To many, the rise of the strikeout has not been a good thing for the game of baseball.
In the first of a two-part series, examining the different facets of data and how they apply to a baseball front office.
Projectionists should embrace the mystical side of the craft to find something new in this beautifully massive set of data.
The Phillies would have been better had they adopted sabermetrics earlier on, but that’s not why they’re struggling now.
There’s reason to believe that so-so pitchers with good curves and sliders don’t use them enough.
Sometimes, unorthodox mechanics are a good thing.
We can’t get all the calls right, but there is a subset of calls where baseball can do better.
Do low-strikeout hitters receive special strike zone preference?
Could a team punt Game One against the other team’s ace to gain the upper hand for the rest of the series?
Let’s talk about quantifying the most efficient way to play a baseball game.
The long-time executive always keeps us on our toes.
Frank Costanza wanted to know how you could trade Jay Buhner for Ken Phelps. Here within lies the answer.
Continuing the quest to determine how many wins consistent play is or is not worth to major league teams.
Sometimes, it makes no sense for a team to add another good relief pitcher.
We are facing a troubling decline in offense, but are shifts really the cause?
How much credit should the pitcher get for reining in runners? Here’s a framework for figuring that out.
We’ve just scratched the surface when it comes to using PITCHf/x data to tell us about hitters.
Diving back into the effects of extra-long games — before noticing someone drained the pool.
For the impatient hitter, it’s not letting a good first pitch go by. For the patient hitter, it’s not swinging at a pitch until you see something you like.
Offensive production is not linear – one and one equals more than two. How can a team take advantage of this blind spot in advanced statistics?