Maps have been an essential part of video games since their early days to help player orient themselves in game spaces. However, maps fulfil various purposes beyond pure navigation tasks. In this paper, we specifically focus on diegetic maps, and look at the dimensions of storytelling and wayfinding that emerged through a thematic analysis of posts about video game maps. Our analysis also shows that diegetic maps take on different forms, ranging from passive maps displayed on walls to ’physical’ maps and fully digital maps. We then reflect on how diegetic maps support different forms of immersion, in particular spatial, narrative, and emotional immersion. Next, we highlight the relevance of different map components and elements in influencing immersion. Together, these factors can support specific types of player experiences. Based on our findings, we propose that the immersion of diegetic maps should be examined and evaluated through a multifaceted view of this phenomenon.

With video games steadily increasing in complexity, automated generation of game content has found widespread interest. However, the task of 3D gaming map art creation remains largely underexplored to date due to its unique complexity and domain-specific challenges. To this end, we introduce a novel Smart Brush for map editing, designed to assist artists in seamlessly modifying selected areas of a game map with minimal effort. By leveraging generative adversarial networks (GANs) and diffusion models we propose two variants of the brush that both enable efficient and context-aware generation. Our hybrid workflow aims to enhance both artistic flexibility and production efficiency, enabling the refinement of environments without manually reworking every detail, thus helping to bridge the gap between automation and creative control in game development. A comparative evaluation of our two methods with adapted versions of several state-of-the art models shows that our GAN-based brush produces the sharpest and most detailed outputs while preserving image context. In contrast, the evaluated state-of-the-art models tend towards blurrier results and exhibit difficulties in maintaining contextual consistency.

Interactions with non-player characters (NPCs) are ubiquitous in video games but traditionally rely on scripted dialogue. The advent of large language models (LLMs) has opened up new opportunities to converse with NPCs and, in turn, contribute to player experience. However, integrating LLM-driven dialogue in games is not necessarily straightforward and needs an understanding of how this new technique is perceived by players and shapes their game experience. In this paper we contribute to this emerging line of inquiry and report on a study investigating players’ perceptions on LLM-generated dialogues in a bespoke game. Towards this end, we have conducted an online survey, containing both quantitative measures and qualitative open-ended questions, with 62 participants. Our results indicate that LLM-generated dialogues have several benefits such as enhanced input flexibility or leading to more natural conversations but can create a variety of undesired side-effects that might be hard to anticipate beforehand or control for.

Understanding and predicting player movement in multiplayer games is crucial for achieving use cases such as player-mimicking bot navigation, preemptive bot control, strategy recommendation, and real-time player behavior analytics. However, the complex environments allow for a high degree of navigational freedom, and the interactions and team-play between players require models that make effective use of the available heterogeneous input data. This paper presents a multimodal architecture for predicting future player locations on a dynamic time horizon, using a$U$-Net-based approach for calculating endpoint location probability heatmaps, conditioned using a multimodal feature encoder. The application of a multi-head attention mechanism for different groups of features allows for communication between agents. In doing so, the architecture makes efficient use of the multimodal game state including image inputs, numerical and categorical features, as well as dynamic game data. Consequently, the presented technique lays the foundation for various downstream tasks that rely on future player positions such as the creation of player-predictive bot behavior or player anomaly detection.

A central area of interest in many competitive online games is spatial behavior which due to its complexity can be difficult to visualize. Such behaviors of interest include not only overall movement patterns but also being able to understand which player or team is exerting control over an area to inform decision-making. Map control can, however, be challenging to quantify. In this paper, we propose a method for calculating frontlines and first efforts towards a visualization of them. The visualization can show map control and frontlines at a specific time point or changes of these over time. For this purpose, it utilizes support vector machines to derive frontlines from unit positions. We illustrate our algorithm and visualization with examples based on the team-based online game World of Tanks.

