings. Our research aims to elucidate the influence of social ties on individual and team performance dynamics. We will focus on a popular multiplayer online battle arena (MOBA) collaborative team-based game, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Defense of the Ancients 2 (Dota 2)</i> , a rich data set with millions of players and matches. Our research reveals that, when playing with their friends, individuals are systematically more active in the game as opposed to taking part in a team of strangers. However, we find that increased activity does not homogeneously lead to an improvement in players’ performance. Despite being beneficial to low-skill players, playing with friends negatively affects the performance of high-skill players. Our findings shed light on the mixed influence of social ties on performance and can inform new perspectives on virtual team management and behavioral incentives.

Which Heroes to Pick? Learning to Draft in MOBA Games With Neural Networks and Tree Search

Hero drafting is essential in multiplayer online battle arena (MOBA) game playing as it builds the team of each side and directly affects the match outcome. State-of-the-art drafting methods fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the multiround nature of a MOBA 5v5 match series, i.e., two teams play best-of- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> and the same hero is only allowed to be drafted once throughout the series. In this article, we formulate the drafting process as a multiround combinatorial game and propose a novel drafting algorithm based on neural networks and Monte Carlo tree search, named JueWuDraft. Specifically, we design a long-term value estimation mechanism to handle the best-of- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> drafting case. Taking <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Honor of Kings</i> , one of the most popular MOBA games at present, as a running case, we demonstrate the practicality and effectiveness of JueWuDraft when compared to state-of-the-art drafting methods.

Memprediksi Pola Ban Hero Pada Game Mobile Legends Menggunakan Algoritma Apriori

In this digital era, the development of video games is so rapid, from console-based to smartphone devices. One of the trending video game genres is the Multiplayer Online Battle Arena (MOBA) with one of the most popular MOBA games, the Mobile Legends game. In winning a match in a Mobile Legends game, a good game strategy is needed from each team to defend the base and destroy the opponent's base, one of which is by Ban Hero or banning some existing heroes so that they cannot be used both for the opposing team and for the opposing team. own team. Therefore, this study was conducted to predict the pattern of hero tires using the Apriori algorithm which was carried out on 9 attributes of Hero Mobile Legends. From the results it is known that CHOU Hero is more widely used and not banned compared to 8 other Heroes

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings.

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games.

Motivational Profiling of League of Legends Players.

Player motivation is a key research area within games research, with the aim of understanding how the motivation of players is related to their experience and behavior in the game. We present the results of a cross-sectional study with data from 750 players of League of Legends, a popular Multiplayer Online Battle Arena game. Based on the motivational regulations posited by Self-Determination Theory and Latent Profile Analysis, we identify four distinct motivational profiles, which differ with regards to player experience and, to a lesser extent, in-game behavior. While the more self-determined profiles “Intrinsic” and “Autonomous” report mainly positive experience-related outcomes, a considerable part of the player base does not. Players of the “Amotivated” and “External” profile derive less enjoyment, experience more negative affect and tension, and score lower on vitality, indicating game engagement that is potentially detrimental to players' well-being. With regards to game metrics, minor differences in the rate of assists in unranked matches and performance indicators were observed between profiles. This strengthens the notion that differences in experiences are not necessarily reflected in differences in behavioral game metrics. Our findings provide insights into the interplay of player motivation, experience, and in-game behavior, contributing to a more nuanced understanding of player-computer interaction.

Win Prediction in Multiplayer Esports: Live Professional Match Prediction

Esports are competitive videogames watched by audiences. Most esports generate detailed data for each match that are publicly available. Esports analytics research is focused on predicting match outcomes. Previous research has emphasized prematch prediction and used data from amateur games, which are more easily available than those from professional level. However, the commercial value of win prediction exists at the professional level. Furthermore, predicting real-time data is unexplored, as is its potential for informing audiences. Here, we present the first comprehensive case study on live win prediction in a professional esport. We provide a literature review for win prediction in a multiplayer online battle arena (MOBA) esport. This article evaluates the first professional-level prediction models for live DotA 2 matches, one of the most popular MOBA games, and trials it at a major international esports tournament. Using standard machine learning models, feature engineering and optimization, our model is up to 85% accurate after 5 min of gameplay. Our analyses highlight the need for algorithm evaluation and optimization. Finally, we present implications for the esports/game analytics domains, describe commercial opportunities and practical challenges, and propose a set of evaluation criteria for research on esports win prediction.

