Theatres and concert halls play a crucial role within the performing arts, where managerial and administrative staff are essential to bringing live performances to audiences. Existing AI research has focused on artistic creation, but less attention has been paid to the purposeful design of AI systems that support organisational practices. This paper addresses this gap by identifying the needs, challenges and opportunities for AI integration into everyday workflows, forming the basis for design principles to guide the architecturing, training, and deployment of AI systems that empower staff, rather than replace them. This is explored through a co-design workshop with theatre marketing and communication professionals. Through reflections of the themes explored in the workshop and by following the guiding principles, this paper presents examples of implementation of AI systems that could be adopted, offering concrete directions for developing AI that benefits the cultural sector.

Games livestreaming has become an invaluable tool for game studios, supporting game discoverability, community building, and community management. Understanding the different forms of success on livestreaming platforms such as Twitch, along with the relevant metrics and target benchmarks, is crucial for maximising engagement. Similarly, gaining insight into how short-term success influences long-term performance can empower studios to strategically plan, design, and implement future content or game releases. However, the diverse ways in which games perform on Twitch present challenges for detailed analysis. To address this, this paper applies unsupervised machine learning techniques to identify and present seven archetypes of success, enabling studios to gain deeper insights into their game's performance.

Esports has become a popular field of research, enabling advances in areas such as machine learning and environment modeling. However, complex modeling systems require complex visualisations. Despite that, visualisation of complex modeling systems within esports have been limited or fragmented, particularly when focused on the audience. Furthermore, the use of data visualisation and data-driven storytelling has been proven to be an effective and imperative method for enhancing audience experience for esport spectators. Therefore, this paper investigates data visualisation techniques within esports, and compiles design considerations for developing visualisation tools for esports broadcast. This is achieved through a case-study, in which the WARDS model was utilised in live coverage of a Dota 2 tournament and evaluated through observational data.

Esports - competitive video games watched by online audiences - are the fastest growing form of mainstream entertainment. Esports coverage is predominantly delivered via online video streaming platforms which include interactive elements. However, there is limited understanding of how audiences engage with such interactive content. This paper presents a large-scale case study of an interactive data-driven streaming extension developed for Dota 2, reaching over 300,000 people during the DreamLeague Season 15 DPC Western Europe tournament. The extension provides interactive live statistics, analysis and highlights reels of ongoing matches. This paper presents an analysis of audience telemetry collected over the course of the four week tournament, introducing a novel approach to analysing usage data delivered seamlessly in conjunction to a linear broadcast feed. The work presented advances our general understanding of the evolving consumption patterns in esports, and leverages esports as a lens to understand future challenges and opportunities in interactive viewing across sports and entertainment.

Data analytics is commonly used to enable storytelling and enhance esport coverage. One prominent use of it is win prediction, where machine learning models predict the winner of the game before its conclusion. However, predictions are most commonly results of black-box systems, forcing commentators to produce ad-hoc interpretations. Additionally, broadcasters generally rely other metrics to build narratives, limiting the impact of win prediction models for storytelling. This paper explores an alternative method to win prediction, identifying the needs of broadcasters to guide development of a novel win condition model. By focusing on existing storytelling points, the proposed win condition model can offer greater storytelling opportunities to broadcasters, focusing on the user needs identified from within the esport domain. Rather than utilising game state data to predict the winner, as it is usually done in win prediction, the proposed win condition model uses an exploration of the possible winners to predict the game state needed for each team to win. Lastly, the features identified for win condition are evaluated through a series of machine learning models, which provide a data-driven metric to test and predict win condition in the context of Dota 2, a popular esport title.

Esport is a large and still growing industry with vast audiences. Multiplayer Online Battle Arenas (MOBAs), a sub-genre of esports, possess a very complex environment, which often leads to experts missing important coverage while broadcasting live competitions. One common game event that holds significant importance for broadcasting is referred to as a team fight engagement. Professional player's own knowledge and understanding of the game may provide a solution to this problem. This paper suggests a model that predicts and detects ongoing team fights in a live scenario. This approach outlines a novel technique of deriving representations of a complex game environment by relying on player knowledge. This is done by analysing the positions of the in-game characters and their associated cameras, utilising this data to train a neural network. The proposed model is able to both assist in the production of live esport coverage as well as provide a live, expert-derived, analysis of the game without the need of relying on outside sources.

Esports (competitive videogames) have grown into a global phenomenon 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 data-driven 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.