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Learner Reviews & Feedback for Prediction Models with Sports Data by University of Michigan
23 ratings
About the 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.
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1 - 2 of 2 Reviews for Prediction Models with Sports Data
By VALAY S
•Jul 18, 2022
This course covers basics of modeling in form of logistic regression.
The course is worth for those who want hands-on experience/beginning in pandas/python data science coding and those who are already familiar with mathematics & statistics of regression
By Надежда В
•Feb 15, 2022
Excellent course
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