Here's some of what I've been reading over the past week:
The Baseball Analysts: Another Attempt (at a new boxscore)
This is the nicest way to look at a baseball game since the Win Probability graph. I might even like this one better. Provides different information, of course, but this is incredibly readable and intuitive given how much information it contains. Now, can it be automated?
Presenting FanGraphs Audio | FanGraphs Baseball
FanGraphs enters the airways! I listed today to their most recent piece on prospects on my drive home. Given that my only other mainstay baseball podcast is ESPN's Baseball Today, this was very refreshing--I think they're onto something really good here. Not overly stat-heavy thus far--and probably shouldn't be--but it's informed, sensible talk about baseball. Looking forward to listening all season.
Walk Like a Sabermetrician: Pseudo-SIERA Using BsR
Patriot jumps into the SIERA debate and argues that BsR can be used to model DIPS in arguably a more intuitive way than what Eric & Matt came up with. Voros McCracken himself also put together a DIPSy BsR model some years ago, though it still used HR instead of batted ball terms. I like Patriot's better for that reason.
Free Player Injury Database | Rotoblog
Josh provides an amazing resource: complete injury database for 2002-2009. And Jeff's already started working through the data. :) This could turn out to be a goldmine. As Tango said on his blog (hat tip to Tango!), this could be the 2010 resource of the year--or, it may lay the foundation for an injury tool the likes of which we haven't seen before.
The Baseball Analysts: The Verducci Effect
Jeremy uses pitch data and the new injury database to test the Verducci effect: the hypothesis that young pitchers who experience a large surge in innings pitched (30 or more) over the course of one year are at additional risk to injury and/or loss in performance. He finds evidence to support this claim lacking.
The Baseball Analysts: There Are Two Types of Pitchers....
Sky uses principle component analysis on pitchers and discovers that the first major axis separating different types of pitchers is...wait for it...power vs. finesse pitchers! In all seriousness, I like this work, as it helps us see major trends in player performance through the eyes of an algorithm designed to pick out differences--rather than through our own eyes. It's nice when the algorithm finds something that makes sense. :)
THE BOOK--Playing The Percentages In Baseball: The Marcels takes on the field
Tango tries his hand on the J. Cross projection dataset, pitting Marcel against the field. The result? CHONE performs well, ZiPS ties, PECOTA was beat handily by the monkey.
So, what did I miss? Feel free to add links in the comments. I may make this a regular feature here, so feel free to send good links my way--I try, but I don't always get a chance to read everything. If there's good stuff I'm missing, I'll include it in the next edition.