3/18/2024

Topics Covered in Basketball Projects

• Correlation analysis
• Data wrangling techniques - see Modern Data Science with R
• Effect size
  • Cohen's d
  • Hedges's g
  • Glass's delta
• Elo rating system
• Gaussian mixture models
• Hierarchical modeling
• MANOVA and Pillai's Trace
• Multiple linear regression and ANOVA models
• Nonparametric Methods
  • Kruskal-Wallis Test
  • Bootstrapping
• Rolling average models
• Time series models (ARIMA)
• Z-scores and standard deviations from different eras
• Partial least squares - see Chapter 4
• Principal component regression - see Chapter 4
• K-Nearest Neighbors - see Chapter 8
• Decision Trees - see Chapter 9
• Bagging - see Chapter 10
• Random forest - see Chapter 11
• Gradient boosting - see Chapter 12
• Model tuning - see Chapter 13
• Support vector machines - see Chapter 14
• Stacked models - see Chapter 15
• Principal Component Analysis (PCA) - see Chapter 17
• K-means clustering - see Chapter 20
• Hierarchical clustering - see Chapter 21

Project #3 - Written executive summary (3 to 5 pages) for analytics staff with the Charlotte Hornets. See email message on February 28, 2024

Project #4 - Baseball or team performance projects (presentations will begin next week