Predicting skill from gameplay input to a first-person shooter

Buckley, David L.; Chen, Ke; Knowles, Joshua

doi:10.1109/cig.2013.6633655

Cited by 18 publications

(11 citation statements)

References 18 publications

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“…An accurate skill rating enables the system to generate balanced matches [3,4]. It allows for the assignment of teams with similar skill levels [3,[5][6][7][8]. It serves as feedback for the players so that they can track their performance over time [4,8,9].…”

Section: Related Workmentioning

confidence: 99%

The Evaluation of Rating Systems in Online Free-for-All Games

Dehpanah

Ghori

Gemmell

et al. 2021

Advances in Data Science and Information Engineering

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Online competitive games have become increasingly popular. To ensure an exciting and competitive environment, these games routinely attempt to match players with similar skill levels. Matching players is often accomplished through a rating system. There has been an increasing amount of research on developing such rating systems. However, less attention has been given to the evaluation metrics of these systems. In this paper, we present an exhaustive analysis of six metrics for evaluating rating systems in online competitive games. We compare traditional metrics such as accuracy. We then introduce other metrics adapted from the field of information retrieval. We evaluate these metrics against several well-known rating systems on a large real-world dataset of over 100,000 free-for-all matches. Our results show stark differences in their utility. Some metrics do not consider deviations between two ranks. Others are inordinately impacted by new players. Many do not capture the importance of distinguishing between errors in higher ranks and lower ranks. Among all metrics studied, we recommend Normalized Discounted Cumulative Gain (NDCG) because not only does it resolve the issues faced by other metrics, but it also offers flexibility to adjust the evaluations based on the goals of the system.

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Section: Related Workmentioning

confidence: 99%

The Evaluation of Rating Systems in Online Free-for-All Games

Dehpanah

Ghori

Gemmell

et al. 2021

Advances in Data Science and Information Engineering

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“…Mouse and keyboard data is a natural source of information about the players. Its relation with player performance in first-person shooters is covered in [8] for Red Eclipse and [37] for Counter-Strike:…”

Section: Activity Recognition Using Sensorsmentioning

confidence: 99%

“…• Mouse movements captured by a custom python script as a measure of the intensity of a player input. This data is an indirect indicator of the hand movement activity as well as the player skill [8].…”

Section: Sensorsmentioning

confidence: 99%

AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

Smerdov¹,

Burnaev²,

Somov³

et al. 2020

Preprint

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The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro 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 game chair data from Pro 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 that attention mechanism improves the generalization of the network and provides the straightforward feature importance as well. The best model achieves ROC AUC score 0.73. The prediction of the performance of particular player is realized although his data are not utilized in the training set. The proposed solution has a number of promising applications for Pro eSports teams as well as a learning tool for amateur players. CCS Concepts: • Computing methodologies → Artificial intelligence; Machine learning; • Computer systems organization → Embedded and cyber-physical systems; • Human-centered computing → Ubiquitous and mobile computing.

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“…However, the gameplay input recorded using a computer mouse and the buttons of keyboard remains the main source of collecting data on and modelling the players behavior and their ability prediction 8 . The appearance of wearables and 'earables' 9 makes them an excellent candidate (2) CS:GO Demos Manager, https://csgo-demos-manager.com (3) Monolith team profile, https://www.hltv.org/team/9182/monolith for upgrading the behaviour models.…”

Section: Almeida Et Al Carried Out a Studymentioning

confidence: 99%

Esports Athletes and Players: A Comparative Study

Khromov

Korotin

Lange

et al. 2019

IEEE Pervasive Comput.

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We present a comparative study of the players' and professional players' (athletes') performance in Counter Strike: Global Offensive (CS:GO) discipline.Our study is based on ubiquitous sensing helping identify the biometric features significantly contributing to the classification of particular skills of the players. The research provides better understanding why the athletes demonstrate superior performance as compared to other players.eSports is an organized and competitive gaming with a specific goal at the end of a game where single players or teams compete against each other. In spite of popularity and official recognition of esports, the debates as to the assessment of esports as an actual sport and comparing it to a sport are still going on 3 . The global esports audience numbered 380.2 million in 2018 and has tended to grow up to 557 million by 2021 (1) . Apart from the rapid increase in the quantity of professional athletes and teams, the number of players has dramatically gone up: 27 million people play League of Legends every day 1 . In this research we define an athlete as a professional player with a work contract with a professional eSports team (we use athlete and professional player interchangeably throughout the article). A player is a person without the eSports contract while having relevant game skills or status.In spite of popularity and official recognition of esports, the debates as to the assessment of esports as an actual sport are still going on. Although professional esports players spend 8-12 hours(1) Newzoo. 2018 Global Esports Market Report. https://asociacionempresarialesports.es/wpcontent/uploads/newzoo_2018_global_esports_market_report_excerpt.pdf

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Predicting skill from gameplay input to a first-person shooter

Cited by 18 publications

References 18 publications

The Evaluation of Rating Systems in Online Free-for-All Games

The Evaluation of Rating Systems in Online Free-for-All Games

AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

Esports Athletes and Players: A Comparative Study

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