Narrative Bytes: Data-Driven Content Production in Esports

Esports – video games played competitively that are broadcast to large audiences – are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a largescale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers.

Like a DNA string: Sequence-based Player Profiling in Tom Clancy’s The Division

In this paper we present an approach for using sequence analysis to model player behavior. This approach is designed to work in game development contexts, integrating production teams and delivering profiles that inform game design. We demonstrate the method via a case study of the game Tom Clancy’s The Division (published by Ubisoft and developed by Ubisoft Massive Entertainment), which with its 20 million players represents a major current commercial title. The approach presented provides a mixed-methods framework, combining qualitative knowledge elicitation and workshops with large scale telemetry analysis, using sequence mining and clustering to develop detailed player profiles showing the core gameplay loops of The Division’s players.

Evaluating the Onboarding Phase of Free-to Play Mobile Games: A Mixed-Method Approach

The first few minutes of play, commonly referred to as the onboarding phase, of Free-to-Play mobile games typically display a substantial churn rate among new players. It is therefore vital for designers to effectively evaluate this phase to investigate its satisfaction of player expectations. This paper presents a study utilizing a lab-based mixed-methods approach in providing insights for evaluating the user experience of onboarding phases in mobile games. This includes an investigation into the contribution of physiological measures (Heart-Rate Variability and Galvanic Skin Conductance) as well as a range of self-reported proxy measures including: a) stimulated recall, engagement graphs, b) flow state survey and c) post-game experience questionnaire. These techniques were applied across 28 participants using three mobile Free-to-Play titles from different genres. This paper makes two important contributions to the games user research (GUR) domain: 1) evaluates different research techniques (e.g. physiological measures and experience graphs) in the context of mobile games; 2) provides an empirically based recommendation for design elements that result in high arousal.

Play With Me? Understanding and Measuring the Social Aspect of Casual Gaming

Social Gaming is a pervasive phenomenon, driven by the advent of social networks and the digitization of game distribution. This paper positions and defines Casual Social Games (CSGs) as a genre and platform agnostic subset of Social Games that incorporates browser, mobile, console and wearable digital games. Here we argue that – as CSGs impact the games industry, shape play patterns and audience characteristics, and proliferate to new platforms – understanding and measuring their social aspect becomes highly relevant. A randomized experiment on added social gameplay in a CSG on both mobile and Facebook serves to support this argument. Experimental results highlight that social gameplay is extremely important for engagement and monetization in casual games, even more so on mobile platforms. This does not only suggest that CSG developers will benefit from focusing on increased social interaction in their games, but that Game Analytics should strive to unify definitions and build a common body of knowledge around the social aspect of casual gaming.

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 games, teams of players fight against each other in enclosed arena environments, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatiotemporally defined components referred to as encounters, enabling performance analysis. In this paper, 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.

Beyond the individual: Understanding social structures of an online player matchmaking website

Engagement and player experience in multi-player games is influenced by the people you play with. For that reason, grouping features and matchmaking facilities in games, as well as third-party services, have gained in popularity in the industry and player community as they assist in building and maintaining social relationships with like-minded players. Understanding how social connections are formed and how these relations can foster in-game activity offers insights for building and maintaining a player base and can, in turn, improve retention and engagement. This paper examines the social network formed by users of the the100.io – a social matchmaking website for the game Destiny. The service provides an opportunity to examine an online social network formed around a game combined with demographic and preference data. The paper explores the correlation of structural network properties with preference and game-related performance data, provides metrics useful for analyzing and understanding the structure of these kinds of player networks and showcases how community analysis and behavioral profiling can be applied to inform game developers about behavioral groupings in social player networks.

Influencers in Multiplayer Online Shooters Evidence of Social Contagion in Playtime and Social Play

In a wide range of social networks, people’s behavior is influenced by social contagion: we do what our network does. Networks often feature particularly influential individuals, commonly called influencers. Existing work suggests that in-game social networks in online games are similar to real life social networks in many respects. However, we do not know whether there are in-game equivalents to influencers. In this paper, we therefore applied standard social network features used to identify influencers to the online multiplayer shooter Tom Clancy’s The Division. Results show that network features defined influencers had indeed an outsized impact on playtime and social play of players joining their in-game network.

