Sports Analytics - Probabilistic Graphical Models

Sports Analytics Methods – Probabilistic Graphical Models

This is a review of the basketball research conducted by Min-hwan Oh, Suraj Keshri, and Garud Iyengar applying probabilistic graphical models for basketball match simulation. With any sporting event, it is natural for analysts, bettors, and fans to make predictions regarding the outcome.  This is certainly true within the National Basketball Association. A simulation infrastructure…

Sports Analytics Methods - Bonferroni Adjusted Comparisons

Sports Analytics Methods – Bonferroni Adjusted Comparisons

A Closer Look at the Prevalence of Time Rule Violations and the Inter-Point Time in Men’s Gland Slam Tennis This is a review of the tennis analytics research conducted by Otto Kolbinger, Simon Grossmann, and Martin Lames, applying descriptive statistics, regression analysis, and Bonferroni adjusted comparisons. Paragraph 29a of the official International Tennis Federation’s Rules…

Sports Analytics - Potential Outcome Framework

Sports Analytics Methods – Potential Outcomes Framework

This is a review of the baseball analytics research conducted by David Michael Vock and Laura Frances Boehm Vock, applying a potential outcomes framework and G-computation algorithm. Baseball is an intricate sport, intertwining offensive and defensive strategies.  Baseball offense depends on the pitches the batter faces, the batter’s choice to swing, and the batter’s hitting…