Modeling Individual and Team Behavior through Spatio-temporal Analysis

Ahmad, Shabir; Bryant, Andy; Kleinman, Erica; Teng, Zhaoqing; El-Nasr, Magy Seif

doi:10.1145/3311350.3347188

Cited by 37 publications

(31 citation statements)

References 33 publications

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“…The results of this quantitative analysis were combined with the results of a qualitative analysis of think aloud data also collected during the play sessions [24]. It is clear that there is a need for more mixed methods approaches to game data analytics [2,31,66], especially for ones that can examine gameplay data at scale, while preserving context. In the next section we will provide a concise background on Interactive Behavior Analytics (IBA) [2] and sequence analysis (SA), and then outline our proposed technique to combine the two to create a scalable, generalizable, context sensitive, data driven gameplay analysis methodology.…”

Section: Mixed Methods Approaches To Gameplay Analysismentioning

confidence: 99%

“…Another collection of work has sought to leverage visualization towards the preservation of spatial context [2,37,[60][61][62][63]. Moura et al proposed a system that allows analysts to interact with telemetry data, specifically different actions, superimposed atop the game map [37].…”

Section: Rnn Approachesmentioning

confidence: 99%

“…Previous work has addressed this issue with the development of Interactive Behavior Analytics (IBA), a mixed methods approach to game data analysis that allows for analysts to preserve gameplay context through action sequences and visualization systems [2,42]. The approach was successful in allowing a human-in-the-loop to draw meaningful conclusions about players' strategies, problem solving, and reactions to situations in a dynamic game environment [2]. However, in IBA the labeling is not informed by the data, rather by experience and what the users see in the spatio-temporal visualization of the action.…”

Section: Introductionmentioning

confidence: 99%

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"And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis

Kleinman¹,

Ahmad²,

Teng³

et al. 2020

Preprint

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Many successful games rely heavily on data analytics to understand players and inform design. Popular methodologies focus on machine learning and statistical analysis of aggregated data. While effective in extracting information regarding player action, much of the context regarding when and how those actions occurred is lost. Qualitative methods allow researchers to examine context and derive meaningful explanations about the goals and motivations behind player behavior, but are difficult to scale. In this paper, we build on previous work by combining two existing methodologies: Interactive Behavior Analytics (IBA) [2] and sequence analysis (SA), in order to create a novel, mixed methods, human-in-the-loop data analysis methodology that uses behavioral labels and visualizations to allow analysts to examine player behavior in a way that is context sensitive, scalable, and generalizable. We present the methodology along with a case study demonstrating how it can be used to analyze behavioral patterns of teamwork in the popular multiplayer game Defense of the Ancients 2 (DotA 2). CCS CONCEPTS• Human-centered computing → Visualization.

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Section: Mixed Methods Approaches To Gameplay Analysismentioning

confidence: 99%

Section: Rnn Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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"And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis

Kleinman¹,

Ahmad²,

Teng³

et al. 2020

Preprint

Self Cite

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show abstract

“…Other work has taken a mixed-methods approach to analysis and modeling, combining data visualization with human insight to create human-in-the-loop systems that can help people understand gameplay [1,18,25]. These mixed-methods techniques are able to overcome many of the shortcomings of pure quantitative techniques, specifically, they better account for context and allow domain experts to glean insight from game data in an interactive and transparent manner [31].…”

Section: An Abundance Of Datamentioning

confidence: 99%

Using Data to "Git Gud": A Push for a Player-Centric approach tothe Use of Data in Esports

Kleinman¹,

El-Nasr²

2021

Preprint

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The rapid increase in the availability of player data and the advancement of player modeling technologies have resulted in an abundance of data-driven systems for the domain of esports, both within academia and the industry. However, there is a notable lack of research exploring how players use their data to gain expertise in the context of esports. In this position paper we discuss the current state of the field and argue that there is a need for further research into how players use their data and what they want from data-driven systems. We argue that such knowledge would be invaluable to better design data-driven systems that can aid players in gaining expertise and mastering gameplay.

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“…In addition to the most popular categories, there are reviewed papers focused on to develop visualization methods to improve the comprehension about game patterns. They could be used by players [38] [39], coaches [40] [41] [42] [43] and spectators [44].…”

Section: Other Techniquesmentioning

confidence: 99%

Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

López-Guzmán¹,

Bustacara-Medina²

2021

CSRN

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Popularity of Multiplayer Online Battle Arena (MOBA) video games has grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables (predictors) to train the models. For this reason, this paper proposes a list of variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames. In order to get the mentioned list, the consulted works were grouped by the used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed.

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Modeling Individual and Team Behavior through Spatio-temporal Analysis

Cited by 37 publications

References 33 publications

"And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis

"And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis

Using Data to "Git Gud": A Push for a Player-Centric approach tothe Use of Data in Esports

Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

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