Correlation between Heart Rate, Electrodermal Activity and Player Experience in First-Person Shooter Games

Psychophysiological methods are becoming more popular in game research as covert and reliable measures of affective player experience, emotions, and cognition. Since player experience is not well understood, correlations between self-reports from players and psychophysiological data may provide a quantitative understanding of this experience. Measurements of electrodermal activity (EDA) and heart rate (HR) allow making inferences about player arousal (i.e., excitement) and are easy to deploy. This paper reports a case study on HR and EDA correlations with subjective gameplay experience, testing the feasibility of these measures in commercial game development contexts. Results indicate a significant correlation (p < 0.01) between psychophysiological arousal (i.e., HR, EDA) and self-reported gameplay experience. However, the covariance between psychophysiological measures and self-reports varies between the two measures. The results are consistent across three different contemporary major commercial first-person shooter (FPS) games (Prey, Doom 3, and Bioshock).

Time to Die: Death Prediction in Dota 2 using Deep Learning

Esports have become major international sports with hundreds of millions of spectators. Esports games generate massive amounts of telemetry data. Using these to predict the outcome of esports matches has received considerable attention, but micro-predictions, which seek to predict events inside a match, is as yet unknown territory. Micro-predictions are however of perennial interest across esports commentators and audience, because they provide the ability to observe events that might otherwise be missed: esports games are highly complex with fast-moving action where the balance of a game can change in the span of seconds, and where events can happen in multiple areas of the playing field at the same time. Such events can happen rapidly, and it is easy for commentators and viewers alike to miss an event and only observe the following impact of events. In Dota 2, a player hero being killed by the opposing team is a key event of interest to commentators and audience. We present a deep learning network with shared weights which provides accurate death predictions within a five-second window. The network is trained on a vast selection of Dota 2 gameplay features and professional/semi-professional level match dataset. Even though death events are rare within a game (1\% of the data), the model achieves 0.377 precision with 0.725 recall on test data when prompted to predict which of \textit{any} of the 10 players of either team will die within 5 seconds. An example of the system applied to a Dota 2 match is presented here. This model enables real-time micro-predictions of kills in Dota 2, one of the most played esports titles in the world, giving commentators and viewers time to move their attention to these key events.

Win Prediction in Multi-Player 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 emphasised pre-match prediction and used data from amateur games, whichare more easily available than professional level. However, the commercial value of win prediction exists at the professional level. Furthermore, predicting real-time data is unexplored, as isits 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 multi-player online battle arena (MOBA) esport. The paper evaluates the first professional-level prediction models for liveDotA 2 matches, one of the most popular MOBA games and trials it at a major international esports tournament. Using standardmachine learning models, feature engineering and optimization,our model is 85% accurate after 5 minutes of gameplay. Our analyses highlight the need for algorithm evaluation and optimization and how professional data differs from amateur data.Finally, we present implications for the esports/game analytics domains, describe commercial opportunities, practical challenges,and propose a set of evaluation criteria for research on esports win prediction

Exploring the relationship between video game expertise and Fluid Intelligence

Hundreds of millions of people play intellectually-demanding video games every day. What does individual performance on these games tell us about cognition? Here, we describe two studies that examine the potential link between intelligence and performance in one of the most popular video games genres in the world (Multiplayer Online Battle Arenas: MOBAs). In the first study, we show that performance in the popular MOBA League of Legends’ correlates with fluid intelligence as measured under controlled laboratory conditions. In the second study, we also show that the age profile of performance in the two most widely-played MOBAs (League of Legends and DOTA II) matches that of raw fluid intelligence. We discuss and extend previous videogame literature on intelligence and videogames and suggest that commercial video games can be useful as ‘proxy’ tests of cognitive performance at a global population level.

