A Random Forest approach to identify metrics that best predict match outcome and player ranking in the esport Rocket League

Impact of focus of attention on aiming performance in the first-person shooter videogame Aim Lab

Research examining the impact of Focus of Attention (FoA) has consistently demonstrated a benefit of adopting an external FoA over an internal FoA across a variety of sports and other domains. However, FoA research has yet to be applied within the rapidly growing world of competitive gaming. This study investigated whether an external FoA provided benefits over an internal FoA for aiming performance in First-Person Shooter (FPS) videogames, using the aim-training game Aim Lab. The study explored whether the level of participants' previous experience of FPS games impacted any effect, as few studies have investigated this directly. Participants with high (N = 20) and low (N = 17) FPS experience who had a minimum of 200 hours FPS experience were selected for the study. The participants were instructed before each set of ten trials to either attend to their wrist/arm movements (internal FoA) or to the target (external FoA). There was no significant main effect of FoA on performance and no significant interaction between FoA and experience. In contrast to findings in other studies, an external FoA provided no performance benefits over an internal FoA in the FPS game Aim Lab. We discuss methodological issues related to the measures used and suggest avenues for future research with a view to improving understanding of putative underlying mechanisms for FoA effects.

Co-Transformation of Digital Health and eSport in Metaverse: Moderating Effects of Digital Personality on Mental Health in Multiplayer Online Battle Arena (MOBA)

Mental health issues (e.g., social exclusion, depression, anxiety, and burnout) became highly prevalent in the global eSport industry. Likewise, the eSport trend in China also dramatically increased, while the attitudes and behaviours of the players also impacted their intentions to utilize video gaming. As China became the epicentre of the online video gaming industry, especially MOBA, it primarily influenced young athletes to adopt video gaming strategies for training purposes. Still, preventive measures are needed for video gaming addictions by athletes to improve their overall eSport performance. To conduct this study, self-administered questionnaires were distributed to 400 athletes aged 18-27 years; the response rate was adequate after screening, out of which 345 were finalized for the data analysis. The results indicate that metaverse-based digital healthcare significantly impacts eSport performance. Moreover, mental health significantly mediated the relationship between metaverse-based digital health and eSport performance. In addition, the digital personality also significantly moderated the relationship between metaverse-based digital healthcare and eSport performance. This research holds tremendous significance both from theoretical and practical perspectives. The study adds valuable insights to the growing body of literature regarding eSport gaming and mental health. The beneficial and constructive intuitions regarding eSport from a psychological perspective can be gained from this study, along with its pros and cons on the mental health of young Chinese athletes.

AI-enabled prediction of video game player performance using the data from heterogeneous sensors

The emerging progress of video gaming and eSports lacks the tools for ensuring high-quality analytics and training in professional and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the smart chair data from professional and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated an attention mechanism improves the generalization of the network and provides a straightforward feature importance as well. The best model achieves Area Under the Receiver Operating Characteristic Curve (ROC AUC) score 0.73 in predicting whether a player will perform better or worse in the next 240 seconds based on in-game metrics. The prediction of the performance of a particular player is realized although their data are not utilized in the training set. The proposed solution has a number of promising applications for professional eSports teams and amateur players, such as a learning tool or performance monitoring system.

Android Spyware Detection Using Machine Learning: A Novel Dataset

Smartphones are an essential part of all aspects of our lives. Socially, politically, and commercially, there is almost complete reliance on smartphones as a communication tool, a source of information, and for entertainment. Rapid developments in the world of information and cyber security have necessitated close attention to the privacy and protection of smartphone data. Spyware detection systems have recently been developed as a promising and encouraging solution for smartphone users’ privacy protection. The Android operating system is the most widely used worldwide, making it a significant target for many parties interested in targeting smartphone users’ privacy. This paper introduces a novel dataset collected in a realistic environment, obtained through a novel data collection methodology based on a unified activity list. The data are divided into three main classes: the first class represents normal smartphone traffic; the second class represents traffic data for the spyware installation process; finally, the third class represents spyware operation traffic data. The random forest classification algorithm was adopted to validate this dataset and the proposed model. Two methodologies were adopted for data classification: binary-class and multi-class classification. Good results were achieved in terms of accuracy. The overall average accuracy was 79% for the binary-class classification, and 77% for the multi-class classification. In the multi-class approach, the detection accuracy for spyware systems (UMobix, TheWiSPY, MobileSPY, FlexiSPY, and mSPY) was 90%, 83.7%, 69.3%, 69.2%, and 73.4%, respectively; in binary-class classification, the detection accuracy for spyware systems (UMobix, TheWiSPY, MobileSPY, FlexiSPY, and mSPY) was 93.9%, 85.63%, 71%, 72.3%, and 75.96%; respectively.

Using Collective Metrics to Assess Team Dynamics and Performance in eSports

A challenge posed by virtual teams is monitoring team interactions remotely. Research with field-based soccer teams provides evidence that measures of collective behavior can be used to assess the dynamics of sports teams. Collective behaviors calculated using the spatial characteristics of teammates as they moved across the field have been found to vary by the state of the soccer match, including ball possession and proximity to a goal. The present study examined whether similar effects were observed with collective metrics calculated from players of a car-soccer eSport video game. A set of matches were retrieved and used to calculate collective behavior metrics based on the placement of teammates within a virtual arena. A subset of metrics varied by team location and ball possession, aligning with and extending previous field-based soccer research, and correlated with team performance. This suggests that collective behaviors can be used to assess aspects of team dynamics within virtual environments.