Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Expected Value01:15

Expected Value

3.8K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
3.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

14
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
14
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

47
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
47
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

65
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
65
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.1K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Different formations, different patterns: An integrated approach combining entropy, machine learning, and XAI to analyze passing networks in soccer.

Journal of sports sciences·2026
Same author

Personality profiles of youth elite soccer players: sport-specific traits, playing position differences, and coaches' subjective evaluations.

Science & medicine in football·2026
Same author

Prevalence of stress urinary incontinence and associated factors in elite female football players: Better understanding for better prevention.

Journal of science and medicine in sport·2026
Same author

A Semi-Markov framework for modeling football possessions and temporal expected threat.

Scientific reports·2026
Same author

Time to Blow the Whistle on Mental Fatigue in Soccer Referees: A Current Opinion on the Impact of Cognitive and Physical Demands on Performance and Training Interventions.

Sports medicine (Auckland, N.Z.)·2026
Same author

Effect of a Visual Dual-Task on Single-Leg Countermovement-Jump in Male Professional Soccer Players with Lower-Limb Injuries: A Cross-Sectional Observational Study.

Sports (Basel, Switzerland)·2025

相关实验视频

Updated: May 12, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.0K

用贝叶斯混合模型对可解释的预期目标进行建模.

Loïc Iapteff1, Sebastian Le Coz1, Maxime Rioland1

  • 1Seenovate, Montpellier, France.

Frontiers in sports and active living
|May 8, 2025
PubMed
概括

本研究介绍了一种简单,可解释的贝叶斯模型,用于预测足球比赛中的预期进球 (xG). 该模型实现了与复杂方法相美的性能,为体育分析和投注提供了宝贵的见解.

关键词:
贝叶斯的推理 贝叶斯的推理预期的目标 预期的目标一般化的线性混合模型.足球 足球 足球转移学习转移学习

更多相关视频

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.2K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

9.8K

相关实验视频

Last Updated: May 12, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.0K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.2K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

9.8K

科学领域:

  • 运动分析 运动分析
  • 统计建模 统计建模
  • 运动中的机器学习

背景情况:

  • 体育团队和博彩公司寻求了解球员/球队的活动和比赛结果.
  • 预期目标 (xG) 模型提供了对绩效的洞察力,但往往缺乏可解释性.
  • 复杂的统计和机器学习模型目前用于结果预测.

研究的目的:

  • 开发一种简单可解释的预期目标 (xG) 建模方法.
  • 将拟议模型的性能与现有方法进行比较.
  • 用有限的数据利用转移学习来分析团队的优缺点.

主要方法:

  • 贝叶斯概括的线性混合效应模型为xG.
  • 利用了七个关键变量:射击类型,位置和对手的距离.
  • 员工将学习转移给预先训练的模型.

主要成果:

  • 贝叶斯式xG模型的性能与StatsBomb模型的性能相当 (AUC=0.781对比0.801).
  • 该模型有效地使用一组有限的变量进行预测.
  • 预先训练的模型可以通过小数据集来轻松识别团队的优点/缺点.

结论:

  • 对于xG,一个简单的,可解释的贝叶斯模型是可行的和有效的.
  • 这种方法增强了高级分析在体育中的实际应用.
  • 转移学习为分析团队绩效动态提供了一个强大的工具.