"所有闪闪发光的不是黄金 (反之亦然)?" - 百分比比较方法 (PCM) 如何改善青年绩效评估
Joshua Wooldridge1, Shaun Abbott1, Clorinda Hogan1
1Discipline of Exercise & Sport Science, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia.
Journal of sports sciences
|April 18, 2025
概括
百分比比较方法 (PCM) 通过考虑发育差异,为评价青少年游泳者提供了更公平的方法. 这种方法显著调整了绩效排名,为个人潜力提供了更好的洞察力.
科学领域:
- 运动科学 运动科学 运动科学
- 人类运动 人类运动
- 儿科的绩效分析.
背景情况:
- 对青少年体育表现的公平评估受到个体间发展差异的挑战.
- 百分比比较方法 (PCM) 旨在解决这些差异,提供相对年龄和成熟度状态的特定绩效等级.
研究的目的:
- 为了提高PCM估计的准确性.
- 评估50米和200米前 (FC) 赛事中男性游泳者的PCM配置分布的一致性.
主要方法:
- 利用了930名 (50米FC) 和733名 (200米FC) 11-16岁的男子游泳者的数据.
- 收集绩效,背景和人体测量数据以确定成熟度状态.
- 采用二次回归趋势线来建模年龄和成熟度状态对绩效的影响,并结合信心区间和错误估计来识别PCM概况.
主要成果:
- 与规范年龄组排名相比,当考虑相对年龄和成熟差异时,观察到显著的绩效排名变化 (94%).
- 通过个人-队列PCM档案确定了五种不同的排名变化模式,显示了50米和200米FC赛事中类似的流行率.
结论:
- PCM提供了一个更准确的背景来评估青少年游泳者的表现.
- PCM可以帮助识别人才,选择流程,并减轻与相对年龄和成熟度相关的偏见.
- 这种方法为年轻运动员的短期发展轨迹提供了有价值的见解.
更多相关视频
相关概念视频
Percentile
6.4K
A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
6.4K
Review and Preview
6.8K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
Percentiles are a type of fractile that partition data into...
6.8K
Friedman Two-way Analysis of Variance by Ranks
95
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
95
Quartile
4.0K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
4.0K
Reliability and Validity
12.6K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.6K
Detection of Gross Error: The Q Test
4.4K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
4.4K