Effects of Mobile Platform on Female Engagement in MOBA Games

Previous research shows that fewer female players participate in competitive games than male players. However, it has been reported that there are more female than male players in King of Glory (KoG), one of the most popular multiplayer online battle arena (MOBA) games on the mobile platform in China. This study aims to investigate how KoG captures the interest of female players at a higher level than other games. We compared the game design of KoG with League of Legends (LoL), one of the most popular MOBA games on the PC platform. We followed up with a semi-structured interview study with 20 participants about their gameplay experiences on the two different platforms. Our analysis indicates that mobility, sociability, and lower barrier to entry are the main factors that drove female players to participate in KoG.

Deep Neural Networks for Optimal Team Composition.

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.

Does Streaming Esports Affect Players’ Behavior and Performance?

In this work, we analyze what effect streaming gameplay on Twitch has on players’ in-game behavior and performance. We hypothesized that streaming can act as a form of implicit incentive to boost players’ performance and engagement. To test this hypothesis, we continuously collected data about all Twitch streams related to a popular Multiplayer Online Battle Arena (MOBA) game, League of Legends (LoL), and data of all LoL matches played during the same time frame, and cross-mapped the two data sets. We found that, counterintuitively, streaming significantly deteriorates players’ in-game performance: This may be due to the burden of carrying out two cognitively intensive activities at the same time, namely, playing the game and producing its commentary for streaming purposes. On the other hand, streaming increases engagement keeping players in significantly longer game sessions. We investigate these two effects further, to characterize how they vary upon individual characteristics.

Individual performance in team-based online games.

Complex real-world challenges are often solved through teamwork. Of special interest are ad hoc teams assembled to complete some task. Many popular multiplayer online battle arena (MOBA) video-games adopt this team formation strategy and thus provide a natural environment to study ad hoc teams. Our work examines data from a popular MOBA game, League of Legends, to understand the evolution of individual performance within ad hoc teams. Our analysis of player performance in successive matches of a gaming session demonstrates that a player’s success deteriorates over the course of the session, but this effect is mitigated by the player’s experience. We also find no significant long-term improvement in the individual performance of most players. Modelling the short-term performance dynamics allows us to accurately predict when players choose to continue to play or end the session. Our findings suggest possible directions for individualized incentives aimed at steering the player’s behaviour and improving team performance.

What Makes a Champion: The Behavioral and Neural Correlates of Expertise in Multiplayer Online Battle Arena Games

ABSTRACTDespite the popularity of multiplayer online battle arena (MOBA) games, academic research on MOBA is still very limited. The current study aimed to fill this gap by exploring the behavioral and neural correlates of expertise for the most popular MOBA game, League of Legends (LOL). Three groups of LOL players with different expertise levels were recruited, including professional players, background-matched trainees, and age-matched students with no systematic LOL trainings. A series of behavioral tests and questionnaires was used to evaluate their general cognitive skills and their LOL-specific abilities were extracted from the neural activities (Electroencephalographs (EEG)s and Electrocardiographs (ECG)s) recorded during LOL matches. Using the behavioral features, both the students and the trainees could be significantly separated from the professional players (trainees vs. professional players, 61.11%; students vs. professional players, 66.67%), whereas the students and the trainees cannot be distinguished. Using the neural features, all three groups could be well separated with higher classification accuracies (students vs. trainees: 88.24%; trainees vs. professional players, 93.33%; students vs. professional players, 93.75%). The most contributing behavioral and neural indices were revealed as well, including multiple-object tracking capability, mental concentration, visuospatial attention ability, etc. The authors’ results for the first time showed the possibility of recognizing MOBA expertise using both behavioral and neural measurements and provided a framework for evaluation, selection, and training of professional MOBA players.

Relating conversational topics and toxic behavior effects in a MOBA game

Multiplayer Online Battle Arena (MOBA) games are very competitive and victory relies on effective teamwork. Toxic behavior is a common concern, as it corrodes team effort and harms game ambiance. Related work has focused on the automatic detection of toxic behavior, mostly based on features from the communication channels used by players. In this paper, we investigate the conversational patterns used by players of a popular MOBA game, League of Legends, and their effects on performance and contamination. We identify the main conversation topics exchanged by players using in-game chats; characterize the behavior of each type of player according to these topics; and develop an in-depth analysis of how they affect game performance and toxic contamination. Our findings show that some topics are directly related to toxic behavior. The allies of a toxic player are in general more affected by toxic behavior than the opponents, and in some cases, they act together as a source of toxic behavior. Opponents are more affected when the toxic behavior is directly targeted at them (e.g. racist slurs). Players with no significant contact with toxic players tend to be more positive, concentrating on game tactics and socialization.

