Toward Successful Esports Team: How Does National Diversity Affect Multiplayer Online Battle Arena Video Games

Today, esports teams in multiplayer online battle arena (MOBA) games are often composed of players from around the world. The paper asks whether a greater national heterogeneity of professional esports teams means their higher effectiveness. Desk research data of 13 tournaments of Dota 2 game held in 2011-2018 is used to calculate the teams’ win ratio, i.e., the ratio of skirmishes (in all matches) won to the total number of skirmishes (match is a series of skirmishes). Hence, effectiveness is understood not as ranks or matches won, but as the lowest possible number of lost skirmishes. Multinational teams achieved a higher win ratio, compared to nationally homogenous teams and the analysis includes the role of coaches’ nationalities. Working groups, cognitive diversity, and similarity/attraction theories are used to signal potential reasons and consequences of diversity on team performance. This exploratory study indicates future research threads on esports teams’ national diversity

Psychological Skills Training Manual for eSports Athletes

eSports are a new category of competitive games, where groups of players compete against others in competitive video games on personal computers and gaming consoles. These games can be individually based or team oriented. This project applies theoretical and empirical research in sports psychology to develop a psychological skills training manual for eSports athletes and coaches. In particular, tenets of Achievement Goal Theory and relevant research are reviewed and discussed. This manual focuses on the select psychological skill strategies of goal setting, imagery and positive self talk with an aim to minimize potential adverse affects, cognitions, and behaviors in eSport athletes. eSports athletes have been found to report symptoms depression, anxiety, and difficulties with socialization. An athlete trained in imagery and other cognitive techniques (e.g., negative thought stoppage) can reduce performance related anxiety typically associated with fear of failure. Also, appropriately short and long term goals that are focused primarily on learning and self referenced improvement (i.e., mastery approach) have the capability of improving self confidence and continued motivation. Thus, this manual, when successfully applied, will provide athletes with a selection of skills to enhance their functioning in achievement situations, and these more positive psychological states should be associated with an improvement in performance

An Ecosystem Framework for the Meta in Esport Games

This paper examines the evolving landscape of modern digital games, emphasizing their nature as live services that continually evolve and adapt. In addition to engaging with the core gameplay, players and other stakeholders actively participate in various game-related experiences, such as tournaments and streaming. This interplay forms a vibrant and intricate ecosystem, facilitating the construction and dissemination of knowledge about the game. Such knowledge flow, accompanied by resulting behavioral changes, gives rise to the concept of a video game meta. Within the competitive gaming context, the meta represents the strategic and tactical knowledge that goes beyond the fundamental mechanics of the game, enabling players to gain a competitive advantage. We present a review of the state-of-the-art of knowledge for game metas and propose a novel model for the meta knowledge structure and propagation that accounts for this ecosystem, based on a review of the academic literature and practical examples. By exploring the dynamics of knowledge exchange and its influence on gameplay, the review presented here sheds light on the intricate relationship between game evolution, player engagement, and the associated emergence of game meta

Fighting \u27Stance\u27: The Role of Conversational Positioning in League of Legends (Multiplayer Online Battle Arena) Discourse

For researchers, the study of video game players - how they behave, interact, and cooperate in a virtual world – presents a challenge: what methodologies are best suited to approaching these interactions? From a sociolinguistic approach, how do gamers converse, and what do these conversations reveal about epistemic, affective, and political relationships? This study uses John DuBois’ Stance Theory (2007) and recent modifications of it (Kiesling 2022), to analyze data gathered from the popular multiplayer online battle-arena (MOBA) game League of Legends. It focuses on in-game interlocutors’ conversation samples to show their positioning, intersubjective alignment, and evaluation of a constantly changing speech environment. DuBois’ Stance Triangle permits visualization of the stances taken within such chat-room interactions that focus on player comments concerning the game, game-playing, and other gamers (as well as themselves). In the search for stance identity, DuBois’ model specifically seeks to understand the alignment between interlocutors, the evaluation each interlocutor makes of the stance object, and the position each interlocutor takes with regard to that object. This study builds on the work of researchers in stance-based analysis of gaming discourse (Sierra 2016), multimodality (Collister 2012), and language acquisition (Bakos 2018). This triangulation model will be supplemented with other discourse and pragmatic analyses when necessary, to interpret the stance-taking in a rapidly changing online environment filled with stances often likely to be related to ethical positions and displays of commentary on a range of topics, including the meta-game skills and abilities of the players, and extra-game references, and the intersection of these concepts in the construction of attitudinal positioning, stancetaking, and inter-personal dynamics in a common goal-motivated speech environment

