基于深度学习算法的足球教学能力评估

IF 0.7 4区 工程技术 Q4 ENGINEERING, MARINE International Journal of Maritime Engineering Pub Date : 2024-07-27 DOI:10.5750/ijme.v1i1.1401
FL Yu
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引用次数: 0

摘要

足球教学包括传授与足球运动相关的基本技能、战术和策略。教练和指导员重点教授球员正确的运球、传球、射门和防守技术,同时还强调团队合作、体育精神和比赛意识。足球教学课程通常包括针对球员年龄和技术水平的练习、比拼和战术讨论。本文利用自动概率深度学习(Automated Probabilistic Deep Learning,APDL)模型提出了一种有效的足球教学能力评估技术。所提出的 APDL 模型包括一个用于评估学生表现的自动化模型。所提出的 APDL 模型会对输入的足球图像进行特征预处理。APDL 模型使用概率计算特征来计算足球数据中的变量。提取特征后,利用深度学习模型特征计算最大似然进行分类。APDL 模型实现了基于分类的深度学习模型特征,并对足球教学、指导、技能发展和球员进行了研究。模拟结果表明,APDL 模型的预测概率为 0.91,估计不确定值为 0.08。在教学和教练评估方面,两者的不确定值均为 0.08,预测评估得分均为 0.92。建议的 APDL 模型的分类准确度为 0.95,精确度为 0.97。这项研究的结果有助于足球教练方法的进步,为教练和教育工作者提供了有关其教学效果和有待改进之处的宝贵见解。此外,APDL 模型的自动化特性为评估教练绩效提供了可扩展性和高效性,为加强足球教练实践和球员发展铺平了道路。
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Assessment of Soccer Teaching Ability Based on Deep Learning Algorithm
Soccer teaching involves imparting fundamental skills, tactics, and strategies related to the sport of soccer. Coaches and instructors focus on teaching players proper techniques for dribbling, passing, shooting, and defending, while also emphasizing teamwork, sportsmanship, and game awareness. Soccer teaching sessions typically include a combination of drills, scrimmages, and tactical discussions tailored to the age and skill level of the players. This paper proposed an effective soccer assessment technique for teaching ability with the Automated Probabilistic Deep Learning (APDL) model. The proposed APDL model comprises an automated model for the assessment of student performance. The proposed APDL model processes the input soccer images with the pre-processing of the features. With APDL model uses the probabilistic computation features for the computation of the variables in the soccer data. With the extraction of the features, the maximum likelihood is computed for the classification with the deep learning model features. The APDL model implements the classification-based deep learning model features with the examination of soccer teaching, instruction, development of skill, and players. Simulation results demonstrated that prediction with the APDL model estimates the probability of prediction as 0.91 with an estimated uncertainty value of 0.08. In the case of teaching and coaching assessment, the uncertainty is stated as 0.08 for both with the prediction assessment score of 0.92. The classification accuracy of the proposed APDL model is achieved as 0.95 with the precision value of 0.97. The findings of this research contribute to the advancement of coaching methodologies in soccer, providing coaches and educators with valuable insights into their teaching effectiveness and areas for improvement. Additionally, the automated nature of the APDL model offers scalability and efficiency in assessing coaching performance, paving the way for enhanced coaching practices and player development in soccer.
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来源期刊
CiteScore
1.20
自引率
0.00%
发文量
18
审稿时长
>12 weeks
期刊介绍: The International Journal of Maritime Engineering (IJME) provides a forum for the reporting and discussion on technical and scientific issues associated with the design and construction of commercial marine vessels . Contributions in the form of papers and notes, together with discussion on published papers are welcomed.
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