The Trails of Just Cause 2: Spatio-Temporal Player Profiling in Open-World Games

Behavioral profiling of players in digital games is a key challenge in game analytics, representing a particular challenge in Open-World Games. These games are characterized by large virtual worlds and few restrictions on player affordances. In these games, incorporating the spatial and temporal dimensions of player behavior is necessary when profiling behavior, as these dimensions are important to the playing experience. We present analyses that apply cluster analysis and the DEDICOM decompositional model to profile the behavior of more than 5,000 players of the major commercial title Just Cause 2 integrating both spatio-temporal trails and behavioral metrics. The application of DEDICOM to profile the spatio-temporal behavior of players is demonstrated for the purpose of analysing the entire play history of Just Cause 2 players, but also for the more detailed analysis of a single mission. This showcases the applicability of spatio-temporal profiling to condense player behavior across large sample sizes, across different scales of investigation. The method presented here provides a means to build profiles of player activity in game environments with high degrees of freedom across different scales of analysis – from a small segment to the entire game.

Controlling the Crucible: A PvP Recommender Systems Framework for Destiny

Compared to conventional retail games, today’s Massively Multiplayer Online Games have become progressively more complex. Consumable resources in such games are nearly unlimited, making decisions to improve levels of engagement more challenging.Intelligent information filtering methods here can help players make smarter decisions, thereby improving performance, increasing engagement, and reducing the likelihood of early departure. In this paper, a novel approach towards building a hybrid content- and knowledge-based recommender system for player-versus-player (PvP) content in the Destiny is presented. The frame-work groups the players based on three distinct traced behavioral aspects: base stats, cooldown stats, and weapon playstyle. Different combinations of these pro-files are considered to make playstyle recommendations and online evaluations through the social community website Reddit are made to evaluate the performance of the framework.

Guns, Swords and Data: Clustering of Player Behavior in Computer Games in the Wild

Behavioral data from computer games can be exceptionally high-dimensional, of massive scale and cover a temporal segment reaching years of real-time and a varying population of users. Clustering of user behavior provides a way to discover behavioral patterns that are actionable for game developers. Behavioral data from computer games can be exceptionally high-dimensional, of massive scale and cover a temporal segment reaching years of real-time and a varying population of users. Clustering of user behavior provides a way to discover behavioral patterns that are actionable for game developers. Interpretability and reliability of clustering results is vital, as decisions based on them affect game design and thus ultimately revenue. In this paper, case studies are presented focusing on clustering analysis applied to high-dimensionality player behavior telemetry, covering a combined total of 260,000 characters from two major commercial game titles: the Massively Multiplayer Online Role-Playing Game Tera and the multi-player strategy war game Battlefield 2: Bad Company 2. Kmeans and Simplex Volume Maximization clustering were applied to the two datasets, combined with considerations of the design of the games, resulting in actionable behavioral profiles. Depending on the algorithm different insights into the underlying behavior of the population of the two games are provided.

Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability

Player Modeling using Self-Organization in Tomb Raider: Underworld

We present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game. The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game. In this paper, we present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game. The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game. The proposed approach automates, in part, the traditional user and play testing procedures followed in the game industry since it can inform game developers, in detail, if the players play the game as intended by the game design. Subsequently, player models can assist the tailoring of game mechanics in real-time for the needs of the player type identified.

Predicting Player Churn in Destiny: A Hidden Markov Models Approach to Predicting Player Departure in a Major Online Game

Destiny is, to date, the most expensive digital game ever released with a total operating budget of over half a billion US dollars. It stands as one of the main examples of AAA titles, the term used for the largest and most heavily marketed game productions in the games industry. Destiny is a blend of a shooter game and massively multi-player online game, and has attracted dozens of millions of players. As a persistent game title, predicting retention and churn in Destiny is crucial to the running operations of the game, but prediction has not been attempted for this type of game in the past. In this paper, we present a discussion of the challenge of predicting churn in Destiny, evaluate the area under curve (ROC) of behavioral features, and use Hidden Markov Models to develop a churn prediction model for the game.

