The reliability and validity of mobalytics proving ground as a perceptual-motor skill assessment for esports

Pluss, Matthew A.; Novak, Andrew R.; Bennett, Kyle J.M.; Panchuk, Derek; Coutts, Aaron J.; Fransen, Job

doi:10.1177/17479541221086793

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…For example, authors have assessed individuals on metrics from standalone accuracy (Neri et al, 2021), to game score (Liu et al, 2021), to perceived performance (Hopp & Fisher, 2017). Although noted by prior authors (Leis et al, 2021;Pedraza-Ramirez et al, 2020;Pluss et al, 2022), the most empirically justified means to indicate performance is limited.…”

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confidence: 99%

Indexing Esport Performance

Sharpe

Besombes

Welsh

et al. 2023

Journal of Electronic Gaming and Esports

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The assessment of an athlete’s performance can play a key role in determining their current state, their readiness to compete, the impact of an experimental manipulation, and/or the influence of an intervention. At present, there is limited empirical evidence stating the indicators that encapsulate individual performance within any esport. To identify the variables that are historically associated with indicating Counter-Strike: Global Offensive performance, a literature review was conducted. Identified variables were accumulated and presented to three technical expert panels composed of world-class esport athletes, researchers, and practitioners. We utilized a modified Delphi method to provide direction concerning the examination of performance in esports. The expert panelists presented numerous opinions on what encapsulates performance, considerations for best practices, and concerns associated with the semantics of performance. This study presents the opinions of various domain-specific experts and encourages the use of more explicit terminology when discussing performance measurement. It was the intention of the project to generate an open discussion rather than draw a unified conclusion on best practices.

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confidence: 99%

Indexing Esport Performance

Sharpe

Besombes

Welsh

et al. 2023

Journal of Electronic Gaming and Esports

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“…A significant difference in keyboard and mouse usage between CS: GO amateur players and professional players was reported in [28]. They particularly considered the time interval in which the player used the A and D keys to move to the right and left and the time interval in which the player pressed the left mouse button (MOUSE1) while moving forward using the key W. In one study, the Mobalytics Proving Ground Assessment (MPGA) could differentiate between highly skilled and low-skilled League of Legends (LoL) players [31]. MPGA is an online assessment tool that evaluates LoL players' ability to work with a mouse and keyboard in response to randomly appearing targets.…”

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confidence: 99%

An efficient machine learning approach for extracting eSports players’ distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross-validation

Noroozi,

Hasan,

Ravan

et al. 2024

Int J Data Sci Anal

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Discovering features that set elite players apart is of great significance for eSports coaches as it enables them to arrange a more effective training program focused on improving those features. Moreover, finding such features results in a better evaluation of eSports players' skills, which, besides coaches, is of interest for game developers to design games automatically adaptable to the players' expertise. Sensor data combined with machine learning have already proved effective in classifying eSports players. However, the existing methods do not provide sufficient information about features that distinguish high-skilled players. In this paper, we propose an efficient method to find these features and then use them to classify players' skill levels. We first apply a time window to extract the players' sensor data, including heart rate, hand activities, etc., before and after game events in the League of Legends game. We use the extracted segments and symbolic transfer entropy to calculate connectivity features between sensors. The most relevant features are then selected using the newly developed consensus nested cross validation method. These features, representing the harmony between body parts, are finally used to find the optimum window size and classify players' skills. The classification results demonstrate a significant improvement by achieving 90.1% accuracy. Also, connectivity features between players' gaze positions and keyboard, mouse, and hand activities were the most distinguishing features in classifying players' skills. The proposed method in this paper can be similarly applied to sportspeople's data and potentially revolutionize the training programs in both eSports and sports industries.

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Operationalizing General Mechanical Skill in Time-Pressure Action Esports

Kim,

Dunkel,

Lee

2024

Journal of Electronic Gaming and Esports

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In time-pressure action esports games, a player’s mechanical skill refers to the ability to quickly and accurately control input devices such as a keyboard or mouse. In this study, we present a novel methodology to quantify and operationalize mechanical skills commonly required in game titles played with the same set of input devices (i.e., general mechanical skill). From this, differences in mechanical skills between players can be compared on a standard basis, regardless of game title, which offers new possibilities for esports talent development. As a result of partial least squares path modeling based on data from 292 players of four different game titles played with keyboard and mouse, we show that general mechanical skill is a valid construct and that it accounts for approximately 21% of the variance in players’ in-game achievement (or rank). To maximize impact, our software is released to the public, enabling anyone to operationalize general mechanical skills in time-pressure action esports games played with keyboard and mouse.

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The reliability and validity of mobalytics proving ground as a perceptual-motor skill assessment for esports

Cited by 5 publications

References 28 publications

Indexing Esport Performance

Indexing Esport Performance

An efficient machine learning approach for extracting eSports players’ distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross-validation

Operationalizing General Mechanical Skill in Time-Pressure Action Esports

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