As player demand for information about esports games surges, live companion tools were developed to help them understand game-related knowledge, make decisions, and review post-game data.Despite growing interest, little is currently known about how different users perceive and use commercially available apps and their different features.However, this is important for creating tools that actually meet players' needs.To help fill this gap, we selected two companion apps for two different competitive game genres and explored them with respect to four dimensions -understandability, informativeness, decision making, and trustworthiness -across professional and nonprofessional players.Towards this end, we first conducted an online survey to assess users' perception around these dimensions.This was followed by online interviews to gain further insights.Through statistical and thematic analysis, our results highlight commonalities and disparities in user behavior and preferences across user groups, contributing to our understanding and future design of live companion tools.

We investigate mouse movement as an interaction technique to solve tasks in simple visual interface settings. For this purpose, we recruited 17 participants in a controlled within-subjects study. The design of the study is based on six different task groups. As an independent variable, we tested the impact of typical Gestalt principles visually encoded in user interfaces on the dependent variable mouse movement, in particular on movement path trajectories and task completion times until mouse click. The variations of the independent variable take into account Gestalt principles such as proximity, similarity, common region, and figure ground, testing cases in which the principles are followed and cases in which they are disregarded. We illustrate movement paths as trajectory visualizations in three variations showing the paths themselves over space and time, movement speed, and mouse clicks. The main result of the study suggests that visual interfaces that follow Gestalt principles produce shorter mouse movement paths and task completion times.

Interest in data-informed play has accelerated in recent years across player communities, particularly within the competitive gaming scene. As such research on analytics tools for players has concentrated predominantly on popular esports games and genres. However, such tools could be of interest to players of other genres as well given a general tendency for self-tracking. In this work we focus on Metroidvania games, studying a multi-view dashboard for visualizing gameplay data from Hollow Knight. Using an insight-based evaluation methodology and interviews with 12 participants we study 1) insights players infer from the dashboard and 2) use cases such a tool can enable within the context of Metroidvania games. Our results reveal that in addition to common performance-related use cases, participants found personal satisfaction when viewing the visualizations and considered them to be an opportunity for social engagement. Participants mostly used the map and events view to infer insights but also relied on recalled data and their existing familiarity with the game. Based on our findings we further reflect on future research directions. Through this we contribute to developing a broader understanding of computational support tools for players.

Data-informed play practices are starting to be embraced by different subgroups of the player population. In the wake of this, interest in data tracking and visualization tools is growing. Player-facing visualizations often take the form of aggregated performance statistics. However, map-based analytics for players are still being comparable less explored due to the rare availability of detailed behavioral and movement data. Nonetheless, navigation is central in many games, among them, Metroidvania-like games which emphasize exploration and discovery. In this paper, we introduce HKViz, which consists of 1) a modification for Hollow Knight—a popular Metroidvania—to track players’ in-game data and 2) a website offering a dashboard for data analysis. HKViz is intended to enable user research on player-centric map-based analytics. We further present preliminary user feedback and discuss potential extensions.

Movement and navigation are key aspects of many games. Endpoint and trajectory prediction in games is thus becoming an emerging matter, also because they can serve as important components for downstream tasks such as real-time assistants, AI behaviour selection, or bot detection. However, such predictions can become costly due to the large volumes of data. In this paper, we present first steps towards a lightweight modular pipeline for endpoint and trajectory prediction based on Voronoi tessellation for compact and efficient data storage. The model outputs probability distributions, allowing for multimodality and easy processing by downstream tasks. We illustrate and evaluate the proposed approach using data from the team-based computer game World of Tanks. First results suggest that the proposed pipeline performs well in predicting trajectories, while keeping memory and computation requirements small.

Games are frequently viewed as spatial constructs, where the game space enables play, creates challenge, and enhances and facilitates immersion. The spatial analysis of player behavior thus constitutes a key element in games user research and game analytics to help understand how players navigate game environments. However, the complexity of movement calls for visual solutions that clearly define and communicate patterns in player behavior. In this paper, we address this challenge by introducing a method for visualizing aggregated player trajectories, also in conjunction with other behavioral metrics. This way movement is not viewed in isolation but contextualized within the broader player behavior. Line integral convolution textures are used to summarize the structural patterns of the movement while additional data can be displayed simultaneously through encoding it in the visual channels of the texture. Further, α -shapes are used to highlight and describe the spatial shape of the traversed parts of the game environment. We demonstrate the approach by applying it to the popular esports games Dota 2 and Starcraft: Brood War and discuss its generalizability within and outside esports.