A Team Based Player Versus Player Recommender Systems Framework For Player Improvement

Modern Massively Multi-player Online Games (MMOGs) have grown to become extremely complex in terms of the usable resources in the games, resulting in an increase in the amount of data collected by tracking the in-game activities of players. This has opened the door for researchers to come up with novel methods to utilize this data to improve and personalize the user experience. In this paper, a novel but flexible framework towards building a team based recommender system for player-versus-player (PvP) content in such MMOGs is presented, and applied to a case study in the context of the major commercial title Destiny 2. The framework combines behavioral profiling via cluster analysis with recommendation systems to look at teams of players as a unit, as well as the individual players, to make recommendations to the players, with the purpose of providing information to them towards improving their performance.

Rapid Prediction of Player Retention in Free-to-Play Mobile Games

Predicting and improving player retention is crucial to the success of mobile Free-to-Play games. This paper explores the problem of rapid retention prediction in this context. Heuristic modeling approaches are introduced as a way of building simple rules for predicting short-term retention. Compared to common classification algorithms, our heuristic-based approach achieves reasonable and comparable performance using information from the first session, day, and week of player activity

How Players Lose Interest in Playing a Game: An Empirical Study Based on Distributions of Total Playing Time

An important problem in game analytics is to characterize how player engagement evolves over time. Reliable models allow for assessing the long-term success of game products and can provide estimates of how long players may be expected to keep actively playing a game. We introduce methods from random process theory into game data mining in order to draw inferences about player engagement. In this paper, we find that the Weibull distribution gives a good account of the statistics of total playing times. This implies that an average player’s interest in playing one of the games considered evolves according to a non-homogeneous Poisson process. Therefore, given data on the initial playtime behavior of the players of a game, it becomes possible to predict when they stop playing.

Evaluating Motion. Spatial User Behavior in Virtual Environments

User-behaviour analysis has only recently been adapted to the context of the virtual world domain and remains limited in its application. Behaviour analysis is based on instrumentation data, automated, detailed, quantitative information about user behaviour within the virtual environment (VE) of digital games. A key advantage of the method in comparison with existing user-research methods, such as usability- and playability-testing is that it permits very large sample sizes. Furthermore, games are in the vast majority of cases based on spatial, VEs within which the players operate and through which they experience the games. Therefore, spatial behaviour analyses are useful to game research and design. In this paper, spatial analysis methods are introduced and arguments posed for their use in user-behaviour analysis. Case studies involving data from thousands of players are used to exemplify the application of instrumentation data to the analysis of spatial patterns of user behaviour.

Predicting Player Behavior in Tomb Raider: Underworld 

This paper presents the results of an explorative study on predicting aspects of playing behavior for the major commercial title Tomb Raider: Underworld (TRU). Various supervised learning algorithms are trained on a large-scale set of in-game player behavior data, to predict when a player will stop playing the TRU game and, if the player completes the game, how long will it take to do so. Results reveal that linear regression models and other non-linear classification techniques perform well on the tasks and that decision tree learning induces small yet well-performing and informative trees. Moderate performance is achieved from the prediction models, which indicates the complexity of predicting player behavior based on a constrained set of gameplay metrics and the noise existent in the dataset examined, a generic problem in large-scale data collection from millions of remote clients.

Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny

Competitive multi-player game play is a common feature in major commercial titles, and has formed the foundation for esports. In this paper, the question whether it is possible to predict match outcomes in First Person Shooter-type multiplayer competitive games with mixed genres is addressed. The case employed is Destiny, which forms a hybrid title combining Massively Multi-player Online Role-Playing game features and First-Person Shooter games. Destiny provides the opportunity to investigate prediction of the match outcome, as well as the influence of performance metrics on the match results in a hybrid multi-player major commercial title. Two groups of models are presented for predicting match results: One group predicts match results for each individual game mode and the other group predicts match results in general, without considering specific game modes. Models achieve a performance between 63% and 99% in terms of average precision, with a higher performance recorded for the models trained on specific multi-player game modes, of which Destiny has several. We also analyzed performance metrics and their influence for each model. The results show that many key shooter performance metrics such as Kill/Death ratio are relevant across game modes, but also that some performance metrics are mainly important for specific competitive game modes. The results indicate that reliable match prediction is possible in FPS-type esports games.

