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In this course, you will :
- Discover a variety of techniques for representing sports data, as well as how to extract narratives based on these analytical techniques.
- The introduction will focus on using 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).
- How to use Python to programme data to test the claims that underpin the Moneyball storey, and how to examine the evolution of Moneyball statistics since the book's publication
- The learner will be shown how to use Python to forecast game results in professional sports.
- The course will teach the learner how to assess the reliability of a model using betting odds data.
- Explore supervised machine learning techniques with the python scikit learn (sklearn) toolkit and real-world athletic data to gain a better understanding of machine learning algorithms and how to predict athletic outcomes.
Syllabus :
- Foundations of Sports Analytics: Data, Representation, and Models in Sports
- Moneyball and Beyond
- Prediction Models with Sports Data
- Wearable Technologies and Sports Analytics
- Introduction to Machine Learning in Sports Analytics
