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Updated: Jun 4, 2025

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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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一个多维数据库的游戏中的球员运动 (行动和事件) 在盖尔式足球
Valerio Antonini1, Dermot Sheridan1, Mark Roantree1,2
1School of Computing, Dublin City University, Dublin, Ireland.
Data in brief
|December 17, 2024
概括
这项研究通过将原始GPS数据转化为球员的行为来设计了一个新的体育数据集. 这种丰富,多维的数据集使运动科学家和教练能够进行先进的机器学习分析.
科学领域:
- 运动科学 运动科学 运动科学
- 数据工程数据工程
- 机器学习 机器学习
背景情况:
- 田野运动表现分析依赖GPS数据的描述性统计数据.
- 原始GPS数据 (时间,位置) 不适合高级分析和机器学习.
- 需要处理,可分析的体育表现数据.
研究的目的:
- 从原始GPS坐标设计一个新的数据集.
- 将细粒度的GPS数据转化为有意义的玩家行动.
- 为了促进机器学习应用在体育表现分析.
主要方法:
- 一个数据工程过程被应用到原始GPS数据.
- 领域专家指导了坐标转化为玩家的行动.
- 从11场比赛和14名外野球员中创建了一个12个变量和约16万个动作的数据库.
主要成果:
- 一个经过处理的,玩家行动的维度数据集被生成.
- 数据集包含有关游戏,玩家,动作类型和持续时间的丰富信息.
- 数据集结构为各种分析查询.
结论:
- 工程数据集弥合了GPS原始数据和机器学习之间的差距.
- 这个资源支持体育科学家和教练在绩效分析.
- 维度性质允许对球员表现进行多方面的洞察.
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