Analyzing Spatial user Behavior in Computer Games using Geographic Information Systems 

An important aspect of the production of digital games is user oriented testing. A central problem facing practitioners is however the increasing complexity of user-game interaction in modern games, which places challenges on the evaluation of interaction using traditional user-oriented approaches. Gameplay metrics are instrumentation data which detail user behavior within the virtual environment of digital games, forming accurate and detailed datasets about user behavior that supplement existing user-testing methods such as playtesting and usability testing. In this paper existing work on gameplay metrics is reviewed, and spatial analysis of gameplay metrics introduced as a new approach in the toolbox of user-experience testing and –research. Furthermore, Geographic Information Systems (GIS) are introduced as a tool for performing spatial analysis. A case study is presented with Tomb Raider: Underworld, showcasing the merger of GIS with gameplay metrics analysis and its application to game testing and –design. 

Naming Virtual Identities: Patterns and Inspirations for Character Names in World of Warcraft

People are increasingly interacting via online services – from forums, online communities, social networks and online computer games. While there has been considerable research on the motivations and communication of people online, as well as the social environment provided by online services, large-scale analyses of the virtual identities people use online are rare. In this paper, the first large-scale analysis of virtual identities in the massively multi-player online game World of Warcraft is presented based on a dataset of nearly eight million avatar names and associated information. The results presented highlight the inventiveness of the names the users of World of Warcraft express in terms of naming their virtual identities, and the varied nature of the sources of inspiration for these names, which range from mythology, literature, popular culture, real-world names and others. The results also indicate that how players name their characters is influenced by the aesthetics and game function of the characters. The server type also appears to have an impact on player naming strategies, with role-playing servers forming a distinct cluster from Player-vs-Player type servers. Additionally, the analysis presented reveals that character name frequencies follow, similar to real-world names, a power law distribution.        

Going Out of Business: Auction House Behavior in the Massively MultiPlayer Online Game Glitch   

The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period. The data comprise almost 3 million data points, over 20,000 unique players and more than 650 products. Furthermore, an interactive visualization, based on Sankey flow diagrams, is presented which shows the proportion of the different clusters across each time bin, as well as the flow of players between clusters. The diagram allows evaluation of migration of players between clusters as a function of time, as well as churn analysis. The presented work provides a template analysis and visualization model for progression-based or temporal-based analysis of player behavior broadly applicable to games.

Behavior Evolution in Tomb Raider Underworld

Behavioral datasets from major commercial game titles of the “AAA” grade generally feature high dimensionality and large sample sizes, from tens of thousands to millions, covering time scales stretching into several years of real-time, and evolving user populations. This makes dimensionality-reduction methods such as clustering and classification useful for discovering and defining patterns in player behavior. The goal from the perspective of game development is the formation of behavioral profiles that provide actionable insights into how a game is being played, and enables the detection of e.g. problems hindering player progression. Due to its unsupervised nature, clustering is notably useful in cases where no prior-defined classes exist. Previous research in this area has successfully applied clustering algorithms to behavioral datasets from different games. In this paper, the focus is on examining the behavior of 62,000 players from the major commercial game Tomb Raider: Underworld, as it unfolds from the beginning of the game and throughout the seven main levels of the game. Where previous research has focused on aggregated behavioral datasets spanning an entire game, or conversely a limited slice or snapshot viewed in isolation, this is to the best knowledge of the authors the first study to examine the application of clustering methods to player behavior as it evolves throughout an entire game.

