University of Michigan - Foundations of Sports Analytics: Data, Representation, and Models in Sports
- Offered byCoursera
Foundations of Sports Analytics: Data, Representation, and Models in Sports at Coursera Overview
Duration | 49 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Foundations of Sports Analytics: Data, Representation, and Models in Sports at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 5 in the Sports Performance Analytics Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 49 hours to complete
- English Subtitles: English
Foundations of Sports Analytics: Data, Representation, and Models in Sports at Coursera Course details
- 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.
Foundations of Sports Analytics: Data, Representation, and Models in Sports at Coursera Curriculum
Introduction to Sports Performance and Data
Introduction to Foundations and Instructor Stefan Szymanski
Faculty Introduction: Wenche Wang
Pythagorean Expectation & Baseball Part 1
Pythagorean Expectation & Baseball Part 2
Pythagorean Expectation & the IPL
Pythagorean Expectation & the NBA
Pythagorean Expectation & English Football
Pythagorean Expectation as a Predictor in the MLB
Foundations of Sports Analytics Course Syllabus
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Assignment Overview
Week 1 - Sample Notebook
Week 1 R Content
Week 1 Quiz
Introduction to Data Sources
Accessing Data in Python I
Accessing Data in Python II
Data Exploration
Summary Statistics
More on Summary Statistics
Correlation Analysis
Assignment Overview
Assignment Instructions- Part 1
Assignment Instructions- Part 2
Assignment Instructions- Part 3
Week 2 - Sample Notebook
Week 2 R Content
Week 2 - Quiz 1
Week 2 - Quiz 2
Week 2 - Quiz 3
Introduction to Sports Data and Plots in Python
Data Representation: Cricket Pt. 1
Data Representation: Cricket Pt. 2
Data Representation: Baseball
Data Representation: Basketball
Assignment Overview
Assignment Instructions - Part 1
Week 3 - Part 1 - Sample Notebooks
Assignment Instructions - Part 2
Week 3 - Part 2 - Sample Notebook
Week 3 R Content
Week 3 - Quiz 1
Week 3 - Quiz 2
Introduction to Sports Data and Regression Using Python
Introduction to Regression Analysis
Interpreting Regression Results
More on Regressions
Regression Analysis - Intro to Cricket Data
Regression Analysis - Batsman's performance and salary
Regression Analysis - Bowler's performance and salary
Assignment Overview
Assignment Instructions - Part 1
Assignment Instructions- Part 2
Assignment Instructions- Part 3
Week 4 - Sample Notebook
Week 4 R Content
Week 4 - Quiz 1
Week 4 - Quiz 2
Week 4 - Quiz 3
More on Regressions
Using regression analysis - an example with NBA data
Using regression analysis - an example with EPL data
Using regression analysis - an example with MLB data
Using regression analysis - an example with NHL data
Assignment Overview
Assignment Instructions
Week 5 - Sample Notebook
Week 5 R Content
Week 5 Quiz
Is There a Hot Hand in Basketball?
Hot Hand: Phenomenon or Fallacy?
NBA Shot Log Data Preparation I
NBA Shot Log Data Preparation II
Conditional Probability
Conditional and Unconditional Probabilities
Autocorrelation
Regression Analysis on Hot Hand I
Regression Analysis on Hot Hand II
Assignment Overview
Assignment Instructions - Part 1
Assignment Instructions - Part 2
Assignment Instructions - Part 3
Week 6 - Sample Notebook
Post-Course Survey
Week 6 R Content
Week 6 - Quiz 1
Week 6 - Quiz 2
Week 6 - Quiz 3
Foundations of Sports Analytics: Data, Representation, and Models in Sports at Coursera Admission Process
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