Data Preprocessing of eSport Game Records - Counter-Strike: Global Offensive

Bednárek, David; Kruliš, Martin; Yaghob, Jakub; Zavoral, Filip

doi:10.5220/0006475002690276

Cited by 12 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their goal is to assess and predict player performance. We envision that our work can be instrumental to reproduce and expand previous efforts such as those reported in [Bednárek et al 2017].…”

Section: Related Workmentioning

confidence: 83%

“…Some of the first papers involving crawling and analysis of gaming data focused on Second Life Voelker 2010, Varvello et al 2008]. Among works focusing on Counter Strike, the two more closely related to ours are [Xenopoulos et al 2020] and [Bednárek et al 2017]. In [Xenopoulos et al 2020] the authors provide an open source platform to collect data from CS:GO.…”

Section: Related Workmentioning

confidence: 99%

“…In [Bednárek et al 2017] the authors analyze data from HLTV. Their goal is to assess and predict player performance.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Extracting and Composing a Dataset of Competitive Counter-Strike Global Offensive Matches

Rocha

Maio

Menasché

et al. 2021

Anais Do III Dataset Showcase Workshop (DSW 2021)

View full text Add to dashboard Cite

There is a growing necessity for insightful and meaningful analyticswithin eSports: be it to entertain spectators as they watch their favorite teamscompete, to automatically identify and catch cheaters or even to gain a com-petitive edge over an opponent, there is a plethora of potential applicationsfor analytics within the scene. It follows then, that there is also a necessityfor well structured and organized datasets that enable efficient data explorationand serve as the foundation for the visualization and analytics layers. Becauseof this, the entire process - from data collection at the source to the means ofaccessing the desired information - need to be planned out to address thoseneeds. Our work provides the means by which to construct such a dataset forthe Counter-Strike Global Offensive (CS:GO) game, thus opening up a range ofpossible applications on top of the data

show abstract

Section: Related Workmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Extracting and Composing a Dataset of Competitive Counter-Strike Global Offensive Matches

Rocha

Maio

Menasché

et al. 2021

Anais Do III Dataset Showcase Workshop (DSW 2021)

View full text Add to dashboard Cite

show abstract

“…The demo file contains a serialization of all client-server communications. When a demo file is recording, it writes data in tick bursts, which contain around eight ticks [3]. While a demo file can be recorded by a client, we focus solely on server-recorded demo files, as they tend to be the predominant source of publicly available demo files.…”

Section: Csgo Datamentioning

confidence: 99%

“…However, the tools for data acquisition are surprisingly limited, and if they exist, undocumented [5]. Furthermore, esports data is often stored in esoteric formats that are riddled with inconsistencies and lacks a commonly accepted data model [3]. Thus, most data in esports exists only as simple counting statistics, as opposed to complex spatiotemporal and event data seen in other sports [16].…”

Section: Introductionmentioning

confidence: 99%