Online-only friends, real-life friends or strangers? Differential associations with passion and social capital in video game play

The present study tests a recently proposed model in which social video game play supports wellbeing by contributing to a harmonious type of engagement with the game. Players (N = 2030) of the online-only multiplayer first-person shooter game, Destiny, reported the frequency they played with real-life friends, online-only friends and strangers, their type of engagement with the game – measured as harmonious and obsessive passion, and completed a wellbeing measure of social capital. Telemetry data also recorded their total time playing over the duration of the study. A structural equation model supported the prediction that harmonious – but not obsessive – passion would mediate the positive association between playing with others and social capital. The findings also supported a supplementary hypothesis that the three types of social relationships would be differentially associated with two forms of social capital – bridging versus bonding – as a function of the closeness of social ties. Real-life friends was positively associated with bonding, strangers with bridging, and online-only friends with both. Overall, these results emphasise that social interactions in (and around) online multiplayer video games are effective for building social capital, and do so by ensuring game play is in harmony with other goals and values.

Game Time: Modeling and Analyzing Time in Multiplayer and Massively Multiplayer Games

Game time is a core feature of game design and study, and forms part of the gaming experience on a variety of levels. It can be viewed from multiple perspectives, for example, the time of the playing of the game or the flow of time in a game world. In this paper, a comprehensive game time model based on empirical research as well as recent theory is presented. It proposes various perspectives on game time and integrates them to allow coherent representation of the same events in the different perspectives. The model has been tested across tabletop and digital formats, and its applicability across game formats is demonstrated. Emphasis is placed on multiplayer and massively multiplayer role-playing games because these feature complex game time behavior not previously evaluated. The model considers game time as an interactively created and nonlinear feature of games and game play.

Inside the Group: Investigating Social Structures in Player Groups and Their Influence on Activity

Social features, matchmaking, and grouping functions are key elements of online multi-player experiences. Understanding how social connections form in and around games and their relationship to in-game activity offers insights for building and maintaining player bases and for improving engagement and retention. This paper presents an analysis of the groups formed by users of the the100.io – a social matchmaking website for different commercial titles, including Destiny on which we focus in this paper. Groups formed on the100.io can be described across a range of social network related metrics. Also, the social network formed within a group is evaluated in combination with user-provided demographic and preference data. Archetypal analysis is used to classify groups into archetypes and a correlation analysis is presented covering the effect of group characteristics on in-game-activity. Finally, weekly activity profiles are described. Our results indicate that group size as well as the number of moderators within a group and their connectedness to other team members influences a group’s activity. We also identified four prototypical types of groups with different characteristics concerning composition, social cohesion, and activity.

What Moves Players? Visual Data Exploration of Twitter and Gameplay Data

In recent years, microblogging platforms have not only become an important communication channel for the game industry to generate and uphold audience interest but also a rich resource for gauging player opinion. In this paper we use data gathered from Twitter to examine which topics matter to players and to identify influential members of a game’s community. By triangulating in-game data with Twitter activity we explore how tweets can provide contextual information for understanding fluctuations in in-game activity. To facilitate analysis of the data we introduce a visual data exploration tool and use it to analyze tweets related to the game Destiny. In total, we collected over one million tweets from about 250,000 users over a 14-month period and gameplay data from roughly 3,500 players over a six-month period.

Analyzing Player Networks in Destiny

Destiny is a hybrid online shooter sharing features with Massively Multi-Player Online Games and first-person shooters and is the to date the most expensive digital game produced. It has attracted millions of players to compete or collaborate within a persistent online environment. In multiplayer online games, the interaction between the players and the social community that forms in persistent games forms a crucial element in retaining and entertaining players. Social networks in games have thus been a focus of research, but the relationships between player behavior, performance, engagement and the networks forming as a result of interactions, are not well understood. In this paper, a large-scale study of social networks in hybrid online games/shooters is presented. In a network of over 3 million players, the connections formed via direct competitive play are explored and analyzed to answer five main research question focusing on the patterns of players who play with the same people and those who play with random groups, and how differences in this behavior influence performance and engagement metrics. Results show that players with stronger social relationships have a higher performance based on win/loss ratio and kill/death ratio, as well as a tendency to play more and longer.