With the growth of competitive gaming and esports, training support tools that offer feedback on players’ performance to assist in skill development have witnessed increased demand. These tools increasingly not only provide prospective and retrospective analyses but also live feedback during gameplay itself. Thus, such ’live companions’ provide overarching training support across the different phases of play through a variety of features. To understand how these tools work, we carried out an analysis of the features offered by commercially available live companions. For our analysis, we selected 15 live companion tools for two popular competitive games, namely Valorant and League of Legends. These games are representatives of first person shooters and multiplayer online battle arena games. Based on our analysis, we provide an overview of the various features offered by such tools, how frequent these features are, and if there are differences between the two games. Finally, we reflect on potential future research directions for this emerging topic of study.

Repeated measurements of player experience are crucial in games user research, assessing how different designs evolve over time. However, this necessitates lightweight measurement instruments that are fit for the purpose. In this study, we conduct an examination of the test-retest reliability of the miniPXI -a short variant of the Player Experience Inventory (PXI) , an established measure for measuring player experience. We analyzed test-retest reliability by leveraging four games involving 100 participants, comparing it with four established multi-item measures and single-item indicators such as the Net Promoter Score ( NPS ) and overall enjoyment. The findings show mixed outcomes; the miniPXI demonstrated varying levels of test-retest reliability. Some constructs showed good to moderate reliability, while others were less consistent. On the other hand, multi-item measures exhibited moderate to good test-retest reliability, demonstrating their effectiveness in measuring player experiences over time. Additionally, the employed single-item indicators ( NPS and overall enjoyment) demonstrated good reliability. The results of our study highlight the complexity of player experience evaluations over time, utilizing single and multiple items per construct measures. We conclude that single-item measures may not be appropriate for long-term investigations of more complex PX dimensions and provide practical considerations for the applicability of such measures in repeated measurements.

Recent years have shown that we are steadily getting closer to industrial applications of quantum computing. As such it is important to teach quantum computing concepts to users to allow them to incorporate quantum computing into their toolbox. As educational research has shown the potential of game-based learning in the past years, we are thus proposing QRogue, an educational game with Rogue-like elements targeted at computer science students. The game’s goal is to teach the math behind quantum computing in a playful environment with analogies to this technology’s counter-intuitive fundamentals. To gather first feedback and stir the further development, we conducted a user study – involving playtesting and a post-experience survey – with eight students showing that the game was positively received but requires further tuning of the onboarding process and of the in-game feedback provided to players.

Players increasingly adopt a data-driven approach to review and improve their gaming skills. In the wake of this, spatio-temporal visualizations gained popularity but remain challenging to design. Storyline visualizations are unique in the way they integrate time and location information into a single view to show how entity relationships develop over time. We adopt the storyline visualization technique to summarize gameplay for the purpose of post-play review. We demonstrate the method by applying it to League of Legends matches and evaluated it with 39 players of the game in a task-based online study using the triad framework for spatio-temporal queries by Peuquet. Results indicate that players responded positively to the approach and could, by and large, solve tasks well but that time-based tasks proved most challenging and least efficient to solve. Based on our findings, we reflect on possibilities for enhancing the design of storyline visualizations for game-related data analysis.

