Foundations of Sports Analytics: Data, Representation, and Models in Sports
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).
Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- Duration 49 hours
- Course by University of Michigan
-
Offered by
About
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.Modules
Week 1: Lecture Videos
-
8
Videos
-
3
Readings
8
Videos
- 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
3
Readings
- Course Syllabus
- Help Us Learn More About You
- A Note on Notebooks
Week 1: Lecture Notebooks
-
6
Lab
6
Labs
- Pythagorean expectation and MLB
- Pythagorean expectation and MLB - Self Test Solutions
- Pythagorean expectation and the IPL
- Pythagorean expectation and the NBA
- Pythagorean expectation and English Football
- Pythagorean expectation as a Predictor in MLB
Week 1: Assignment
-
2
Readings
-
1
Lab
-
1
Assignment
1
Assignment
- Week 1 Quiz
1
Labs
- Assignment 1 Workspace
2
Readings
- Assignment Overview
- Week 1 - Sample Notebook
Week 1: R Content
-
1
Readings
1
Readings
- Week 1 R Content
Week 2: Lecture Videos
-
6
Videos
6
Videos
- Accessing Data in Python I
- Accessing Data in Python II
- Data Exploration
- Summary Statistics
- More on Summary Statistics
- Correlation Analysis
Week 2: Lecture Notebooks
-
4
Lab
4
Labs
- Accessing Data Using Python
- Data Exploration and Summary Statistics
- Summary Statistics and Correlation Analysis
- Week 2 - Self Test Solutions
Week 2: Assignment
-
5
Readings
-
1
Lab
-
3
Assignment
3
Assignment
- Week 2 - Quiz 1
- Week 2 - Quiz 2
- Week 2 - Quiz 3
1
Labs
- Assignment 2 Workspace
5
Readings
- Assignment Overview
- Assignment Instructions- Part 1
- Assignment Instructions- Part 2
- Assignment Instructions- Part 3
- Week 2 - Sample Notebook
Week 2: R Content
-
1
Readings
1
Readings
- Week 2 R Content
Week 3: Lecture Videos
-
4
Videos
4
Videos
- Data Representation: Cricket Pt. 1
- Data Representation: Cricket Pt. 2
- Data Representation: Baseball
- Data Representation: Basketball
Week 3: Lecture Notebooks
-
3
Lab
3
Labs
- Basketball Heatmap
- Indian Premier League Graphs
- Simple Heatmaps Baseball
Week 3: Assignment
-
5
Readings
-
2
Lab
-
2
Assignment
2
Assignment
- Week 3 - Quiz 1
- Week 3 - Quiz 2
2
Labs
- Week 3 Assignment - Part 1 - Workspace
- Week 3 Assignment - Part 2 - Workspace
5
Readings
- 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
-
1
Readings
1
Readings
- Week 3 R Content
Week 4: Lecture Videos
-
6
Videos
6
Videos
- 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
Week 4: Lecture Notebooks
-
3
Lab
3
Labs
- Introduction to Regression Analysis
- Introduction to Regression Analysis - Self Test Solutions
- Regression Analysis with Cricket Data
Week 4 Assignment
-
5
Readings
-
1
Lab
-
3
Assignment
3
Assignment
- Week 4 - Quiz 1
- Week 4 - Quiz 2
- Week 4 - Quiz 3
1
Labs
- Week 4 - Assignment Workspace
5
Readings
- Assignment Overview
- Assignment Instructions - Part 1
- Assignment Instructions- Part 2
- Assignment Instructions- Part 3
- Week 4 - Sample Notebook
Week 4: R Content
-
1
Readings
1
Readings
- Week 4 R Content
Week 5: Lecture Videos
-
4
Videos
4
Videos
- 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
Week 5: Lecture Notebooks
-
4
Lab
4
Labs
- EPL
- Hockey
- MLB
- NBA
Week 5: Assignment
-
3
Readings
-
1
Lab
-
1
Assignment
1
Assignment
- Week 5 Quiz
1
Labs
- Week 5 - Assignment Workspace
3
Readings
- Assignment Overview
- Assignment Instructions
- Week 5 - Sample Notebook
Week 5: R Content
-
1
Readings
1
Readings
- Week 5 R Content
Week 6: Lecture Videos
-
8
Videos
8
Videos
- 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
Week 6: Lecture Notebooks
-
4
Lab
4
Labs
- Understanding and Cleaning the NBA Shot Log Data
- Using Summary Statistics to Examine the Hot Hand
- Using Regression Analysis to Test the Hot Hand
- Using Regression Analysis to Test the Hot Hand - Self Test Solutions
Week 6: Assignment
-
6
Readings
-
1
Lab
-
3
Assignment
3
Assignment
- Week 6 - Quiz 1
- Week 6 - Quiz 2
- Week 6 - Quiz 3
1
Labs
- Week 6 - Assignment Workspace
6
Readings
- 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
-
1
Readings
1
Readings
- Week 6 R Content
Auto Summary
Discover the exciting world of sports analytics with this foundational course focused on using Python to analyze team performance. Dive into regression analysis techniques across various sports leagues like NFL, NBA, NHL, EPL, and IPL. Empower yourself to generate your own insights and become a producer of sports analytics. The course is designed for beginners and offers code in both Python and R. Available through Coursera's Starter subscription, it's perfect for data enthusiasts keen on sports.
Wenche Wang
Stefan Szymanski