Clustering Game Behavior Data

Recent years have seen a deluge of behavioral data from players hitting the game industry. Reasons for this data surge are many and include the introduction of new business models, technical innovations, the popularity of online games, and the increasing persistence of games. Irrespective of the causes, the proliferation of behavioral data poses the problem of how to derive insights therefrom. Behavioral data sets can be large, time-dependent and high-dimensional. Clustering offers a way to explore such data and to discover patterns that can reduce the overall complexity of the data. Clustering and other techniques for player profiling and play style analysis have therefore become popular in the nascent field of game analytics. However, the proper use of clustering techniques requires expertise and an understanding of games is essential to evaluate results. With this paper, we address game data scientists and present a review and tutorial focusing on the application of clustering techniques to mine behavioral game data. Several algorithms are reviewed and examples of their application shown. Key topics such as feature normalization are discussed and open problems in the context of game analytics are pointed out.

Game Analytics for Game User Research: A Workshop Review and Case Study

The emerging field of game user research (GUR) investigates interaction between players and games and the surrounding context of play. Game user researchers have explored methods from, for example, human-computer interaction, psychology, interaction design, media studies, and the social sciences. They have extended and modified these methods for different types of digital games, such as social games, casual games, and serious games. This article focuses on quantitative analytics of in-game behavioral user data and its emergent use by the GUR community. The article outlines open problems emerging from several GUR workshops. In addition, a case study of a current collaboration between researchers and a game company demonstrates game analytics’ use and benefits.

Social Aspects of the Game Development Process in the Global Gam Jam

Game jamming is a valuable tool and process to connect game developers from a range of disciplines such as art, programming, audio engineering, or story-telling. The Global Game Jam® (GGJ) thus fundamentally supports the process of connecting people, and forms a basis for studying teamwork formation and -mechanics. It is an annual event, where participants (jammers) meet on physical locations to develop games together within a short time-span of 48 hours. People with similar interest but diferent skill-sets have the chance to meet and collaborate to create prototypes of games together, whether digital or analog. In this explorative study, data from the GGJ website across four years of game jams and their participants are analyzed, towards investigating group interaction and group forming behavior. The focus is on using social network analysis and social metrics to evaluate the inluence of a) jammer skillset and b) jam locations, on social and communicative structures among game jammers in the GGJ environment. The indings suggest that diferent skill-sets support diferent forms of social structures and also setups in countries refer to diferent group sizes and diferent social structures.

Predicting skill learning outcomes in a large, longitudinal MOBA dataset

The exploration of the relationships between behavior and cognitive psychology of game players has gained impetus in recent years because such links provides an opportunity for improving user experiences and optimizing products in the games industry. At the same time, the volume and global scope of digital game telemetry data has opened up new experimental opportunities for studying human behavior at large scales. Prior research has demonstrated that a relation exists between learning rates and performance. Although many factors might contribute to this correlation at least one may be the presence of innate cognitive resources, as demonstrated in recent work relating IQ and performance in a Multi-player Online Battle Arena game. In this paper, we extend this work by examining the relationship between early learning rate and long term performance using a 400,000 player longitudinal dataset generated by new players of the widely-played MOBA League of Legends. We observed that the learning rate of new players in a competitive season explains a significant amount of variance in the performance at the end of the year. This analysis was then extended by training two multivariate classifiers (Logistic Regression, Random Forest) for predicting players who by the end of the season would be considered masters (top 0.05%), based on their performance in the first 10 matches of the same season. Both classifiers performed similarly (ROC AUC 0.888 for Logistic Regression, 0.878 for Random Forest), extending the time frame for skill prediction in games based on a relatively sparse sample of early data. We discuss the implications for these findings based on preexisting psychological studies of learning and intelligence, and close with challenges and direction for future research.

