188 research outputs found
Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game
Social media has become a major communication channel for communities
centered around video games. Consequently, social media offers a rich data
source to study online communities and the discussions evolving around games.
Towards this end, we explore a large-scale dataset consisting of over 1 million
tweets related to the online multiplayer shooter Destiny and spanning a time
period of about 14 months using unsupervised clustering and topic modelling.
Furthermore, we correlate Twitter activity of over 3,000 players with their
playtime. Our results contribute to the understanding of online player
communities by identifying distinct player groups with respect to their Twitter
characteristics, describing subgroups within the Destiny community, and
uncovering broad topics of community interest.Comment: Accepted at IEEE Conference on Games 201
How do Software Professionals Use Local Informal Meetups?
This report presents the findings of the worldâs first study of informal technology meetups. Local meetings organised by and for technology professionals have grown rapidly in size, reach and scope in recent years. Despite this, however, little is known about how participating in such communities impacts local professionals
Esports Analytics Through Encounter Detection
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 spatioâtemporally defined components referred to as encounters, enabling performance analysis. We apply encounterâbased 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
Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games
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
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Exploration and Skill Acquisition in a Major Online Game
Using data from a major commercial online game, Destiny, we track the development of player skill across time. From over 20,000 player record we identify 3475 players who have played on 50 or more days. Our focus is on how variability in elements of play affect subsequent skill development. After validating the persistent influence of differences in initial performance between players, we test how practice spacing, social play, play mode variability and a direct measure of game-world exploration affect learning rate. These latter two factors do not affect learning rate. Players who space their practice more learn faster, in line with our expectations, whereas players who coordinate more with other players learn slower, which contradicts our initial hypothesis. We conclude that not all forms of practice variety expedite skill acquisition. Online game telemetry is a rich domain for exploring theories of optimal skill acquisition
Do Influencers Influence? -- Analyzing Players' Activity in an Online Multiplayer Game
In social and online media, influencers have traditionally been understood as
highly visible individuals. Recent outcomes suggest that people are likely to
mimic influencers' behavior, which can be exploited, for instance, in marketing
strategies. Also in the Games User Research field, the interest in studying
player social networks has emerged due to the heavy reliance on online
influencers in marketing campaigns for games, as well as in keeping players
engaged. Despite the inherent value of those individuals, it is still difficult
to identify influencers, as the definition of influencers is a debated topic.
Thus, how can we identify influencers, and are they indeed the individuals
impacting others' behavior? In this work, we focus on influence in retention to
verify whether central players impacted others' permanence in the game. We
identified the central players in the social network built from the competitive
player-vs-player (PvP) multiplayer (Crucible) matches in the online shooter
Destiny. Then, we computed influence scores for each player evaluating the
increase in similarity over time between two connected individuals. In this
paper, we were able to show the first indications that the traditional metrics
for influencers do not necessarily apply for games. On the contrary, we found
that the group of central players was distinct from the group of influential
players, defined as the individuals with the highest influence scores. Then, we
provide an analysis of the two groups.Comment: accepted for publication in IEEE Conference on Games (CoG) 202
Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics
Esport games comprise a sizeable fraction of the global games market, and is
the fastest growing segment in games. This has given rise to the domain of
esports analytics, which uses telemetry data from games to inform players,
coaches, broadcasters and other stakeholders. Compared to traditional sports,
esport titles change rapidly, in terms of mechanics as well as rules. Due to
these frequent changes to the parameters of the game, esport analytics models
can have a short life-spam, a problem which is largely ignored within the
literature. This paper extracts information from game design (i.e. patch notes)
and utilises clustering techniques to propose a new form of character
representation. As a case study, a neural network model is trained to predict
the number of kills in a Dota 2 match utilising this novel character
representation technique. The performance of this model is then evaluated
against two distinct baselines, including conventional techniques. Not only did
the model significantly outperform the baselines in terms of accuracy (85%
AUC), but the model also maintains the accuracy in two newer iterations of the
game that introduced one new character and a brand new character type. These
changes introduced to the design of the game would typically break conventional
techniques that are commonly used within the literature. Therefore, the
proposed methodology for representing characters can increase the life-spam of
machine learning models as well as contribute to a higher performance when
compared to traditional techniques typically employed within the literature
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