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Around the SaberSphere 12/21: 2012's Best Defensive Teams, Mike Minor, Park Factors

Friday's edition of sabermetric links includes a look at the best and worst defensive teams of 2012, Mike Minor's resurgent second-half, park factors and more..


John Dewan of Acta Sports and Baseball Info Solutions presents the list of 2012's Best and Worst defensive teams: Stat of the Week | ACTA Sports

The defensive shift is more popular in the American League, especially in the East, where four of the top five top teams reside. Some of that is specific to players. The Indians played in a division with Adam Dunn and Prince Fielder, and the Blue Jays, Rays, Orioles, and Red Sox used a lot of defensive shifts against Carlos Pena, David Ortiz, and Mark Teixeira. Those five hitters were the most heavily shifted players in baseball. Still, there are players on every team that should be shifted, and certain teams are taking advantage more than others.

Ben Lindbergh of Baseball Prospectus questions whether Mike Minor's resurgent second half was due to luck or a change in approach: Baseball Prospectus | Overthinking It: The Mike Minor Mystery

So, which was most responsible for Minor’s second-half success: more mixing of pitches, better fastball, or better luck? (Or something else entirely!) Did Minor stop serving up hits and homers because his luck turned and more balls began to find gloves? Or did he succeed because he started doing things that were more likely to lead to outs?

Also at BP, Colin Wyers delivers a great reference on the implementation of park factors: Baseball Prospectus | BP Unfiltered: The Philosophy of Park Factors

Those are useful things to know if we want to project a player's skill, but TAv is not supposed to measure a player's skill, it's supposed to measure a player's value to his team. So our interest in park adjustment is not in seeing what a player would have done in a different park (I abjure those sorts of hypotheticals in value stats), but accounting for the different value of a run in different park contexts. We all know that Juan Pierre wasn't the sort of player who could really take advantage of Coors back when he was on the Rockies, for instance. But the average player coming in to face the Rockies could, and that changed the run environment Pierre played in, so even if he couldn't hit additional home runs in Coors it still affected the run environment he played in, and in a value stat that's important to account for.

Matt Swartz of the Hardball Times concludes his series on game theory and baseball with an awesome pitch selection model: Game theory and baseball, Part 5: Generalizing the pitch selection model

These articles have only served to develop a framework. Going forward, analysts could use FanGraphs’ o-swing and z-swing statistics to actually calculate some more exact payoffs. Variation in pitch usage by count could be explored differently, too, using these payoffs. Additionally, discussions with players could determine ability to detect pitches. Variations in counts could be used to adjusted x+y and z frameworks. Other pitches could be added to complicate the equilibrium for pitchers with more than two pitches, too.

Finally, I have to announce that this will be the last sabermetric link post of this form at Beyond the Box Score.

I've really enjoyed doing these link pieces since July and I hope the readers have enjoyed them at least half that much. Also, hopefully it became some sort of a resource for those who wanted to find the good baseball pieces from around the internet.

BTBS will be moving towards a different form of links going forward. The goal will be to respond and interact with pieces that our writers find interesting, follow this link for an example.

Anyways, it's been a fun ride, but before I go for the last time, I'll leave you with this clip of the great Josh Gibson, whose birthday is today: Baseball Hall of Fame - Biographies: Josh Gibson - YouTube