Prediction Models with Sports Data
About this Course
In this course the learner will be shown how to generate forecasts of game results in professional sports using Python. The main emphasis of the course is on teaching the method of logistic regression as a way of modeling game results, using data on team expenditures. The learner is taken through the process of modeling past results, and then using the model to forecast the outcome games not yet played. The course will show the learner how to evaluate the reliability of a model using data on betting odds. The analysis is applied first to the English Premier League, then the NBA and NHL. The course also provides an overview of the relationship between data analytics and gambling, its history and the social issues that arise in relation to sports betting, including the personal risks.Created by: University of Michigan
Related Online Courses
In today\'s rapidly evolving digital landscape, the importance of cybersecurity cannot be overstated. Threats to organizations, both large and small, are on the rise, and the consequences of... more
In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts,... more
This course will give you an introduction to bacteria and chronic infections. Leading experts in the field will make you familiar with the fundamental concepts of microbiology and bacteriology such... more
This course explores the features of complexity science. Our world is connected by an abundance of complex systems. Across all levels of organizations from physical, biological world to the social... more
The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately... more
Powered by
