DAX: Data-Driven Audience Experiences in Esports

被引:18
|
作者
Kokkinakis, Athanasios V. [1 ]
Demediuk, Simon [1 ]
Nolle, Isabelle [1 ]
Olarewaju, Oluseyi [1 ]
Patra, Sagarika [1 ]
Robertson, Justus [1 ]
York, Peter [1 ]
Chitayat, Alan Pedrassoli [1 ]
Coates, Alistair [1 ]
Slawson, Daniel [1 ]
Hughes, Peter [2 ]
Hardie, Nicolas [2 ]
Kirman, Ben [1 ]
Hook, Jonathan [1 ]
Drachen, Anders [1 ]
Ursu, Marian F. [1 ]
Block, Florian [1 ]
机构
[1] Univ York, York, N Yorkshire, England
[2] REWIND FX LTD, St Albans, England
基金
“创新英国”项目; 英国工程与自然科学研究理事会;
关键词
Esport; Data-Driven Storytelling; Dota; 2; Game Analytics; Artificial Intelligence; Machine Learning; AI; Broadcasting; social viewing;
D O I
10.1145/3391614.3393659
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:94 / 105
页数:12
相关论文
共 50 条
  • [41] DATA-DRIVEN PROTOTYPING
    APPLETON, DS
    DATAMATION, 1983, 29 (11): : 259 - &
  • [42] Data-Driven Crazy
    Mercer, Kenneth L.
    JOURNAL AWWA, 2025, 117 (02): : 1 - 1
  • [43] Data-Driven Personas
    Jansen, Bernard J.
    Salminen, Joni
    Jung, Soon-Gyo
    Guan, Kathleen
    Synthesis Lectures on Human-Centered Informatics, 2021, 14 (01):
  • [44] Data-Driven Dialectology
    Nerbonne, John
    LANGUAGE AND LINGUISTICS COMPASS, 2009, 3 (01): : 175 - 198
  • [45] Data-Driven Design
    Schmidt, Aaron
    LIBRARY JOURNAL, 2016, 141 (06) : 26 - 26
  • [46] THE DATA-DRIVEN SOCIETY
    Pentland, Alex Sandy
    SCIENTIFIC AMERICAN, 2013, 309 (04) : 78 - 83
  • [47] Data-driven Simulation
    Lazarova-Molnar, Sanja
    2022 SIGNAL PROCESSING: ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS (SPA), 2022, : 18 - 18
  • [48] Data-driven clinical improvement: Oncology nurse leaders' perceptions and experiences of organisational data reports
    Mazzella-Ebstein, Ann M.
    Paradiso, Cynthia
    Lynch, Kathleen
    Arnetz, Judith E.
    Barton-Burke, Margaret
    JOURNAL OF NURSING MANAGEMENT, 2022, 30 (07) : 3139 - 3148
  • [49] EXPERIENCES FROM THE USE OF DATA-DRIVEN DECISION-SUPPORT IN DIFFERENT ENVIRONMENTS
    AHLFELDT, H
    JOHANSSON, B
    LINNARSSON, R
    WIGERTZ, O
    COMPUTERS IN BIOLOGY AND MEDICINE, 1994, 24 (05) : 397 - 404
  • [50] Data-Driven MoE: A Data-Driven Approach to Construct MoE by a Single LLM
    Teng, Zeyu
    Yan, Zhiwei
    Song, Yong
    Ye, Xiaozhou
    Ouyang, Ye
    ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PT IV, ICIC 2024, 2024, 14878 : 352 - 363