Understanding Human Skill Acquisition through Statistical Modelling of Big Data in eSports

专业知识和技能在社会中发挥着越来越重要的作用，经济越来越多地由知识驱动，涉及快速的技术变革和越来越长的工作寿命。游戏玩家在数小时、数天甚至数年的练习中积累了深厚的技能，生成了大量关于游戏玩法和技能发展的数据，这些数据可以轻松且不引人注目地记录下来。其结果是提供了一个无与伦比的机会来研究游戏的哪些方面决定了专业技能，以及如何获得专业技能。博士学位的主题是测试技能获取理论，并通过对电子竞技游戏中自然发生的数据的统计询问来表征游戏中的专家技能。这项研究将在约克大学的Anders Drachen教授和谢菲尔德大学的Tom Stafford博士的联合监督下进行，IGGI的研究主题是使用电子竞技数据来了解人类心理。这项提议的研究与电子竞技行业（如DC实验室）、现有专业知识（如Alex Wade教授）以及IGGI正在进行的通过分析主要在线游戏（如《英雄联盟》、《DOTA 2》、《命运》）的玩家数据对表现和技能获取的分析非常吻合。技能获取的认知科学具有广泛的适用性，对电子竞技行业具有实际意义。开发能够预测高水平表现的技能测量方法能够让专业团队更有效地识别人才，并让游戏开发者能够设计出更有效的匹配系统。了解游戏的哪些方面加速了技能获取，也为设计更有效的教程提供了机会，设计反馈系统，通过建议改变游戏玩法行为来帮助新手到专业玩家最大化他们的学习速度，并优化训练行为以适应竞争。除了游戏，技能获取研究对于外科手术和军事航空等领域也非常重要，因为这些领域的技术进步和性能条件往往超过熟练人员的适应速度。了解如何优化学习可以显著影响这些领域，因为手术的成功可能取决于个人及时适应的能力。以前对专业知识和技能获取的研究已经产生了很多关于实践、反馈和转移的知识，但在很大程度上依赖于实验室发现和对实践行为的回顾性描述。通过对来自电子竞技的大型真实数据集应用统计建模和数据可视化技术，目前的研究将绕过实验室方法的缺点，在高水平的统计置信度下产生关于技能学习的原始发现。综上所述，拟议研究的影响可以为电子竞技中的教练实践和游戏开发提供信息，提高我们目前对支持技能习得的心理学的理解，并激发后续对学习领域的科学探索。关键词预测建模技能预测机器学习体育分析绩效分析

Expert knowledge and skill play an increasingly important role in society, with an economy which is increasingly knowledge-driven and which involves rapid technological change and longer and longer working lives. Game players develop profound skill over hours, days and even years of practice, generating huge reservoirs of data about gameplay and skill development that can be easily and unobtrusively recorded. The result is an unparalleled opportunity to investigate what aspects of play determine expert skill, and how it is acquired.The topic of the proposed PhD is to test theories of skill acquisition and characterise expert skill in gaming through the statistical interrogation of naturally occurring data in eSports games. The research would be conducted under the joint supervision of Professor Anders Drachen from the University of York and Dr Tom Stafford from the University of Sheffield under the IGGI research theme of using eSports data to understand human psychology. The proposed research fits well with available links to the eSports industry (i.e., DC Labs), available expertise (e.g., Professor Alex Wade), and ongoing analytics at IGGI into performance and skill acquisition through the analysis of player data from major online games (e.g., League of Legends, DOTA 2, Destiny)The cognitive science of skill acquisition has broad applicability and is of practical significance for the eSports industry. Developing measures of skill that are predictive of high level performance can allow professional teams to identify talent more effectively, and allow game developers to engineer more effective matchmaking systems. Understanding what aspects of play accelerate skill acquisition also presents an opportunity to design more effective tutorials, engineer feedback systems that help novice to professional players maximise their rate of learning by suggesting changes to gameplay behaviour, and optimise training behaviours for competition.Beyond gaming, skill acquisition research is of tremendous import for domains such as surgery and military aviation, where technical advancements and performance conditions often outpace the rate at which skilled personnel can adapt to them. Understanding how to optimise learning can significantly impact these arenas, as the success of an operation can depend upon an individual's ability to adapt in good time.Previous research into expertise and skill acquisition has generated much knowledge about practice, feedback, and transfer, but has largely relied on laboratory findings and retrospective accounts of practice behaviour. By applying statistical modelling and data visualisation techniques on large, real-world data sets from eSports, the current research would bypass the drawbacks of laboratory methods to generate original findings about skill learning at high levels of statistical confidence. Taken together, the impact of the proposed research could inform coaching practices and game development in eSports, improve our current understanding about the psychology underpinning skill acquisition, and motivate subsequent scientific inquiry into the domain of learning.Keywordsprediction modelingskill predictionmachine learningsports analyticsperformance analysis