推进100米冲刺的性能预测:一种机器学习方法来模拟速度曲线和性能相关性
Chung Kit Tam1, Zai-Fu Yao1,2,3,4
1Department of Kinesiology, National Tsing Hua University, Hsinchu City, Taiwan.
PloS one
|May 13, 2024
概括
机器学习模型提供了一种更容易和更准确的方法来预测100米冲刺速度-时间曲线,优于传统方法. 更高的最大冲刺速度与更快的整体冲刺时间有很强的相关性.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 数据科学数据科学数据科学
背景情况:
- 传统的100米冲刺速度-时间模型通常需要复杂的数据收集.
- 在冲刺绩效分析中,需要更容易获得和更准确的建模方法.
研究的目的:
- 开发和评估机器学习模型,用于预测100米冲刺中的速度-时间曲线.
- 将机器学习模型的性能与传统速度模型进行比较.
- 为了研究关键速度参数和整体冲刺时间之间的关系.
主要方法:
- 利用了国际赛道赛事数据 (1987-2019).
- 采用随机森林 (RF) 和神经网络 (NN) 机器学习算法.
- 使用平均平方误差 (MSE) 对传统指数速度模型进行评估模型准确性.
主要成果:
- 与随机森林 (RF) 和传统模型相比,神经网络 (NN) 模型显示出更高的预测性能.
- 在最大冲刺速度和最后100米冲刺时间之间发现了强烈的负相关性.
- 分析了关键参数,例如最大速度的时间和最大速度阶段的持续时间.
结论:
- 机器学习为模拟冲刺动态提供了可行的和有效的替代方案.
- 优化最大速度对于改善整体100米冲刺表现至关重要.
- 这项研究为体育科学家在训练和绩效分析方面提供了宝贵的工具.
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