Multi-agent systems require coordination among the agents to solve a given task. For movement on fixed-track networks, traditional scheduling algorithms have dominated so far, but the interest in autonomous and intelligent agents is growing as they promise to react to unexpected and exceptional situations more robustly. In this paper, we study data from the Flatland 2020 NeurIPS Competition, where trains move through a virtual rail network. We developed a timeline-based visualization that provides an overview of all train movements in a simulated episode, clearly hinting at different phases, non-optimal routes, and issues such as deadlocks. This view is complemented with a map view and a graph view, interactively linked through highlighting and synchronous animation. Defining regions of interest in the map builds an analysis graph for detailed inspection. A comparison mode allows contrasting two different episodes regarding the same rail network across all views. We have conducted this application study in close collaboration with the Flatland community. Identified analysis goals stem from interviews with key persons of the community, while the approach itself was developed in two iterations based on feedback from experts with diverse backgrounds. This feedback, together with an analysis of the winning submissions from the competition, confirms that the initial analysis goals can be answered.

One of the major benefits of virtual reality (VR) lies in its ability to create a heightened sense of presence through visual and auditory modalities. To further enhance presence, research has investigated various ways to allow for tactile perception of objects in virtual environments. Among the different dimensions of tactile perception, thermal feedback is less explored, especially in the context of games which renders many existing approaches impractical due to their interference with game controllers. In this paper, we present Ethermal - a thermal feedback solution - designed to a) convey temperature information through the player’s hands while b) still permitting the use of standard VR game controllers. An evaluation with 10 players, using the device in a custom-made virtual environment to demonstrate different use cases of thermal feedback, suggests that an increased feel of general and spatial presence and experienced realism can be achieved even with simple technological means.

Eye movement data can be used for a variety of research in marketing, advertisement, and other design-related industries to gain interesting insights into customer preferences. However, interpreting such data can be a challenging task due to its spatio-temporal complexity. In this paper we describe a web-based tool that has been developed to provide various visualizations for interpreting eye movement data of static stimuli. The tool provides several techniques to visualize and analyze eye movement data. These visualizations are interactive and linked in a coordinated way to help gain more insights. Overall, this paper illustrates the features and functionality offered by the tool by using data recorded from transport map readers in a previously conducted experiment as use case. Furthermore, the paper discusses limitations of the tool and possible future developments.

With competitive gaming on the rise, players have developed an increased interest in data-driven feedback of their in-game performance. In many cases, this feedback is presented visually post-play but live companions that provide real-time analytics are gaining more and more attention. However, the design of such systems poses different requirements which have been little explored from a scholarly point of view. In this paper, we report on a study of two visualizations - a physical and digital one - providing live feedback to League of Legends players designed based on a community survey (N= 175) and expert input. We further discuss results of a qualitative evaluation with 13 players and an expert, touch upon potential benefits and drawbacks of physical and digital implementations, and present derived design considerations and challenges for such real-time data visualizations, revolving around issues of information density, contextualization, representation, and a diverse range of user needs.

With the rise of competitive online gaming and esports, players’ ability to review, reflect upon, and improve their in-game performance has become important. Post-play visualizations are key for such improvements. Despite the increased interest in visualizations of gameplay, research specifically informing the design of player-centric visualizations is currently limited. As with all visualizations, their design should, however, be guided by a thorough understanding of the goals to be achieved and which information is important and why.

In this paper we describe a visualization tool for representing the dynamics of graph algorithms. Toward this end, we designed a web-based framework which illustrates the dynamics as time-to-space mappings of dynamic graphs. Such static diagrams of dynamic data have the benefit of being able to display longer time spans in one view, hence supporting the observer with comparison tasks. The tool can show details about how an algorithm traverses a graph step-by-step in a static and animated fashion, for graph algorithm exploration as well as educational purposes. The animation together with the time-to-space mapping forms an overview-and-detail approach. By using flight carrier data from the U.S. Department of Transportation we show the usefulness of our interactive visualization for conveying graph algorithm dynamics.

Playtesting of games often relies on a mixed-methods approach to obtain more holistic insights about and, in turn, improve the player experience. However, triangulating the different data sources and visualizing them in an integrated manner such that they contextualize each other still proves challenging. Despite its potential value for gauging player behaviour, this area of research continues to be underexplored. In this paper, we propose a visualization approach that combines commonly tracked movement data with - from a visualization perspective rarely considered - gaze behaviour and emotional responses. We evaluated our approach through a qualitative expert study with five professional game developers. Our results show that both the individual visualization of gaze, emotions, and movement but especially their combination are valuable to understand and form hypotheses about player behaviour. At the same time, our results stress that careful attention needs to be paid to ensure that the visualization remains legible and does not obfuscate information.

