测试板球运动员在"紧张的九十年代"快速得分:来自回归不连续性设计的证据
Leo Roberts1, Daniel R Little2, Mervyn Jackson3
1Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
PloS one
|June 28, 2023
概括
这项研究发现,板球击球员的表现指标,如每球的运行和边界得分,随着他们接近100个运行,增加. 击球者通过快速得分来成功地应对"紧张的九十年代"的心理压力,以达到里程碑.
科学领域:
- 运动科学 运动科学 运动科学
- 绩效分析 绩效分析
- 体育心理学在体育中的心理学
背景情况:
- "紧张的九十年代"是板球中被广泛接受的概念,暗示着击球员接近100场的心理挑战.
- 缺乏实证研究,检查在测试板球中100场比赛周围的击球行为变化.
研究的目的:
- 为了调查球员在测试板球中接近并超过100场比赛时,击球表现和行为如何发生变化.
- 用历史数据统计分析"神经九十年代"现象.
主要方法:
- 利用了来自712场 (2004-2022) 测试比赛的开源球对球数据.
- 采用多级回归建模来分析在100场比赛门周围的击球绩效指标 (每球跑,边界概率,丢失概率).
- 调整了数据集群的模型 (球在球员内,比赛在球员内,局在球员内).
主要成果:
- 每球的跑分和边界得分的概率显著增加,因为击球者接近100个跑.
- 100场比赛后,每球有0.18场比赛的显着下降,边界概率下降了3%.
- 在达到100个运行之前和之后,没有观察到解雇概率的显著变化.
结论:
- 测试板球运动员表现出有效的应对机制,以应对"紧张的九十年代"的心理压力.
- 击球员倾向于采取更具侵略性或机会主义的策略,以迅速实现100场比赛的里程碑.
- 数据表明,通过战略性击球调整,可以有效地管理大约100场比赛的心理需求.
更多相关视频
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
10.7K
05:58Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
Published on: August 29, 2018
8.9K
相关概念视频
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Wilcoxon Signed-Ranks Test for Matched Pairs
170
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
170
Residual Plots
4.7K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
When the residual values are plotted against the variable x, it is called a residual...
4.7K
Outliers and Influential Points
4.1K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.1K
McNemar's Test
317
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
317
Sign Test for Matched Pairs
169
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
To conduct the sign test, we first calculate the differences in...
169
