Publications

Main Publications

From Passive Viewer to Active Fan: Towards the Design and Large-Scale Evaluation of Interactive Audience Experiences in Esports and Beyond

Abstract:

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.

cite Pedrassoli Chitayat, A., Coates, A., Block, F. O., Drachen, A., Walker, J. A., Dean, J., McConachie, M., & York, P. (2024, March). From Passive Viewer to Active Fan: Towards the Design and Large-Scale Evaluation of Interactive Audience Experiences in Esports and Beyond. In ACM International Conference on Interactive Media Experiences (IMX). ACM.
bibtex 

@inproceedings{chitayat2024passive,
  title={From Passive Viewer to Active Fan: Towards the Design and Large-Scale Evaluation of Interactive Audience Experiences in Esports and Beyond},
  author={Pedrassoli Chitayat, Alan and Coates, Alastair and Block, Florian Oliver and Drachen, Anders and Walker, James Alfred and Dean, James and McConachie, Mark and York, Peter},
  booktitle={ACM International Conference on Interactive Media Experiences (IMX)},
  year={2024},
  organization={ACM}
}

Applying and Visualising Complex Models in Esport Broadcast Coverage

Abstract:

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.

cite Pedrassoli Chitayat, A., Block, F. O., Walker, J. A., & Drachen, A. (2024, March). Applying and Visualising Complex Models in Esport Broadcast Coverage. In ACM International Conference on Interactive Media Experiences (IMX). ACM.
bibtex 

@inproceedings{pedrassoli2024applying,
  title={Applying and Visualising Complex Models in Esport Broadcast Coverage},
  author={Pedrassoli Chitayat, Alan and Block, Florian Oliver and Walker, James Alfred and Drachen, Anders},
  booktitle={ACM International Conference on Interactive Media Experiences (IMX)},
  year={2024},
  organization={ACM}
}

Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics

Abstract:

Esport games comprise a sizeable fraction of the global games market, and is the fastest growing segment in games. This has given rise to the domain of esports analytics, which uses telemetry data from games to inform players, coaches, broadcasters and other stakeholders. Compared to traditional sports, esport titles change rapidly, in terms of mechanics as well as rules. Due to these frequent changes to the parameters of the game, esport analytics models can have a short life-spam, a problem which is largely ignored within the literature. This paper extracts information from game design (i.e. patch notes) and utilises clustering techniques to propose a new form of character representation. As a case study, a neural network model is trained to predict the number of kills in a Dota 2 match utilising this novel character representation technique. The performance of this model is then evaluated against two distinct baselines, including conventional techniques. Not only did the model significantly outperform the baselines in terms of accuracy (85% AUC), but the model also maintains the accuracy in two newer iterations of the game that introduced one new character and a brand new character type. These changes introduced to the design of the game would typically break conventional techniques that are commonly used within the literature. Therefore, the proposed methodology for representing characters can increase the life-spam of machine learning models as well as contribute to a higher performance when compared to traditional techniques typically employed within the literature.

cite Pedrassoli Chitayat, A., Block, F., Walker, J., & Drachen, A. (2023, October). Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analitics. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 19, No. 1, pp. 116-125).
bibtex 

@inproceedings{chitayat2023beyond,
  title={Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analitics},
  author={Pedrassoli Chitayat, Alan and Block, Florian and Walker, James and Drachen, Anders},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment},
  volume={19},
  number={1},
  pages={116--125},
  year={2023}
}

What are you looking at? Team fight prediction through player camera

Abstract:

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 rely ng on outside sources.

cite Tot, M., Conserva, M., Pedrassoli Chitayat, A., Kokkinakis, A., Patra, S., Demediuk, S., … & Perez-Liebana, D. (2021, August). What are you looking at? Team fight prediction through player camera. In 2021 IEEE Conference on Games (CoG) (pp. 1-8). IEEE.
bibtex 

@inproceedings{tot2021you,
  title={What are you looking at? Team fight prediction through player camera},
  author={Tot, Marko and Conserva, Michelangelo and Pedrassoli Chitayat, Alan 
and Kokkinakis, Athanasios and Patra, Sagarika and Demediuk, Simon and Munoz, Alvaro Caceres 
and Olarewaju, Oluseji and Ursu, Marian and Kirmann, Ben and others},
  booktitle={2021 IEEE Conference on Games (CoG)},
  pages={1--8},
  year={2021},
  organization={IEEE}
}

WARDS: Modelling the Worth of Vision in MOBA’s

Abstract:

