对田径运动员进行基于AI的绩效分析
概括
这项研究引入了一种人工智能驱动的系统,用于使用历史比赛数据对田径运动员进行排名. 通过这种数据驱动的工具,教练现在可以量化运动员的表现,并预测竞争准备.
科学领域:
- 运动科学 运动科学 运动科学
- 人工智能的人工智能
- 数据分析数据分析数据分析.
背景情况:
- 在田径运动中,传统的绩效评估依赖于手动分析.
- 缺乏客观的,数据驱动的工具阻碍了运动员的准确评估和进度跟踪.
- 预测运动员的稳定性和竞争潜力仍然是教练面临的挑战.
研究的目的:
- 开发和介绍一个人工智能驱动的方法论,用于性能分析和田径运动员的排名.
- 为教练提供一个工具来量化运动员在训练期间的进步.
- 为了使数据驱动的运动员竞争力对未来的事件的预测.
主要方法:
- 利用来自TFRRS数据库的历史比赛数据.
- 从运动员记录中策划和提取关键的统计特征.
- 训练了一种神经网络聚类 (ART2) 技术用于性能分析.
主要成果:
- 开发了一种方法来将运动员记录映射到性能集群.
- 人工智能模型提供了一种数据驱动的方法来排名运动员.
- 能够量化运动员的表现和进步.
结论:
- 拟议的人工智能方法提供了一个新的,数据驱动的解决方案,用于赛道和田野性能分析和排名.
- 教练可以利用这个工具客观地评估运动员,并预测他们的竞争表现.
- 这种方法提高了培训效率和竞赛的战略规划.
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