Foundations of Sports Analytics: Data, Representation, and Models in Sports
- 4.5
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Approx. 49 hours to complete
Course Summary
This course introduces students to the foundations of sports analytics, using real-world examples and data to teach key concepts and techniques.Key Learning Points
- Learn statistical analysis techniques specific to sports data
- Understand how to apply data analysis to improve team performance
- Gain insight into the business side of sports analytics
Related Topics for further study
Learning Outcomes
- Apply statistical analysis techniques to sports data
- Improve team performance through data-driven decision making
- Understand the role of analytics in the sports industry
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics
- Familiarity with Excel or other spreadsheet software
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Sports Performance Analytics
- Sports Data Analytics and Visualization
- Sports Marketing and Sponsorship
Related Education Paths
Notable People in This Field
- Statistician and Founder, FiveThirtyEight
- Baseball Writer and Analyst
Related Books
Description
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).
Outline
- 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
- Help Us Learn More About You
- A Note on Notebooks
- 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
Summary of User Reviews
This course on Foundations of Sports Analytics has received positive reviews from users. The course covers a wide range of topics related to sports analytics and provides practical examples to help learners understand the concepts better. One key aspect that many users found good was the interactive nature of the course.Pros from User Reviews
- Practical examples help learners understand concepts better
- Covers a wide range of topics related to sports analytics
- Interactive nature of the course keeps learners engaged
- Well-structured course content
- Experienced instructors with industry knowledge
Cons from User Reviews
- Some users found the course to be too basic
- Some learners felt that the quizzes and assignments were too easy
- The course may not be suitable for learners with advanced knowledge of sports analytics
- Limited interaction with instructors
- Some learners felt that the course was too focused on theory and lacked practical applications
Wenche Wang
4.5 Raiting
Foundations of Sports Analytics: Data, Representation, and Models in Sports
- 4.5
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This course provides an introduction to using Python to analyze team performance in sports. Introduction to Sports Performance and Data Introduction to Foundations and Instructor Stefan Szymanski...