Basic psychological need satisfaction and thwarting: a study with brazilian professional players of League of Legends

Recently, the skill to play games has led to the professionalization of the activity in the form of “eSports” (electronic sports). Despite the popularity of eSports, little is known about its professional players from a psychological perspective. Given the importance of the coach-created environment in the athletes’ motivational processes, this study aimed to investigate the key psychological dimensions of the coach-created climate in 75 Brazilian professional players of League of Legends (LoL) considering the Self-Determination Theory (SDT) and Achievement Goal Theory (AGT). Fourteen hypotheses were tested, of which seven were confirmed. The empowering climate was a predictor of basic psychological-needs satisfaction and indirectly influenced autonomous motivation. The need satisfaction had a significant impact on both autonomous motivation and on lack of motivation, which, in turn, explained 56% of the variance in well-being and the intention to keep playing eSports. The disempowering climate was a predictor of psychological-needs thwarting but had no significant impact on autonomous motivation or lack of motivation. The results obtained support SDT and AGT in the context of eSports and were similar to those conducted with athletes from traditional sports, indicating that the empower-ing-and-disempowering-coaching-climates conceptualization applies not only to traditional sports athletes but also to professional eSports players.Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Assessing Influential Users in Live Streaming Social Networks

abstract: Live streaming has risen to significant popularity in the recent past and largely this live streaming is a feature of existing social networks like Facebook, Instagram, and Snapchat. However, there does exist at least one social network entirely devoted to live streaming, and specifically the live streaming of video games, Twitch. This social network is unique for a number of reasons, not least because of its hyper-focus on live content and this uniqueness has challenges for social media researchers. Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do? This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Rational Agent Architecture to Recommend which Item to Buy in MOBA Videogames

Los videojuegos multijugador de arena de batalla en línea (MOBA), es un genero de videojuegos que durante la última década han ganado popularidad en la escena competitiva de los E-Sports. Este incremento en su popularidad y la complejidad propia de los mismos han llamado la atención de investigadores en todas las áreas del conocimiento, incluyendo la Inteligencia Artificial. Dichos investigadores han utilizado una amplia variedad de técnicas de Aprendizaje de Maquina buscando mejorar la experiencia de diversos usuarios -jugadores novatos, jugadores expertos, espectadores, entre otros- a través de modelos de predicción, sistemas de recomendación y, aunque se han utilizado técnicas de optimización; estas últimas han sido las menos utilizadas en los videojuegos tipo MOBA. Por ello, el presente trabajo de investigación propone la arquitectura de un agente racional capaz de recomendar a un jugador que objeto comprar para aumentar sus probabilidades de ganar una partida, utilizando una técnica de optimización para la generación de recomendaciones. En la arquitectura propuesta, el agente percibe su ambiente con la información disponible en el API del videojuego League of Legends -uno de los MOBA mas populares actualmente-. Tal información es interpretada por una Regresión Logística que durante las etapas tempranas del juego demostró tener una precisión alrededor de 0.975. A su vez, la técnica de optimización seleccionada para generar la sugerencia fue GRASP; en promedio cada sugerencia es generada en 0.36 segundos, estas sugerencias durante la experimentación lograron aumentar la probabilidad de ganar una partida en promedio 5.2x.Multiplayer online battle arena (MOBA) video games are a genre of video games that during the last decade have gained popularity in the competitive E-Sports scene. This increase in popularity and MOBA’s complexity have attracted the attention of researchers in all areas of knowledge, including Artificial Intelligence (AI). AI researchers have used a wide variety of Machine Learning techniques seeking to improve the experience of various users - novice players, expert players, spectators, among others - through prediction models, recommendation systems and optimization algorithms. However, optimization algorithms have been the least used in MOBA videogames. For that reason, this research proposes the architecture of a rational agent capable of recommending to a player what item to buy to increase his probabilities of winning a game, using an optimization technique for generating recommendations. In the proposed architecture, the agent perceives his environment with the information available in the API of League of Legends -currently, one of the most popular MOBA videogames -. Such information is interpreted by a Logistic Regression that during the early stages of the game was shown to have an accuracy around 0.975. Additionally, the optimization technique selected to generate the suggestion was GRASP. On average each suggestion is generated in 0.36 seconds. During experimentation, these suggestions increase the probability of winning a game on average 5.2x.Magíster en Inteligencia ArtificialMaestrí