M-SABR’s pair of Dans take on the task of creating their own prospect list. Using statistics and modeling, they attempt to provide a trustworthy method for evaluating MLB’s next superstars.
Research
An Evaluation of Cy Young Award Selection Using Machine Learning
Rohan Patel uses machine learning to take a deep dive into what statistics are most valued by Cy Young Award voters and how this has changed over time.
If You’re Not Cheating, You’re Not Trying: Data Analysis of the 2017 Houston Astros
Ryan Jafri looks back at the 2017 Houston Astros scandal-ridden championship season with a data-driven approach.
Improving Defensive Positioning: An Algorithm to Shift Effectively
With shifts becoming more extreme and more commonplace throughout MLB, we set out to create an algorithm to make them more effective.
Checking in with the Enhanced Quality Start: A 2018 Recap
M-SABR graduate advisor Kyle Kumbier is back at it again. See his improved pitching statistics in action as he reflects on the 2018 season.
Time to Change it Up: An Examination of Changeup Usage and Effectiveness from 2015-2018
A 2010 study conducted by Dave Allen over at The Baseball Analysts suggested that all else equal, pitchers should try to avoid throwing their changeups to same-handed hitters and should be more willing to throw their changeups to opposite-handed hitters. 2010 was nearly a decade ago and baseball statistics, technology, and data manipulation have come quite far since then, so we figured we would investigate Allen’s claim that a changeup thrown to an opposite-handed hitter should be more effective than one thrown to a same-handed hitter. What we found was not what we expected.
Enhancing the Quality Start
M-SABR graduate advisor Kyle Kumbier takes a thoroughly-researched deep dive into improving the Quality Start.
The Opener is the Next Huge Advantage in Major League Baseball
Sergio Romo is one of the first relievers to ever start both games in the same weekend. He won’t be the last.