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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/moneyball-and-beyond
课程评论：没有评论
课程名称：《Moneyball与超越》 课程概述：书籍《Moneyball》引发了专业体育性能统计分析的革命，展示了数据分析如何提升团队的获胜百分比。本课程将教您如何使用Python编程来验证与《Moneyball》故事相关的主张，并考察自书籍出版以来“Moneyball”统计数据的演变。学习者将通过公开数据集计算棒球表现统计数据，课程从基础的上垒率（OBP）和长打率（SLG）分析，逐步深入到更高级的统计量，例如基于跑球期望矩阵计算的替代球员胜利数（WAR）。课程结束时，学习者将能够利用这些统计数据进行自己的团队和球员分析。 课程大纲： - **第1周**：介绍“Moneyball”故事并探讨测试该故事的方法。开始复制“Moneyball”测试，通过建立团队胜利与两个表现统计（上垒率和长打率）之间的关系。 - **第2周**：估算大联盟球员薪资与其表现统计（上垒率和长打率）之间的关系。结果确认了“Moneyball”故事：在《Moneyball》出版前，上垒率相对于长打率被低估，出版后这种相对重要性发生了逆转。 - **第3周**：更新Hakes与Sauer的分析，估算1994-2015年间上垒率和长打率的报酬，并展示如何将报酬与长打率的各个组成部分（步数、单打、双打、三打和本垒打）相关联。 - **第4周**：介绍跑球期望的概念，显示如何推导跑球期望矩阵并根据2018赛季所有事件的MLB数据集计算跑值。跑值按事件类型（步数、单打、双打等）和球员进行计算。 - **第5周**：探讨替代球员胜利数（WAR）的概念，并展示如何根据击球表现计算WAR。研究比赛跑值、团队胜率与球员薪资之间的关系，跑值与胜利及薪资有很高的相关性，跑值在一定程度上可以预测胜率。
Name:Week 1
Description:In this module we introduce the Moneyball story and explore the method used to test that story. We begin the process of replicating the moneyball test by establishing the relationship between team winning and and two performance statistics - on base percentage (OBP) and slugging percentage (SLG).
Name:Week 2
Description:In this module we estimate the relationship between MLB player salaries and their performance statistics, OBP (on base percentage) and SLG (slugging). The results appear to confirm the Moneyball story - OBP was undervalued relative to SLG prior to the publication of Moneyball, while after publication the relative significance is reversed.
Name:Week 3
Description:This module updates the analysis of Hakes & Sauer and estimates the rewards to OBP and SLG over the period 1994 -2015. In addition it shows how rewards can be related to individual components of SLG: walks, singles, doubles, triples, and home runs.
Name:Week 4
Description: This module introduces the concept of run expectancy, shows how to derive the run expectancy matrix and the calculation of run values based on an MLB dataset of all events in the 2018 season. Run values are calculated by event type (walks, singles, doubles, etc.) and by player.
Name:Week 5
Description:This module examines the concept of Wins Above Replacement (WAR) and shows how to calculate WAR based on batting performance. The relationship between play run values team win percentage and player salaries is then explored. Run values are shown to have a high degree of correlation with winning and with salaries. Run values can to a limited extent predict win percentage.
The book Moneyball triggered a revolution in the analysis of performance statistics in professional sports, by showing that data analytics could be used to increase team winning percentage. This course shows how to program data using Python to test the claims that lie behind the Moneyball story, and to examine the evolution of Moneyball statistics since the book was published. The learner is led through the process of calculating baseball performance statistics from publicly available datasets. The course progresses from the analysis of on base percentage and slugging percentage to more advanced measures derived using the run expectancy matrix, such as wins above replacement (WAR). By the end of this course the learner will be able to use these statistics to conduct their own team and player analyses.