Game studios aim to develop titles that deliver a fun and engaging experience for players. Playtesting promises to help identify opportunities to improve player experience and assist developers in achieving their design intent. However, a lack of research on the added value of playtesting means that many studios are still uncertain about its commercial viability and impact on product success. This gap in understanding is further complicated by the vague definition of "success" afforded by sales figures and review scores. In this paper, we assess reported feature quality of three commercial titles by analyzing playtesting reports and game reviews. By comparing themes and design issues expressed in game reviews to the results of pre-release playtesting for each game, we aim to highlight the value of playtesting and propose a set of guidelines for selecting playtest methods based on the needs of a given game evaluation. Through the real-world case studies presented, this paper contributes to the growing domain of games user research and highlights the value of playtesting in game development.

Human playtesting is a useful step in the game development process, but involves high economic costs and is time-consuming. While playtesting through artificial intelligence is gaining attention, it is challenging to analyze the collected data. We address the challenge by proposing visualizations to derive insights about level design in 2D side-scrolling games. To focus on the navigation behavior in the level design, we study the trajectories of computer agents trained using artificial intelligence. We demonstrate the effectiveness of our approach by implementing a web-based prototype and presenting the insights gained from the visualizations for the game Sonic the Hedgehog 2. We highlight lessons learned and directions to customize the approach for other analysis goals of playtesting.

In this paper we present a procedural city modeling approach which combines real-world street data and the multi-nuclei theory to generate believable cities. Our approach performs a partitioning of a city into urban zones based on Perlin noise and different parameters that are adjustable by the user. Our method is efficient enough to be used for augmented reality applications to be run on devices with limited processing capabilities such as smartphones. The approach can be used to build applications for professional urban planners and entertainment purposes. We illustrate the usefulness of our approach by applying it to data of three cities. Moreover, we provide implementation details and discuss challenges and limitations of the technique in the light of several application scenarios.

Soccer is one of the most popular sports in the world, played by thousands of professionals and amateurs every week. Consequently, it is no surprise that it generates an enormous amount of data. In today's data-driven world it is essential to find an optimal, self-explanatory, way to present the data in a way to be able to derive visual patterns that relate to the underlying data patterns. In this paper, we describe an interactive visualization for analyzing soccer data and identifying patterns, correlations, and insights. We illustrate the usefulness of our approach, especially targeted towards non-visualization experts, by applying it to World Cup data and by discussing potential use cases.

Abstract Competition and collaboration form complex interaction patterns between the agents and objects involved. Only by understanding these interaction patterns, we can reveal the strategies the participating parties applied. In this paper, we study such competition and collaboration behavior for a computer game. Serving as a testbed for artificial intelligence, the multiplayer bomb laying game Pommerman provides a rich source of advanced behavior of computer agents. We propose a visualization approach that shows an overview of multiple games, with a detailed timeline‐based visualization for exploring the specifics of each game. Since an analyst can only fully understand the data when considering the direct and indirect interactions between agents, we suggest various visual encodings of these interactions. Based on feedback from expert users and an application example, we demonstrate that the approach helps identify central competition strategies and provides insights on collaboration.

Playtesting is a key component in the game development process aimed at improving the quality of games through the collection of gameplay data and identification of design issues. Visualization techniques are currently being employed to help integrate quantitative and qualitative data. Despite that, two existing challenges are to determine the level of detail to be presented to developers based on their needs and to effectively communicate the collected data so that informed design changes can be reached. In this paper, we first propose an aggregated visualization technique that makes use of clustering, territory tessellation, and trajectory aggregation to simultaneously display mixed playtesting data. Secondly, to assess the usefulness of our technique we evaluate it through interviews with professional game developers and compare it to a non-aggregated visualization. The results of this study also provide an important contribution towards identifying areas of improvement in the portrayal of gameplay data.