Multiplayer strategy games are examples of imperfect information games, where information about the game state can be retrieved through in-game mechanics. One such mechanic is vision. Within esports titles of this genre, such as League of Legends (LoL) and Dota 2, players often gather map information through the use of friendly units called wards. In LoL, one of the most popular esports title worldwide, warding has hitherto been evaluated only using a heuristic called vision score, provided by Riot, the game’s developer. In this paper, we examine the accuracy at LoL’s vision score at predicting the overall game-winner within the context supported by the game. We have ported LoL’s vision score to Dota 2, a similarly popular esports title, and compared its performance against a novel warding model. We have compared both models not only at predicting the overall winner, but also the current state of the game and their ability to predict and reflect short term game advantage and events. We found our model significantly outperformed LoL’s vision score. Additionally, we trained and evaluated a model for predicting the value of wards in real-time through the use of a Neural Network.

cite Pedrassoli Chitayat, A., Kokkinakis, A., Patra, S., Demediuk, S., Robertson, J., Olarewaju, O., … & Drachen, A. (2020). WARDS: Modelling the Worth of Vision in MOBA’s. In Intelligent Computing: Proceedings of the 2020 Computing Conference, Volume 2 (pp. 63-81). Springer International Publishing.
bibtex 

@inproceedings{pedrassoli2020wards,
  title={WARDS: Modelling the Worth of Vision in MOBA’s},
  author={Pedrassoli Chitayat, Alan and Kokkinakis, Athanasios and Patra, Sagarika 
and Demediuk, Simon and Robertson, Justus and Olarewaju, Oluseji and Ursu, Marian 
and Kirmann, Ben and Hook, Jonathan and Block, Florian and others},
  booktitle={Intelligent Computing: Proceedings of the 2020 Computing Conference, Volume 2},
  pages={63--81},
  year={2020},
  organization={Springer}
}

Other Publications

Time to Die 2: Improved in-game death prediction in Dota 2

Abstract:

Competitive video game playing, an activity called esports, is increasingly popular to the point that there are now many professional competitions held for a variety of games. These competitions are broadcast in a professional manner similar to traditional sports broadcasting. Esports games are generally fast paced, and due to the virtual nature of these games, camera positioning can be limited. Therefore, knowing ahead of time where to position cameras, and what to focus a broadcast and associated commentary on, is a key challenge in esports reporting. This gives rise to moment-to-moment prediction within esports matches which can empower broadcasters to better observe and process esports matches. In this work we focus on this moment-to-moment prediction and in particular present techniques for predicting if a player will die within a set number of seconds for the esports title Dota 2. A player death is one of the most consequential events in Dota 2. We train our model on ‘telemetry’ data gathered directly from the game itself, and position this work as a novel extension of our previous work on the challenge. We use an enhanced dataset covering 9,822 Dota 2 matches. Since the publication of our previous work, new dataset parsing techniques developed by the WEAVR project enable the model to track more features, namely player status effects, and more importantly, to operate in real time. Additionally, we explore two new enhancements to the original model: one data-based extension and one architectural. Firstly we employ learnt embeddings for categorical features, e.g. which in game character a player has selected, and secondly we explicitly model the temporal element of our telemetry data using recurrent neural networks. We find that these extensions and additional features all aid the predictive power of the model achieving an F1 score of 0.54 compared to 0.17 for our previous model (on the new data). We improve this further by experimenting with the length of the time-series in the input data and find using 15 time steps further improves the F1 score to 0.62. This compares to F1 of 0.1 for a standard RNN on the same task. Additionally a deeper analysis of the Time to Die model is carried out to assess its suitability as a broadcast aid.

cite Ringer, C., Missaoui, S., Hodge, V. J., Chitayat, A. P., Kokkinakis, A., Patra, S., … & Walker, J. A. (2023). Time to die 2: Improved in-game death prediction in dota 2. Machine Learning with Applications, 12, 100466.
bibtex 

@article{ringer2023time,
  title={Time to die 2: Improved in-game death prediction in dota 2},
  author={Ringer, Charles and Missaoui, Sondess and Hodge, Victoria J 
and Chitayat, Alan Pedrassoli and Kokkinakis, Athanasios and Patra, Sagarika 
and Demediuk, Simon and Munoz, Alvaro Caceres and Olarewaju, Oluseji 
and Ursu, Marian and others},
  journal={Machine Learning with Applications},
  volume={12},
  pages={100466},
  year={2023},
  publisher={Elsevier}
}

Metagaming and Metagames in Esports

Abstract:

The metagame is an overloaded term with no unified definition despite its importance and its common occurrence across different fields such as game design and behavioural economics. In our research we provide a unified and compact definition of the term metagame and metagaming by firstly highlighting their historical evolution. Although the original definition of metagame meant multiple things such as the environment surrounding the game, it has come to mean a perceived optimal or dominant playing strategy that is usually popular within an esport at that specific point in time. Metagaming as a verb is distinct and refers to a number of ways, external to the game’s environment, a player can affect the outcome of a game. We focus on how these terms crystallised in the world of digital entertainment (esports) by providing multiple examples of metagames and metagaming in competitive settings. We additionally highlight the benefits of metagame shifts from the point of view of game developers. Finally, we provide a theoretical framework on the life cycles of metagames, as well general guidelines for understanding the current metagame of League of Legends and Dota 2. We conclude that by understanding the highly fluctuating metagame(s) of an esport at specific points in time, researchers will gain a better historical context of that game’s space. This in turn will give them insight into the decision making of professional esports players, game developers and tournament organisers. Additionally, this work will help researchers create better analytics tools and machine learning algorithms.

