Multiplayer Online Battle Arena Research Articles

A Further Test of the Impact of Online Gaming on Psychological Wellbeing and the Role of Play Motivations and Problematic Use

The impact of increased online gaming play time on psychological wellbeing was examined focusing on the Multiplayer Online Battle Arena (MOBA) game genre. This relationship was explored with respect to motivators for playing and resilience factors. A cross-sectional, online questionnaire design was employed with participants (N = 165) to examine the relationship between weekly average hours played and psychological wellbeing. Five previously reported motivators for playing were tested as mediating variables. In addition, exploratory analyses were conducted to determine the moderating effects of self-esteem and self-efficacy on ‘escapist’ gaming and psychological wellbeing. Results revealed a significant correlation with higher levels of play time associated with poorer psychological wellbeing. This relationship was partially mediated by ‘escapist’ motivation. Self-esteem was found to moderate the negative impact of ‘escapist’ gaming on psychological wellbeing. Research and the associated clinical implications are discussed.

Improving RTS Game AI by Supervised Policy Learning, Tactical Search, and Deep Reinforcement Learning

Constructing strong AI systems for video games is difficult due to enormous state and action spaces and the lack of good state evaluation functions and high-level action abstractions. In spite of recent research progress in popular video game genres such as Atari 2600 console games and multiplayer online battle arena (MOBA) games, to this day strong human players can still defeat the best AI systems in adversarial video games. In this paper, we propose to use a deep Convolutional Neural Network (CNN) to select among a limited set of abstract action choices in Real-Time Strategy (RTS) games, and to utilize the remaining computation time for game tree search to improve low-level tactics. The CNN is trained by supervised learning on game states labeled by Puppet Search, a strategic search algorithm that uses action abstractions. Replacing Puppet Search by a CNN frees up time that can be used for improving units' tactical behavior while executing the strategic plan. Experiments in the μRTS game show that the combined algorithm results in higher win-rates than either of its two independent components and other state-of-the-art μRTS agents. We then present a case-study that investigates how deep Reinforcement Learning (RL) can be used in modern video games, such as Total War: Warhammer, to improve tactical multi-agent AI modules. We use popular RL algorithms such as Deep-Q Networks (DQN) and Asynchronous AdvantageActor Critic (A3C), basic network architectures and minimal hyper-parameter tuning to learn complex cooperative behaviors that defeat the highest difficulty built-in AI in mediumscale scenarios.

Hierarchical Macro Strategy Model for MOBA Game AI

The next challenge of game AI lies in Real Time Strategy (RTS) games. RTS games provide partially observable gaming environments, where agents interact with one another in an action space much larger than that of GO. Mastering RTS games requires both strong macro strategies and delicate micro level execution. Recently, great progress has been made in micro level execution, while complete solutions for macro strategies are still lacking. In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games. Trained by the Hierarchical Macro Strategy model, agents explicitly make macro strategy decisions and further guide their micro level execution. Moreover, each of the agents makes independent strategy decisions, while simultaneously communicating with the allies through leveraging a novel imitated crossagent communication mechanism. We perform comprehensive evaluations on a popular 5v5 Multiplayer Online Battle Arena (MOBA) game. Our 5-AI team achieves a 48% winning rate against human player teams which are ranked top 1% in the player ranking system.

VisuaLeague: Player performance analysis using spatial-temporal data

In recent years, the phenomenon of eSports has been a growing trend and consequently, in addition to players, other groups of users, including coaches and analysts, took an interest in online video games and the data extracted from them. Among many types of video games, one of the most widely played is the MOBA (Multiplayer Online Battle Arena) League of Legend (LoL) game. Similary to traditional sports, players and coaches/analysts analyse all game events, such as, players’ movements, to understand how they play to define new strategies and improve their performance. Our main goal is to get a better understanding of which visualizations techniques are more adequate to handle this type of spatio-temporal information data, associated to player performance analysis in video games. To address this goal, we inquired players to identify the analytical questions they need to support for performance analysis and designed the VisuaLeague prototype for the visualization of in-game player trajectories, using animated maps, and events during a LoL match. This paper presents a user study to evaluate the adequacy of animated maps and the analytical strategies followed by players when using spatio-temporal data to analyse player performance. The results support the adequacy of using the animated maps technique to convey information to users in this context. Moreover, they also point out towards a high degree of importance given to the spatio-temporal components of the data for player performance analysis.