So-called battle maps are an appropriate way to visually summarize the flow of battles as they happen in many team-based combat games. Such maps can be a valuable tool for retrospective analysis of battles for the purpose of training or for providing a summary representation for spectators. In this paper an extension to the battle map algorithm previously proposed by the author [1] and which addresses a shortcoming in the depiction of troop movements is described. The extension does not require alteration of the original algorithm and can easily be added as an intermediate step before rendering. The extension is illustrated using gameplay data from the team-based multiplayer game World of Tanks.

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.

Social features, matchmaking, and grouping functions are key elements of online multiplayer 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 the 100.io-a social matchmaking website for different commercial titles, including Destiny on which we focus in this paper. Groups formed on the 100.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.

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.

Online gaming has taken on an indispensable role in popular culture and with it interest in understanding all types of player communities - be it directly within or around a game - shifted into the focus of attention of game developers. While research on player networks has proliferated, visualization of these communities and networks has received little consideration. In this paper we introduce PlaNet, a tool specifically targeted toward visualizing Player Networks. We reflect on the present state of development and utilize a social network derived from data from the matchmaking website the 100.io to demonstrate its current visualization capabilities.

Playful activities are regarded to be a promising way for fostering children's geometric thinking and understanding. In this paper we introduce Lost My Way, an educational game about geometric transformations targeted at middle school children of about 10 years of age. We describe the design of the game and report preliminary results and experiences gained from the first two stages of our iterative development process which included a playtesting session and an on-site evaluation at a local middle school.

User research has become an important aspect in game development. Especially, user telemetry, i.e., the remote tracking of in-game behavioral data and its subsequent analysis has attracted increased attention in game development and research in the last years. Unfortunately, case studies of game industry-academic relationships concerning the application of user telemetry methods in practice are still sparse. In this case study we report our experiences from employing gameplay visualizations during the development of a commercial mobile game. In particular, we describe the process of the collaboration and discuss developer feedback regarding the visualization itself as well as issues regarding user telemetry integration into the development process.

With the rising interest in multiplayer gaming, gameplay statistics have become an increasingly important aspect of the overall game experience for many players. As a part of this trend, visualizations have gained great popularity among players, in particular heatmaps since they allow them to reenact the course of a game and to develop new strategies. In this paper we report results of a user study conducted with 29 players (i) to investigate how players use heatmaps and two further graphical representations that use clustering algorithms to interpret gameplay and (ii) to assess the three representations in regard to time efficiency, correctness, suitability, and player preference. Our results show that heatmaps were mainly used to detect hot spots while the cluster representations proved useful to compare variables, allowing the players to uncover relationships between them and in turn allowing a deeper insight into the gameplay data.

Contemporary video games are highly complex systems with many interacting variables. To make sure that a game provides a satisfying experience, a meaningful analysis of gameplay data is crucial, particularly because the quality of a game directly relates to the experience a user gains from playing it. Automatic instrumentation techniques are increasingly used to record data during playtests. However, the evaluation of the data requires strong analytical skills and experience. The visualization of such gameplay data is essentially an information visualization problem, where a large number of variables have to be displayed in a comprehensible way in order to be able to make global judgments. This paper presents a visualization tool to assist the analytical process. It visualizes the game space as a set of nodes which players visit over the course of a game and is also suitable to observe time-dependent information, such as player distribution. Our tool is not tailored to a specific type of genre. To show the flexibility of our approach we use two different kinds of games as case studies.

With increasing popularity of ontologies in various communities, visualizations of their content and structure became more and more important. In the past few years a number of visualization approaches were developed with the focus either on the representation of the relationships between classes or on the hierarchical structure and instances. However for several applications, a visualization which combines information about instances, classes and hierarchical as well as non-hierarchical relationships is from interest. In this paper we present Knoocks (Knowledge Blocks), which is a visualization approach with focus on both the interconnections within the ontology and the instances in conjunction with their hierarchical structure.