KovaaK's aim trainer as a reliable metrics platform for assessing shooting proficiency in esports players: a pilot study

Author:

Rogers Ethan J.,Trotter Michael G.,Johnson Daniel,Desbrow Ben,King Neil

Abstract

Esports research lacks game-based metrics platforms appropriate for adequately capturing esports performance. The aim of this pilot study was to assess the reliability of the KovaaK's first-person shooter (FPS) aim trainer as a metrics platform for assessing shooting proficiency in esports players. Ten FPS esports players completed two identical experimental trials (T) separated by three to five days. Each trial included four rounds (R) of testing, evaluating four shooting tasks: Micro Flicking, Macro Flicking, Strafe Tracking, and Wall Peeking. Reliability of performance outcomes (e.g., accuracy, headshot accuracy, hits per second, and total shots hit) were assessed using the intraclass correlation coefficient (ICC) and their 95% confidence intervals (CI), and significant differences were identified using repeated-measures analysis of variance (RM-ANOVA). Results indicated excellent, or good to excellent reliability for all outcome variables with the ICC estimates ranging between 0.947–0.995, with lower and upper bound 95% CIs ranging between 0.876–0.988, and 0.984–0.999, respectively. Significant improvements were seen between experimental trials in the Macro Flicking task for accuracy (p = .005) and hits per second (p = .009) only. Significant interactions between trial and round were identified in the Micro Flicking task for accuracy (p = .006), with post hoc analysis showing accuracy was significantly higher in T1R1 compared to T2R1 (87.74 ± 3.13 vs. 85.99 ± 3.05, respectively, p = .02), and in T2R4 compared to T2R2 (87.99 ± 2.89 vs. 84.70 ± 4.25, respectively, p = .049). Significant interactions were also identified in the Strafe Tracking task for headshot accuracy (p = .002), with post hoc analysis showing headshot accuracy was significantly higher in T1R2 compared to T2R2 (78.48 ± 8.15 vs. 76.79 ± 12.16, respectively, p = .003), and in T1R2 compared to T1R1 (78.48 ± 8.15 vs. 73.68 ± 17.94, respectively, p = .023). In summary, this study demonstrates that KovaaK's provides a reliable metrics platform for assessing shooting proficiency in esports, however, some variability in performance was observed.

Publisher

Frontiers Media SA

Reference19 articles.

1. The one billion dollar myth: methods for sizing the massively undervalued esports revenue landscape;Ahn;Int J Esports,2020

2. Definitions of esports: a systematic review and thematic analysis;Formosa;Proc ACM Hum Comput Interact,2022

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3