FROM UNSUNG HERO TO SAVAGE HERO: SHAPING CHARACTERS’ ROLES IN THE ADAPTATION BETWEEN LITERARY WORKS AND MOBILE LEGENDS: BANG BANG! MOBA

This paper examines adaptation study of famous heroes from various literary works (such as Mahabharata, Arthurian legend, Journey to the West) and a Multiplayer online battle arena (MOBA) ‘Mobile Legends: Bang Bang!’ (MLBB) by Moonton. This paper focuses on how fictional heroes and divine figures like Gatotkaca, Lancelot, Freya and Sun Go Kong are presented and shaped into different in-game roles. Mobile Legends: Bang Bang! a game designed for mobile phones, provides two opposing teams fighting one another to reach and destroy the enemy’s base while defending their own base for control. Descriptive analytical method with documentation obtained from books and research journals was used to analyze the media; in addition, the theory used was based on Damono’s theory of adaptation (2012) and Elleström’s modalities (2010) by analyzing the element changes in the adaptation study. The conclusion is that the heroes’ roles are shaped differently due to their true nature and motives from the original literary works.

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.

Measuring Female Gaming: Gamer Profile, Predictors, Prevalence, and Characteristics From Psychological and Gender Perspectives.

Research investigating female gaming is relatively scarce, and past research has demonstrated that men are more likely to be problematic gamers. Few studies have focused on female gamers in community samples, and those that have been published have mainly collected qualitative data in Europe. There is case study evidence suggesting clinicians are increasingly treating problem female gamers. The aim of this study is threefold: (i) to establish an international female gamer profile, (ii) to determine predictors associated with perceived internet gaming disorder (IGD), and (iii) to identify those who are potentially at risk of developing gaming addiction and its characteristics by applying a quantitative approach. A cross-sectional online survey was applied through international gaming forums recruiting 625 female gamers, assessing sociodemographics, gaming devices used and play genres, and a set of questionnaires on gaming [e.g., problem online gaming (e.g., the nine-item short-form scale to assess IGD: IGDS9-SF), female stereotypes (e.g., sex role stereotyping scale), and psychological symptoms (e.g., Symptom CheckList-27-plus)]. Female gamers from all continents reported the use of all videogames, especially popular online games using computers and consoles. The proportion of gamers with potential IGD was one per cent. Regression analyses identified several risk factors for increased scores on the IGDS9-SF, namely having achievement and social motivations, embodied presence and identification with the avatar, hostility and social phobia together with negative body image, playing Multiplayer Online Battle Arena games, Massively Multiplayer Online Role-Playing Games, and First-Person-Shooter games. Findings contribute to filling the gap in knowledge on female gaming, to aid in the applicability of problematic gaming measurements in female gamers, especially those who are at risk of problematic gaming. The purpose of this study is to enhance the validity of the current measures to diagnose problem gaming appropriately in both genders.

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.

Linking adaptation processes to team performance in high-tempo, high-stakes teamwork: a large-scale gaming perspective

This study surveys and synthesises prior research on the processes by which teams adapt when collaborating on high-tempo, high-stakes work. Of principal concern are adaptation processes within teams operating in extreme environments, whether precipitated by changes within the team itself (such as loss or gain of new members) or within the team’s operational environment (such as unplanned-for contingencies). Propositions concerning three aspects of adaptation are developed: changes in the distribution of workflow across team members, improvisation of team members’ individual roles, and emergent behaviours intended to compensate for fluctuations in team member performance. Issues and opportunities for addressing these propositions in highly instrumented environments are then identified, taking as a test case the multiplayer online battle arena combat game, League of Legends. This article concludes with a discussion of opportunities and challenges in using detailed and voluminous naturally occurring data from highly instrumented environments – whether real or virtual – to address the propositions presented here.

