Chevron Left
Moneyball and Beyond(으)로 돌아가기

미시건 대학교의 Moneyball and Beyond 학습자 리뷰 및 피드백

37개의 평가

강좌 소개

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....

최상위 리뷰


2021년 8월 25일

I learned a lot about baseball and the Python language. Thank you for the great course.


2022년 3월 17일

An excellent way to develop Python skills to interesting topics.

필터링 기준:

Moneyball and Beyond의 5개 리뷰 중 1~5

교육 기관: Jim B

2021년 8월 26일

I learned a lot about baseball and the Python language. Thank you for the great course.

교육 기관: Mike H

2022년 3월 18일

An excellent way to develop Python skills to interesting topics.

교육 기관: sibghatulllah m

2022년 1월 24일


교육 기관: Jaime M L

2022년 6월 29일

Some baseball concepts are complex for european people. But the content of the course is really interesting and very well explained.

교육 기관: Kevin . H

2022년 10월 27일

I could not have been more disappointed in this course. Its lack of depth, both mathematically and from a sports analysis perspective, shocked me. This course spends almost no time at all on WHY any of the statistics presented are used. Nor on how to develop models to evaluate sport performance. Rather, it focuses almost entirely on prescriptive steps on how to calculate basic stats models. And, given all the time that is spent just walking through the code, one might expect that it is instructive and well-written. Sadly, that is not the case. The code for the course DRAMATICALLY under uses the power of the libraries included (Pandas and Numpy). Effectively, the first 4 weeks of the course are just tediously walking through dozens of convoluted lines of code, to do something that could have been 2 lines by simply using Pandas built-in functions.

So, if you are interested in learning about statistical analysis methods for sports, you can literally watch the first video from week 5 and get 95% of the value of this course without having to slog through 4 weeks of (badly) coding up basically 2 regressions, only to prove that when two different people do the same math, they will arrive at the same result.

If you are interested in learning how to use Python to do sports analysis, AVOID THIS CLASS COMPLETELY!! The lectures contain no information about how to apply any of what’s covered to anything outside of the one example given. AND, the code is so needlessly convoluted, it’s clear that whomever wrote it did NOT know how to use the tools available to them. So, if you are newer to Python, this course would be a little like trying to learn how to cook in an Italian style, by listening to someone read a recipe for canned spaghetti.