Patterns in the distribution of digital games via BitTorrent

The distribution of illegal copies of computer games via digital networks forms the centre in one of the most heated debates in the international games environment, but there is minimal objective information available. In this paper, the results of a large-scale, open-method analysis of the distribution of computer games via BitTorrent peer-to-peer file-sharing protocol is presented. 173 games were included, tracked over a period of three months from 2010 to 2011. A total of 12.6 million unique peers were identified across over 200 countries. Analysis indicates that the distribution of illegal copies of games follows distinct pattern, e.g., that a few game titles drive the traffic – the 10 most accessed games encompassed 42.7% of the number of peers tracked. The traffic is geographically localised – 20 countries encompassed 76.7% of the total. Geographic patterns in the distribution of BitTorrent peers are presented, as well as time–frequency distributions of torrents, and additional results.

Stylized Facts for Mobile Game Analytics

There are numerous widely disseminated beliefs in the rapidly growing domain of Mobile Game Analytics, notably within the context of the Free-to-Play model. However, the field remains in its infancy, as there is limited conclusive empirical knowledge available across industry and academia, to provide evidence for these beliefs. Additionally, the current knowledge base is highly fragmented. For Mobile Game Analytics to mature, empirical frameworks are needed. In this paper the concept of stylized facts is presented as a means to develop an initial framework for a common understanding of key hypotheses and concepts in the field, as well as organizing the available empirical knowledge. A focus on stylized facts research will not only facilitate communication but also, more importantly, improve the quality and actionability of insights. Unified terminology and a comprehensive collection of stylized facts can be the building blocks for a conceptually well-founded understanding of mobile gaming.

Patterns of Play: Play-Personas in User-Centred Game Development

In recent years certain trends from User-Centered design have been seeping into the practice of designing computer games. The balance of power between game designers and players is being renegotiated in order to find a more active role for players and provide them with control in shaping the experiences that games are meant to evoke. A growing player agency can turn both into an increased sense of player immersion and potentially improve the chances of critical acclaim. This paper presents a possible solution to the challenge of involving the user in the design of interactive entertainment by adopting and adapting the “persona” framework introduced by Alan Cooper in the field of Human Computer Interaction. The original method is improved by complementing the traditional ethnographic descriptions of personas with parametric, quantitative, data-oriented models of patterns of user behaviour for computer games.

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, in this paper, 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.

Methods for Evaluating Gameplay Experience in a Serious Gaming Context

Gameplay experience (GX) is created during the process of player-game interaction, where this interaction has the goal to provide a motivating, fun experience for the player. Since GX is an important factor for the success of failure of a game, a formal classification of how to design for and evaluate GX is necessary. Using appropriate mechanisms for evaluation and measurement of GX allows the validation of good gameplay experiences. This paper presents an approach to formalize such evaluative methods and a roadmap for applying these mechanisms in the context of serious games. We first discuss related work of user experience (UX) and player experience models, based on which we propose a three-layer framework of GX. For each layer, a number of measurement methodologies are listed and our focus is put on physiological and technical metrics for game evaluation. Finally, we point out the potential use of this framework within the field of game-based learning and serious gaming for sports and health.

Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning

Major commercial (AAA) games increasingly transit to a semi-persistent or persistent format in order to extend the value of the game to the player, and to add new sources of revenue beyond basic retail sales. Given this shift in the design of AAA titles, game analytics needs to address new types of problems, notably the problem of forecasting future player behavior. This is because player retention is a key factor in driving revenue in semi-persistent titles, for example via downloadable content. This paper introduces a model for predicting retention of players in AAA games and provides a tensor-based spatio-temporal model for analyzing player trajectories in 3D games. We show how knowledge as to trajectories can help with predicting player retention. Furthermore, we describe two new algorithms for three way DEDICOM including a fast gradient method and a semi-nonnegative constrained method. These approaches are validated against a detailed behavioral data set from the AAA open-world game Just Cause 2.