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).
Este curso faz parte do Programa de cursos integrados Sports Performance Analytics
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Informações sobre o curso
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Universidade de 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.
Programa - O que você aprenderá com este curso
Introduction to Sports Performance and Data
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).
Introduction to Data Sources
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.
Introduction to Sports Data and Plots in Python
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.
Introduction to Sports Data and Regression Using Python
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.
Avaliações
- 5 stars64,48%
- 4 stars26,16%
- 3 stars2,80%
- 2 stars2,80%
- 1 star3,73%
Principais avaliações do FOUNDATIONS OF SPORTS ANALYTICS: DATA, REPRESENTATION, AND MODELS IN SPORTS
Great course. Although this course focuses on sports analysis, the analyzing process I learned from it can apply to any other areas of analysis.
Best course to interact with data representation programming and libraries, especially for the great sports fan.
Great material and well paced for people working. One instructor is a bit green though.
An excellent way to get hands-on experience exploring sports data in Python/R
Sobre Programa de cursos integrados Sports Performance Analytics
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.

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