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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/foundations-sports-analytics
课程评论：没有评论
课程名称：《体育分析基础：体育中的数据、表现和模型》 课程概述： 本课程介绍了如何使用Python分析体育团队的表现。学习者将发现多种用于表示体育数据的技术，以及如何根据这些分析技术提取叙事。本课程的主要焦点是利用回归分析来分析团队和球员表现数据，所用例子来源于国家橄榄球联盟（NFL）、国家篮球协会（NBA）、国家冰球联盟（NHL）、英超联赛（EPL，足球）和印度超级联赛（IPL，板球）。课程不仅解释方法和技术，还鼓励学习者将它们应用于感兴趣的体育数据集，从而生成自己的结果，而不是依赖他人的数据处理。这使得学习者能够探索自己关于体育团队表现的想法，使用数据进行测试，成为体育分析的生产者，而不仅仅是消费者。 尽管课程材料采用Python开发，但也提供了R代码，以便喜欢该环境的学习者使用。 课程大纲： 1. **体育表现与数据简介**： 介绍简单的体育分析实例——用毕达哥拉斯期望值计算团队体育中的胜利模型，并用于预测。涵盖五个不同体育联盟的实例：美国职棒大联盟（MLB）、国家篮球协会（NBA）、国家冰球联盟（NHL）、英超联赛（EPL，足球）和印度超级联赛（IPL，板球）。 2. **数据源简介**： 使用NBA数据介绍基本的Python代码以进行数据清理和准备。同时讨论统计数据和图形的总结性和描述性分析，以理解数据分布、变量特点及其关系，最后介绍相关系数以总结两个变量之间的线性关系。 3. **Python中的体育数据和图形**： 介绍多种数据表示方式，使用来自MLB、NBA和IPL的例子。分析MLB数据以探讨不同击球的空间分布，利用NBA数据生成热力图，展示球员贡献的不同方式，使用IPL数据展示如何图形化比较团队表现。 4. **Python中的体育数据与回归分析**： 介绍回归分析的基本概念，讨论如何使用Python进行回归分析及解读回归输出。使用NHL数据估算多个回归模型，识别影响球队胜率的因素，同时使用IPL的板球数据进行回归分析，考察球员表现对薪资的影响。 5. **深入回归分析**： 利用回归分析调查NBA、NHL、EPL和IPL中团队薪资支出与团队表现之间的关系，探讨不同的回归模型定义方式，以及如何解读竞争回归模型的结果。 6. **篮球中的热手现象是否存在？**： 研究体育中的一个有趣话题——热手现象。介绍热手概念，并讨论学术研究是否揭示该现象的真实存在。利用NBA投篮记录数据分析热手现象，计算条件概率和自相关系数，并进行回归分析测试NBA球员是否具有热手表现。 此课程旨在帮助学习者掌握体育分析的基础知识，并激励他们在真实数据集上进行深入探索与创新。
Name:Introduction to Sports Performance and Data
Description:This week introduces a simple example of sports analytics in practice - the calculation of the Pythagorean expectation to model winning in team sports. This can also be used for the purposes of prediction. Examples are developed for five different sports leagues, Major League Baseball (MLB), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier League (EPL-soccer) and the Indian Premier League (IPL-cricket).
Name:Introduction to Data Sources
Description:This week will use NBA data to introduce basic and important Python codes to conduct data cleaning and data preparation. This week also discusses summary and descriptive analyses with statistics and graphs to understand the distribution of data, the characteristics and pattern of variables as well as the relationship between two variables. At the end of this week, we will introduce correlation coefficients to summarize the linear relationship between two variables.
Name: Introduction to Sports Data and Plots in Python
Description:This module introduces some ways of representing data using examples from MLB, the NBA and Indian Premier League. MLB data is used to analyze the spatial distribution of different hits. NBA data is used to generate heatmaps to illustrate the different ways in which players contribute. IPL data is used to show how team performances can be compared graphically.
Name:Introduction to Sports Data and Regression Using Python
Description:This week introduces the fundamentals of regression analysis. We will discuss how to perform regression analysis using Python and how to interpret regression output. We will use NHL data to estimate multiple regression models to identify the team level performance factors that affect the team's winning percentage. We will also use cricket data from the Indian Premier League to run regression analyses to examine whether player performance impacts player salary.
Name:More on Regressions
Description:This module uses regression analysis to investigate the relationship between team salary spending and team performance in the NBA, NHL, EPL and IPL. The module explores different ways of defining the regression model, and how to interpret competing regression model results.
Name:Is There a Hot Hand in Basketball?
Description:This week studies an interesting topic in sport, the hot hand. We will introduce the concept of hot hand and discuss the academic research that examines whether the hot hand is a phenomenon or a fallacy. We will demonstrate how to analytically test the hot hand using the NBA shot log data. We will test whether NBA players have hot hand by computing conditional probabilities and autocorrelation coefficients as well as performing regression analyses.
This course provides an introduction to using Python to analyze team performance in sports. Learners will discover a variety of techniques that can be used to represent sports data and how to extract narratives based on these analytical techniques. The main focus of the introduction will be on the use of regression analysis to analyze team and player performance data, using examples drawn from the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier LEague (EPL, soccer) and the Indian Premier League (IPL, cricket). This course does not simply explain methods and techniques, it enables the learner to apply them to sports datasets of interest so that they can generate their own results, rather than relying on the data processing performed by others. As a consequence the learning will be empowered to explore their own ideas about sports team performance, test them out using the data, and so become a producer of sports analytics rather than a consumer. While the course materials have been developed using Python, code has also been produced to derive all of the results in R, for those who prefer that environment.
