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).
ミシガン大学（University of Michigan）
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
- 5 stars67.01%
- 4 stars23.71%
- 3 stars2.06%
- 2 stars3.09%
- 1 star4.12%
FOUNDATIONS OF SPORTS ANALYTICS: DATA, REPRESENTATION, AND MODELS IN SPORTS からの人気レビュー
Great material and well paced for people working. One instructor is a bit green though.
Great course. Although this course focuses on sports analysis, the analyzing process I learned from it can apply to any other areas of analysis.
An excellent way to get hands-on experience exploring sports data in Python/R
Best course to interact with data representation programming and libraries, especially for the great sports fan.
Sports Performance Analytics専門講座について
Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball. Analysis of team and player performance data has continued to revolutionize the sports industry on the field, court, and ice as well as in living rooms among fantasy sports players and online sports gambling.