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
This specialization introduces Red Hat Enterprise Linux system administration and private cloud capabilities of IBM Systems. IBM Power servers will be used to demonstrate these concepts. Through... more
In this 2 hours long project you will create a business value proposition canvas in Miro, categorizing business products, services and features by company pillars, mapping customer gains, pains and... more
In this course, you will learn about the technical foundations and key terminology related to generative artificial intelligence (AI). You will also learn how to evaluate the risks and benefits of... more
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business... more
Take a trip with me in this the third and final course of this Economics 100 Specialization which is a direct adaptation from one of my courses I teach at Rice University. Specifically, in this... more
Powered by
