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In this course, you will learn :
- an introduction to using Python to analyse sports team performance
- discover a variety of techniques for representing sports data, as well as how to extract narratives based on these analytical techniques.
- The introduction will concentrate on the use of regression analysis to analyse team and player performance data, with examples 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).
Syllabus :
1. Introduction to Sports Performance and Data
- Faculty Introduction: Wenche Wang
- Pythagorean Expectation & Baseball
- Pythagorean Expectation & the IPL
- Pythagorean Expectation & the NBA
- Pythagorean Expectation & English Football
- Pythagorean Expectation as a Predictor in the MLB
2. Introduction to Data Sources
- Accessing Data in Python
- Data Exploration
- Summary Statistics
- More on Summary Statistics
- Correlation Analysis
3. Introduction to Sports Data and Plots in Python
- Data Representation: Cricket
- Data Representation: Baseball
- Data Representation: Basketball
4. Introduction to Sports Data and Regression Using Python
- 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
5. 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
5. Is There a Hot Hand in Basketball?
- Hot Hand: Phenomenon or Fallacy?
- NBA Shot Log Data Preparation
- Conditional Probability
- Conditional and Unconditional Probabilities
- Autocorrelation
- Regression Analysis on Hot Hand