Psychometric properties of the Internet Gaming Disorder Test (IGDT-10) and problematic gaming behavior among Finnish vocational school students.

This study was conducted to investigate the psychometric properties of the Finnish version of the Internet Gaming Disorder Test (IGDT-10), identify the proportion displaying problematic gaming behavior (PGB) among Finnish vocational school students, and characterize the relationships between sociodemographic factors and gaming characteristics predicting PGB in this population. This cross-sectional study involving 773 students (mean age 17.5years). Socio-demographic and gaming behavior characteristics were established using a web-based survey. A confirmatory factor analysis (CFA) showed that the IGDT-10 test has appropriate psychometric properties. The proportion of students exhibiting PGB based on the DSM-5 cut-off threshold was 1.3%. Regression analysis showed that male gender, preference for gaming on a personal computer, gaming time, poor self-perceived health status, and a preference for the solo and multiplayer online battle arenas (MOBA) game genres related positively to PGB scores. These findings demonstrate the appropriateness of the IGDT-10 test for the assessment of problematic gaming among adults, and support further research on PGB in Finland.

Internet gaming disorder and psychiatric symptoms in Bengaluru, India: Treatment implication for promotion of user mental health

Background: Psychological distress has been seen among internet users with gaming. There is a need to evaluate the effect of internet gaming on psychological well-being. The present study assessed the depression, anxiety, and stress among users with internet gaming disorder (IGD). Methodology: Sixty users (58 males and 2 females) in the age group of 16–18 years were screened using IGD test for cases with disordered gaming. They were administered background data sheet as well as depression anxiety stress scale. Majority of them were playing multiplayer online battle arena game and first person shooter game. Results: The pattern of use was from 10 to 14 h a day. About 6.8% individuals had lifetime psychiatric history. There was significant positive relationship between the internet gaming and depression (0.412 at 0.05 level), anxiety (0.408 at 0.001 level), and stress (0.308 at 0.05 level) scale. Conclusion: It also implies the need to evolve intensive intervention program to manage internet gaming as well as other psychosocial dysfunctions.

Analysis of Movement Features in Multiplayer Online Battle Arenas

On-line games represent one of the primary instances of virtual environments, in which avatars share a synchronous and persistent virtual world with each other. A careful analysis and a deep understanding of mobility in online games is of paramount importance to properly design mobility models to accurately reproduce avatar movements. In fact, mobility models of avatar have a relevant impact on the architectural design of virtual environments, especially for those based on distributed and decentralized solutions. This paper analyses around 640 hours of mobility of an extremely popular online game genre, the Multiplayer Online Battle Arenas (MOBA). The analysis has revealed interesting characteristics with respect ot the area of interest of the players and their utilization of the game area. Many of the results confirm the speculations of MOBA players, in terms of the phases of the game and the general behaviours of the avatars. Ultimately, our analysis is aimed toward the creation of realistic mobility models for MOBA environments, as well as the development of AI agents for MOBA games.

Attack, Damage and Carry: Role Familiarity and Team Performance in League of Legends

Objective: This research studies the impact of role familiarity on team performance by examining performance of the “Carry” role in the Multi-player Online Battle Arena gaming environment, League of Legends™. Background: Roles are typically defined as stable patterns of expectations, relationships, and behaviors. As social constructs, roles therefore include notions of status, relationships with additional social actors, and of defined sets of behaviors tied to the assigned role. We hypothesize that the importance of role familiarity in teams is mediated by the nature and extent of team members’ experience working together in defined roles. Methods: The data set used for this study is from League of Legends’ Application Program Interface and consists of ranked match play from 2011–2016. Results: ANOVA and visualization techniques are used to explore match-level data in order to address the proposed research questions. The proportion of time the same team member is assigned to the “Carry” role (role familiarity) has a direct and positive impact on team performance. Conclusions: This study shows how objective, detailed data on teamwork may be used to provide insights into questions of the composition and performance of teams. Additionally, the results illustrate the importance of role familiarity in the performance of teams. Application: This research highlights the value and feasibility of studying virtual teams for new insights into team performance.

