Optimizing User Defined Functions with Apache Spark™ and R in the Real World: Scaling Pitch Scenario Analysis with the Minnesota Twins Part 2
Introduction In part 1 we talked about how Baseball Operations for the Minnesota Twins wanted to run up to 20k simulations on 15 million historical pitches - 300 billion total simulations - to more accurately evaluate player performance. The idea is simple: if 15 million historical pitches sketched an image of player performance, 300 billion...
How the Minnesota Twins Scaled Pitch Scenario Analysis to Measure Player Performance – Part 1
Statistical Analysis in the Game of Baseball A single pitch in Major League Baseball (MLB) generates tens of megabytes of data, from pitch movement to ball rotation to hitter behavior to the movement of each individual baseball player in response to a hit. How do you derive actionable insights from all this data over the...
