DAX: Data-Driven Audience Experiences in Esports

Esports(competitivevideogames)havegrownintoaglobalphenomenon with over 450m viewers and a 1.5bn USD market. Esports broadcasts follow a similar structure to traditional sports. However, due to their virtual nature, a large and detailed amount data is available about in-game actions not currently accessible in traditional sport. This provides an opportunity to incorporate novel insights about complex aspects of gameplay into the audience experience – enabling more in-depth coverage for experienced viewers, and increased accessibility for newcomers. Previous research has only explored a limited range of ways data could be incorporated into esports viewing (e.g. data visualizations post-match) and only a few studies have investigated how the presentation of statistics impacts spectators’ experiences and viewing behaviors. We present Weavr, a companion app that allows audiences to consume datadriven insights during and around esports broadcasts. We report on deployments at two major tournaments, that provide ecologically valid findings about how the app’s features were experienced by audiences and their impact on viewing behavior. We discuss implications for the design of second-screen apps for live esports events, and for traditional sports as similar data becomes available for them via improved tracking technologies

Visual analytics and team strategies in online games

Tese de Mestrado em Informática, Faculdade de Ciências, Universidade de Lisboa, 2022The eSports (electronic sports) phenomenon has been growing and so does the interest in online video games, from players and spectators. With technological advancements it has become easier to use techniques to retrieve data about the events occurring during a game, generating big volumes of data that can be used for a performance analysis. Casual players are looking for methods to better themselves overall or with specific characters, whereas, in a professional context, the focus is to study other teams and how to defeat them. For efficiency, it is imperative to explore data analysis mechanisms combined with visualisation techniques (visual analytics) applied to spatiotemporal data and to various relevant events during a match such as a player’s position (space) in a given instant (time) or, for example, the position where the player died. The goal of this project is the study of previous work and the development and ap plication of the acquired knowledge in analytic visualisation techniques to League of Legends[31] (LoL) spatiotemporal datasets. The developed tool used Tableau Desktop[24] to create a series of dashboards depicting the behaviour of multiple LoL matches, using the Riot API (Application Programming Interface) provided dataset, and clustering algorithms. The tool was evaluated by a team of semi-professional players in order to understand if the visualisation techniques and data used was adequate, useful or innovative compared to already existing tools for game analysis and the players’ needs. The results were mostly positive, with the participants pointing out the interactivity of the visualisations and ability of analysing multiple games as an advantage compared to existing tools. To conclude, even though spatiotemporal data is not yet implemented in MOBA (Multiplayer Online Battle Arena) videogame analysis tools, it is still relevant for the players’ personal goals and overall an interesting approach

VisuaLeague III: Visual Analytics of Multiple Games

Tese de mestrado, Informática, Universidade de Lisboa, Faculdade de Ciências, 2020Digital data available has been growing over the last years and with it, the need to create representative ways to understand and make use of its potential with visualization techniques that can be applied in different purposes. One of these cases are eSports (electronic sports), considered nowadays a sport with high growth expectation, and for which data analyses can have a significant impact. One of the most popular game type practiced in eSports is the Multiplayer Online Battle Arena (MOBA) genre represented by one of the most popular competitive games, League of Legends (LoL), which will be the case study for this thesis. As many traditional sports, there are various events to have in consideration when observing performance of gameplay. In addition to statistics for each game there is relevant information on players’ positions (spatial data), in a specific period in time (temporal data). Specific events in a game, related with objectives, can also be considered, such as purchasing an item, player kills, destroying towers, or complete objectives. Having a way to analyze and visualize this data helps not only programmers and game designers to improve gameplay but also players, coaches and analysts to improve player performance. The objective of this work is to redesign the previous prototype VisuaLeague II, and propose a new version, VisuaLeague III in order to explore techniques to implement analysis for multiple games, team searches and access to professional games’ training sections, scrims. Common problems presented in the analysis with voluminous amount of data, like cluttering and overlapping, are addressed by adding filters to searches, interaction with the visualizations, aggregation of data, and clustering. The developed prototype, VisuaLeague III was evaluated by professional coaches to understand if the searches and visualization techniques implemented are adequate for analysing players’ performance in a competitive environment. The results demonstrate overall positive attitude with particular interest in analysis for custom games and multiple games analysis as those provide visualizations that do not exist in common tools, specially, regarding spatiotemporal data

Enhancing battle maps through flow graphs

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

Enhancing battle maps through flow graphs

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