Presented in the order in which they appear:
Lecture 1 (1/26): What is statistics in sports?
Lab 1 (1/28): Intro to R, RStudio, RMarkdown. Review of Intro Stats material using baseball statistics. Markdown code here.
Lecture 2 (2/2): Stats in baseball. Overview of sabermetrics, runs created. Bivariate data analysis
Lab 2 (2/4): Lahman package, bivariate analysis, linear regression, correlation matrices. Markdown code here.
Lecture 3 (2/9): Baseball stats, pitchers. Defense independent pitching. Multivariate regression, model checks, prediction methods
Lab 3 (2/11): Lahman package, multiple regression, pitching statistics
Lecture 4 (2/16): Logistic regression, field goal kicking
Lab 4 (2/18): Multiple logistic regression, NFL field goals
Lecture 5 (2/23): NFL decision-making
Lab 5 (2/25): Simulating expected points and NFL plays
Lab 6 (3/3): NBA, expected points, data cleaning, ggplot
Lecture 6 (3/8): NBA, possession based metrics
Lab 7 (3/10): NBA, effective field goal percentage
Lecture 7 (3/22): NHL, Shot metrics, PDO, Score adjustments
Lab 8 (3/24): NHL shot statistics and repeatability
Lecture_8 (3/29): Stein’s Paradox & NHL shot percentages
Lab 9 (3/31): Implementing the James-Stein estimator using NHL metrics
Lec 9 (4/5): Referee behavior
Lab 10 (4/7): NFL penalties (Raw Github code here)
Lec 10 (4/12): Power rankings, Bradley-Terry
Lab 11 (4/14): Bradley-Terry models
Lecture_11 (4/19): Statistics in soccer
Note: Any information from this course may be used, as long as the original source is appropriately cited.