An Exploratory Study of Player and Team Performance in Multiplayer First-Person-Shooter Games

Shim, Kyong Jin; Hsu, Kuo-Wei; Damania, Samarth; DeLong, Colin; Srivastava, Jaideep

doi:10.1109/passat/socialcom.2011.155

Cited by 15 publications

(9 citation statements)

References 7 publications

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“…For assessing task performance, two measures were applied: first, the relation between player kills and player deaths and, second, player shooting accuracy. In this type of first-person arena shooters, the relation between kills and deaths is a common metric of performance and is often used to determine how individuals and teams perform during professional and amateur FPS gaming matches ( Shim et al, 2011 ). For this analysis, the same kill-to-death relation metric was used as is preprogrammed into the Shooter Game.…”

Section: Methodsmentioning

confidence: 99%

A Novel Brain–Computer Interface Virtual Environment for Neurofeedback During Functional MRI

et al. 2021

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Virtual environments (VEs), in the recent years, have become more prevalent in neuroscience. These VEs can offer great flexibility, replicability, and control over the presented stimuli in an immersive setting. With recent developments, it has become feasible to achieve higher-quality visuals and VEs at a reasonable investment. Our aim in this project was to develop and implement a novel real-time functional magnetic resonance imaging (rt-fMRI)–based neurofeedback (NF) training paradigm, taking into account new technological advances that allow us to integrate complex stimuli into a visually updated and engaging VE. We built upon and developed a first-person shooter in which the dynamic change of the VE was the feedback variable in the brain–computer interface (BCI). We designed a study to assess the feasibility of the BCI in creating an immersive VE for NF training. In a randomized single-blinded fMRI-based NF-training session, 24 participants were randomly allocated into one of two groups: active and reduced contingency NF. All participants completed three runs of the shooter-game VE lasting 10 min each. Brain activity in a supplementary motor area region of interest regulated the possible movement speed of the player’s avatar and thus increased the reward probability. The gaming performance revealed that the participants were able to actively engage in game tasks and improve across sessions. All 24 participants reported being able to successfully employ NF strategies during the training while performing in-game tasks with significantly higher perceived NF control ratings in the NF group. Spectral analysis showed significant differential effects on brain activity between the groups. Connectivity analysis revealed significant differences, showing a lowered connectivity in the NF group compared to the reduced contingency-NF group. The self-assessment manikin ratings showed an increase in arousal in both groups but failed significance. Arousal has been linked to presence, or feelings of immersion, supporting the VE’s objective. Long paradigms, such as NF in MRI settings, can lead to mental fatigue; therefore, VEs can help overcome such limitations. The rewarding achievements from gaming targets can lead to implicit learning of self-regulation and may broaden the scope of NF applications.

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Section: Methodsmentioning

confidence: 99%

A Novel Brain–Computer Interface Virtual Environment for Neurofeedback During Functional MRI

et al. 2021

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“…Another possibility to predict the match outcome is to use in-game statistics like kills, deaths, gold, abilities learned, etc. This approach showed that, to some extent, match outcome could be predicted in the MOBA genre [17]- [20] and firstperson shooter (FPS) genre [21]. More complex models also try to interpret model results [22], [23], or calibrate confidence of a prediction [24].…”

Section: B In-game Data In Esports Researchmentioning

confidence: 99%

“…where ỹ(t) equals 1 if player performed well in the encounter at timestep t, and equals −1 otherwise. Encounter outcome suppose to estimate players performance better than classical metrics like Kill Death Ratio [51], since these metrics underestimate players in supportive roles and don't utilize the encounter-like nature of game events.…”

Section: Particle Sensormentioning

confidence: 99%

Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

Smerdov,

Somov,

Burnaev

et al. 2020

Preprint

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Current research in eSports lacks the tools for proper game practising and performance analytics. The majority of prior work relied only on in-game data for advising the players on how to perform better. However, in-game mechanics and trends are frequently changed by new patches limiting the lifespan of the models trained exclusively on the in-game logs. In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter. The sensor data were collected from 10 participants in 22 matches in League of Legends video game. We have trained machine learning models including Transformer and Gated Recurrent Unit to predict whether the player wins the encounter taking place after some fixed time in the future. For 10 seconds forecasting horizon Transformer neural network architecture achieves ROC AUC score 0.706. This model is further developed into the detector capable of predicting that a player will lose the encounter occurring in 10 seconds in 88.3% of cases with 73.5% accuracy. This might be used as a players' burnout or fatigue detector, advising players to retreat. We have also investigated which physiological features affect the chance to win or lose the next in-game encounter.

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“…The club data application mainly includes team win rate prediction, player technical analysis, opponent analysis, tactical formulation, etc. Shim et al (2011) performed a regression analysis on the team data after the "Halo 3" game, and the results showed that the team performance can be effectively predicted based on the player's ability and winning rate. In designing player techniques and tactics, Bauckhage et al (2014) Ontanón et al (2013) proposed semi-Markov models to predict the location information of opponents in "StarCraft", so as to formulate targeted combat plans for the team.…”

Section: Club Application Researchmentioning

confidence: 99%

Research on the Overall Architecture and Application of E-Sports Big Data

Liu¹,

Zhang²

2020

IJBM

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Big data has a profound impact on the transformation of human society and culture. With the rapid development of E-sports industry in the world, the application research of big data in the field of E-sports has gradually become one of the focuses of academic attention. Based on the method of literature analysis, this paper makes a systematic analysis of 628 articles about big data of E-sports published from 2013 to 2020, and attempts to systematically study the literature from the concepts, characteristics, system architecture and practical applications of E-sports big data, and propose possible future improvements in this field.

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An Exploratory Study of Player and Team Performance in Multiplayer First-Person-Shooter Games

Cited by 15 publications

References 7 publications

A Novel Brain–Computer Interface Virtual Environment for Neurofeedback During Functional MRI

A Novel Brain–Computer Interface Virtual Environment for Neurofeedback During Functional MRI

Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

Research on the Overall Architecture and Application of E-Sports Big Data

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