Russophobia in DotA 2

Online gaming has been a fascinating field of study for the last ten years, especially in the field of socialization (Kolo &amp; Baur, 2004) or even language use and language learning (Thorne, Black &amp; Sykes, 2009). It has become clear that gamers are able to perform processes of identification in completely new ways in these particular contexts, yet forums linked to specific games become a new source of metapragmatic or metadiscursive utterances. Through their experiences in the game, users make comments, assumptions and draw conclusions in order to ‘do identity’ and separate themselves from Others. The aim of this paper will be to analyse the discourses produced in a corpus of forum discussions linked to “DotA 2”, a popular MOBA (multiplayer online battle arena) game where players from every country of the world gather, which leads to specific forms of discrimination—especially towards Russian gamers. In order to analyse these discursive productions and their semantic and pragmatic impacts, we will use three different approaches in order to triangulate our results: a lexicometric analysis (Garric &amp; Capdevielle-Mougnibas, 2009), the semantic study of argumentative possibilities (Galatanu, 2009) and the mobilization of the proximization model (Cap, 2010), in order to understand the semantic variations and dynamics that are at use when gamers publish discourses about Russian players. In particular, we wish to explore how these precise discourses about Russian players are drawing on pragmatics of common sense (Sarfati, 2011), insofar as they rely on prediscourses (Paveau, 2006) to maintain pragmatic effects which imply cognitive impacts on speakers (Maillat &amp; Oswald, 2009) as well as on the interdiscourses at use (Garric &amp; Longhi, 2013).

Impulsivity in Multiplayer Online Battle Arena Gamers: Preliminary Results on Experimental and Self-Report Measures

Background and aimsMultiplayer Online Battle Arena (MOBA) games have become the most popular type of video games played worldwide, superseding the playing of Massively Multiplayer Online Role-Playing Games and First-Person Shooter games. However, empirical studies focusing on the use and abuse of MOBA games are still very limited, particularly regarding impulsivity, which is an indicator of addictive states but has not yet been explored in MOBA games. In this context, the objective of the present study is to explore the associations between impulsivity and symptoms of addictive use of MOBA games in a sample of highly involved League of Legends (LoL, currently the most popular MOBA game) gamers.MethodsThirty-six LoL gamers were recruited and completed both experimental (Single Key Impulsivity Paradigm) and self-reported impulsivity assessments (s-UPPS-P Impulsive Behavior Scale, Barratt Impulsiveness Scale), in addition to an assessment of problematic video game use (Problematic Online Gaming Questionnaire).ResultsResults showed links between impulsivity-related constructs and signs of excessive MOBA game involvement. Findings indicated that impaired ability to postpone rewards in an experimental laboratory task was strongly related to problematic patterns of MOBA game involvement. Although less consistent, several associations were also found between self-reported impulsivity traits and signs of excessive MOBA game involvement.ConclusionsDespite these results are preliminary and based upon a small (self-selected) sample, the present study highlights potential psychological factors related to the addictive use of MOBA games.

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Multiplayer Online Battle Arena (MOBA) games are very competitive and victory relies on effective teamwork. Toxic behavior is a common concern, as it corrodes team effort and harms game ambiance. Related work has focused on the automatic detection of toxic behavior, mostly based on features from the communication channels used by players. In this paper, we investigate the conversational patterns used by players of a popular MOBA game, League of Legends, and their effects on performance and contamination. We identify the main conversation topics exchanged by players using in-game chats; characterize the behavior of each type of player according to these topics; and develop an in-depth analysis of how they affect game performance and toxic contamination. Our findings show that some topics are directly related to toxic behavior. The allies of a toxic player are in general more affected by toxic behavior than the opponents, and in some cases, they act together as a source of toxic behavior. Opponents are more affected when the toxic behavior is directly targeted at them (e.g. racist slurs). Players with no significant contact with toxic players tend to be more positive, concentrating on game tactics and socialization.