Exploring player experience in ranked League of Legends

ABSTRACTWhile video games provide different Player Experiences (PE), some genres can provide particularly unique PEs driven by their particular features. Such is the case of MOBA (Multiplayer Online Battle Arena) games, currently led in number of players and popularity by League of Legends. In spite of this popularity, PE in MOBA games remains largely unexplored. We aim to explore this gap by presenting a PE study that focuses in League of Legends and its player base. After surveying more than 400 players in the database of the largest eSports organisation in Spain, a series of tests were run from multiple perspectives using the PENS (Player Experience of Need Satisfaction) model and the SPGQ (Social Presence in Gaming Questionnaire) as response variables. Among our findings, we show how PE differs across different levels of competence (or rank) inside the game. When looking at how team play impact PE, results show how PENS dimensions remain unaffected while empathy is driven by playing with known teammates. Role selection, on the other hand, has an arguably insignificant impact in PE. Last but not least, an invariant behavioural engagement across all dimensions shows how players perceive team collaboration as an essential factor for success.

Real-time monitoring prefrontal activities during online video game playing by functional near-infrared spectroscopy.

A growing body of literature has suggested that video game playing can induce functional and structural plasticity of the brain. The underlying mechanisms, however, remain poorly understood. In this study, functional near-infrared spectroscopy (fNIRS) was used to record prefrontal activities in 24 experienced game players when they played a massively multiplayer online battle arena video game, League of Legends (LOL), under naturalistic conditions. It was observed that game onset was associated with significant activations in the ventrolateral prefrontal cortex (VLPFC) and concomitant deactivations in the dorsolateral prefrontal cortex (DLPFC) and frontal pole area (FPA). Game events, such as slaying an enemy and being slain by an enemy evoked region-specific time-locked hemodynamic/oxygenation responses in the prefrontal cortex (PFC). It was proposed that the VLPFC activities during LOL playing are likely responses to visuo-motor task load of the game, while the DLPFC/FPA activities may be involved in the constant shifts of attentional states and allocation of cognitive resources required by game playing. The present study demonstrated that it is feasible to use fNIRS to monitor real-time prefrontal activity during online video game playing.

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.

Non-Negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games

Multiplayer online battle arena is a genre of online games that has become extremely popular. Due to their success, these games also drew the attention of our research community, because they provide a wealth of information about human online interactions and behaviors. A crucial problem is the extraction of activity patterns that characterize this type of data, in an interpretable way. Here, we leverage the Non-negative Tensor Factorization to detect hidden correlated behaviors of playing in a well-known game: League of Legends. To this aim, we collect the entire gaming history of a group of about 1000 players, which accounts for roughly 100K matches. By applying our framework we are able to separate players into different groups. We show that each group exhibits similar features and playing strategies, as well as similar temporal trajectories, i.e., behavioral progressions over the course of their gaming history. We surprisingly discover that playing strategies are stable over time and we provide an explanation for this observation.

MOBA games: A literature review

This article aims to perform a literature review of the available research that focuses strictly on MOBA (multiplayer online battle arena) games. First, a review identifying the relevant papers published since 2011 is conducted, exploring them systematically to extract similarities, gaps and main findings. Results show how League of Legends is the most explored game, with player experience and toxic behaviour as popular topics for research. Second, as MOBA games remain underexplored by researchers despite their vast, enthusiast community, as well as projection and influence on contemporary game designers, a proposal on future lines for research is provided.

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.