Labs & lecture notes

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.