438 research outputs found

    Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

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    Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations

    Esports Analytics Through Encounter Detection

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    Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these es, teams of players fight against each other in enclosed arena environs, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally,metrics for evaluating team performance during match runtime are proposed

    Exploring player experience and social networks in MOBA Games: The case of League of Legends

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    A pesar de la popularidad de los juegos de arena de combate multijugador en línea (MOBA en inglés) como League of Legends (LoL), tanto la experiencia de jugador (PE) que proporciona este género relativamente reciente como las redes sociales que se generan a su alrededor siguen, en gran medida, inexplorados. Con el incremento del tiempo que los jugadores dedican a este tipo de juegos competitivos en línea, los impactos positivos y negativos de hacerlo cobran relevancia; es, por lo tanto, importante entender cómo se estructura dicha experiencia para abordar de forma sistemática los mecanismos que desencadenan respuestas de los jugadores. El presente trabajo empieza obteniendo y caracterizando una muestra de jugadores de League of Legends y sigue con el uso de las variables resultantes y de la estructura de las relaciones sociales como entradas para explorar su relación con la experiencia de los jugadores. Al fin y al cabo, la PE es básica para involucrar al jugador y, por lo tanto, es clave para el éxito de cualquier juego digital. Los resultados muestran, entre otros, cómo los jugadores de League of Legends perciben el juego como “justo” para su nivel de competencia en cualquier rango, mientras que su afinidad respecto a los compañeros se ve afectada por la estructura social. La empatía y los sentimientos negativos, no obstante, no parecen verse afectados por la composición del equipo. Entender la experiencia del jugador en League of Legends puede no tan sólo ser útil para mejorar el propio LoL o los juegos de tipo MOBA, sino también para desarrollar juegos más inmersivos a la vez que se mejora su calidad. A medida que los juegos competitivos online se convierten rápidamente en una de las mayores actividades colectivas humanas a nivel global, la investigación sobre la experiencia del jugador adquiere también una importancia crucial

    Player Behavior Modeling In Video Games

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    Player Behavior Modeling in Video Games In this research, we study players’ interactions in video games to understand player behavior. The first part of the research concerns predicting the winner of a game, which we apply to StarCraft and Destiny. We manage to build models for these games which have reasonable to high accuracy. We also investigate which features of a game comprise strong predictors, which are economic features and micro commands for StarCraft, and key shooter performance metrics for Destiny, though features differ between different match types. The second part of the research concerns distinguishing playing styles of players of StarCraft and Destiny. We find that we can indeed recognize different styles of playing in these games, related to different match types. We relate these different playing styles to chance of winning, but find that there are no significant differences between the effects of different playing styles on winning. However, they do have an effect on the length of matches. In Destiny, we also investigate what player types are distinguished when we use Archetype Analysis on playing style features related to change in performance, and find that the archetypes correspond to different ways of learning. In the final part of the research, we investigate to what extent playing styles are related to different demographics, in particular to national cultures. We investigate this for four popular Massively multiplayer online games, namely Battlefield 4, Counter-Strike, Dota 2, and Destiny. We found that playing styles have relationship with nationality and cultural dimensions, and that there are clear similarities between the playing styles of similar cultures. In particular, the Hofstede dimension Individualism explained most of the variance in playing styles between national cultures for the games that we examined

    Scalable Psychological Momentum Estimation in Esports

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    The world of competitive Esports and video gaming has seen and continues to experience steady growth in popularity and complexity. Correspondingly, more research on the topic is being published, ranging from social network analyses to the benchmarking of advanced artificial intelligence systems in playing against humans.In this paper, we present ongoing work on an intelligent agent recommendation engine that suggests actions to players in order to maximise success and enjoyment, both in the space of in-game choices, as well as decisions made around play session timing in the broader context. By leveraging temporal data and appropriate models, we show that a learned representation of player psychological momentum, and of tilt, can be used, in combination with player expertise, to achieve state-of-the-art performance in pre and post-draft win prediction. Our progress toward fulfilling the potential for deriving optimal recommendations is documented
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