将排球表现评估置于背景:引入背景个人贡献系数来评估技术行动
Carlos López-Serrano1, María Zakynthinaki2, Daniel Mon-López1
1Department of Sports, Faculty of Sciences of Physical Activity and Sport (INEF), Technical University of Madrid, Madrid, Spain.
Perceptual and motor skills
|November 6, 2023
概括
这项研究引入了排球表现分析的新指标,使用上下文系数准确评估球员贡献,并预测比赛结果,提高教练和球员的可靠性.
科学领域:
- 运动科学 运动科学 运动科学
- 绩效分析 绩效分析
- 排球分析 排球分析
背景情况:
- 传统的排球表现指标可能无法完全捕捉到球员在特定游戏场景中的影响.
- 评估玩家的技术技能需要背景,以避免高估,特别是在不均的游戏场景.
研究的目的:
- 提出新的指标和相对上下文系数来评估排球技术表现.
- 评估与行动背景相对的参与者参与,并提高绩效评估的准确性.
- 根据技术和上下文变量开发一个模型来预测球队的胜利.
主要方法:
- 使用数据排球软件和Python分析了2019年FIVB女子俱乐部世界锦标赛的20场比赛.
- 对拟议指标的观察者间和观察者内部可靠性的评估.
- 应用双项逻辑回归,接收器运行特征 (ROC) 曲线和模型合适标准 (Akaike,贝叶斯).
主要成果:
- 提出的上下文评估系数有效地防止在不均的情况下对球员表现的高估.
- 开发的模型准确地预测了团队的胜利,解决了胜利团队得分较少的悖论.
- 语境系数提供了对游戏技术和语境方面的可访问的见解.
结论:
- 新的指标和上下文系数增强了排球技术表现的评估.
- 该方法为教练和球员提供了显著的好处,因为它提供了对游戏动态的更细致的理解.
- 准确的绩效评估和游戏结果预测可以通过上下文感知分析来实现.
相关概念视频
Factors Affecting Activity Coefficient
807
The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size.
The activity coefficient value for an ion is close to one when the solution has almost zero ionic strength, i.e., when the solution shows close to ideal behavior. As the ionic strength of the solution increases from 0 to 0.1 mol/L, a...
The activity coefficient value for an ion is close to one when the solution has almost zero ionic strength, i.e., when the solution shows close to ideal behavior. As the ionic strength of the solution increases from 0 to 0.1 mol/L, a...
807
Social Facilitation
32.0K
Not all intergroup interactions lead to negative outcomes. Sometimes, being in a group situation can improve performance. Social facilitation occurs when an individual performs better when an audience is watching than when the individual performs the behavior alone. This typically occurs when people are performing a task for which they are skilled.
32.0K
Confidence Coefficient
7.6K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.6K
Self-Evaluation: Self-Enhancement and Self-Verification
5.2K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.2K
Coefficient of Variation
3.9K
The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
The coefficient of variation is a practical statistical tool in finance. It allows investors to assess the volatility or...
The coefficient of variation is a practical statistical tool in finance. It allows investors to assess the volatility or...
3.9K
Calculating and Interpreting the Linear Correlation Coefficient
6.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.0K


