Sports Predictive Analytics
Advancements in the field of predictive analytics have led to the creation of predictive models that can compute probabilities of sports win/loss prospects and analyze the performance of players. A well-known success story of sports predictive analytics is its role in predicting the performance of baseball players, which enabled the Boston Red Sox to win three World Series titles since 2004 after 86 years of losses.
In this course, students will compute simple statistics of a game which has already been played, then use correlation to detect statistical relationships between different game metrics. The science of rating and ranking will be covered in detail, and regression models will be used for estimating a metric from several predictor variables. Predictive models will then be used to compute win/loss probabilities.
- Metrics used for team and player evaluation
- Use of sabermetrics for the evaluation of teams
- Team ranking using Massey, Colley and Elo methods
- Predictive models for win/loss probabilities
- Regression techniques for machine learning
- Feature selection using Ridge and Lasso regression
- Sentiment analysis and its role in predicting the game outcomes
- Player and team performance report generation
- Apply predictive analytics to sports data to predict win/loss and other probabilities
Software: R, a free software environment for statistical computing and graphics, is used for this course.
Course typically offered: Online in Winter and Summer
Prerequisites: Introduction to Statistics or equivalent knowledge recommended. Familiarity with R, SAS, SPSS, or similar statistical software recommended.
Next Steps: Upon completion of this course, consider taking Predictive Analytics to continue learning.
More Information: For more information about this course, please contact email@example.com.
Course Number: CSE-41245
Credit: 2.00 unit(s)
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