cite Kokkinakis, A., York, P., Patra, M., Robertson, J., Kirman, B., Coates, A., … & Block, F. O. (2021). Metagaming and metagames in Esports. International Journal of Esports.
bibtex 

@article{kokkinakis2021metagaming,
  title={Metagaming and metagames in Esports},
  author={Kokkinakis, Athanasios and York, Peter and Patra, Moni 
and Robertson, Justus and Kirman, Ben and Coates, Alistair 
and Pedrassoli Chitayat, Alan and Demediuk, Simon Peter 
and Drachen, Anders and Hook, Jonathan David and others},
  journal={International Journal of Esports},
  year={2021},
  publisher={York}
}

Performance Index: A New Way To Compare Players

Contribution Summary:

The Performance Index considers the playstyle of each player and operates in real-time, facilitating real-time storytelling and audience engagement. The PI has been successfully deployed at multiple major Dota 2 tournaments across Europe in 2020. Three different platforms have been used to deliver the index, including broadcast overlays, Twitch overlays, and a companion mobile app. While developed for Dota 2, the principles behind the Performance Index is not limited to esports but can be applied in a similar way to other team-based, multi-role sports such as basketball,
baseball, and football.

cite Demediuk, S., Kokkinakis, A., Patra, M. S., Robertson, J., Kirman, B., Coates, A., … & Drachen, A. (2021). Performance index: A new way to compare players. In 2021 MIT Sloan Sports Analytics Conference. IEEE.
bibtex 

@inproceedings{demediuk2021performance,
  title={Performance index: A new way to compare players},
  author={Demediuk, Simon and Kokkinakis, Athanasios and Patra, Moni Sagarika and Robertson, Justus and Kirman, Ben and Coates, Alistair and Chitayat, Alan and Hook, Jonathan and Nolle, Isabelle and Olarewaju, Oluseyi and others},
  booktitle={2021 MIT Sloan Sports Analytics Conference. IEEE},
  year={2021}
}

DAX: Data-Driven Audience Experiences in Esports

Abstract:

Esports (competitive video games) 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 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.

cite Kokkinakis, A. V., Demediuk, S., Nölle, I., Olarewaju, O., Patra, S., Robertson, J., … & Block, F. (2020, June). Dax: Data-driven audience experiences in esports. In ACM International Conference on Interactive Media Experiences (pp. 94-105).
bibtex 

@inproceedings{kokkinakis2020dax,
  title={Dax: Data-driven audience experiences in esports},
  author={Kokkinakis, Athanasios Vasileios and Demediuk, Simon and N{\"o}lle, Isabelle 
and Olarewaju, Oluseyi and Patra, Sagarika and Robertson, Justus and York, Peter 
and Pedrassoli Chitayat, Alan Pedrassoli and Coates, Alistair and Slawson, Daniel and others},
  booktitle={ACM International Conference on Interactive Media Experiences},
  pages={94--105},
  year={2020}
}

Automatic Generation Of Text For Match Recaps Using Esport Caster Commentaries

Abstract:

Unlike traditional physical sports, Esport games are played using wholly digital platforms. As a consequence, there exists rich data (in-game, audio and video) about the events that take place in matches. These data offer viable linguistic resources for generating comprehensible text descriptions of matches, which could, be used as the basis of novel text-based spectator experiences. We present a study that investigates if users perceive text generated by the NLG system as an accurate recap of highlight moments. We also explore how the text generated supported viewer understanding of highlight moments in two scenarios: i) text as an alternative way to spectate a match, instead of viewing the main broadcast; and ii) text as an additional information resource to be consumed while viewing the main broadcast. Our study provided insights on the implications of the presentation strategies for use of text in recapping highlight moments to Dota 2 spectators.

cite Olarewaju, O., Kokkinakis, A. V., Demediuk, S., Roberstson, J., Nölle, I., Patra, S., … & Hook, J. (2020). Automatic Generation of Text for Match Recaps using ESport Caster Commentaries. Computer Science and Information Technology, 12(2), 117-131.
bibtex 

@article{olarewaju2020automatic,
  title={Automatic Generation of Text for Match Recaps using ESport Caster Commentaries},
  author={Olarewaju, Oluseyi and Kokkinakis, Athanasios V and Demediuk, Simon 
and Roberstson, Justus and N{\"o}lle, Isabelle and Patra, Sagarika and Slawson, Daniel 
and Chitayat, Alan P and Coates, Alistair and Kirman, Ben and others},
  journal={Computer Science and Information Technology},
  volume={12},
  number={2},
  pages={117--131},
  year={2020}
}