Baseball Prospectus Newsbeat
Friday, July 18, 2014
Is this a good system? I don’t ask that question as an exercise in moral philosophy with the poor pitiable minor leaguers cast as a vulnerable group in need of protection. I’m asking this from the other side. Is it smart for teams in Major League Baseball to willfully pay their minor leaguers so little? Aside from cost-savings and the moralizing, could teams actually have more success developing players if they opened their wallets a little wider?
In addition to the points made in the article, a lot of minor leaguers have to supplement their income by working in the offseason at a job rather than working on their craft and potentially making a breakthrough. The assumptions used by the author seem reasonable, though I don’t know enough about minor league finances to know how pay works, or how players staying in the system longer would affect the number of players in the organization.
Friday, April 04, 2014
Remember all those articles about how Billy Hamilton could revolutionize the game?...
HACKING MASS, our competition to pick the least dangerous hitters and least effective pitchers in the majors, returns for the 2014 season!... You’ll have a full week to enter (the deadline is April 11, 2014, at midnight PST), but it’s easy to change players, so feel free to go all in on Darwin Barney on the entry form today, then switch to Ryan Goins next Friday morning…
Your 10 players will be:
one each at catcher and each infield position (5 players)
one at each outfield position (3 players)
two pitchers (2 players)
Simply pick the players who you think will be the stiffest at each position. A team’s aggregate stiffness is measured by summing the ESPN (Exuded Stiff Points, Net) of all of the players on your team. For hitters, ESPN is 0.800, minus his OBP, minus his SLG, and multiplied by plate appearances - i.e., (.8-OPS)*PA. For pitchers, the formula is the pitcher’s ERA, minus 4.5, times his innings pitched, divided by three, or (ERA-4.5)*IP/3. This results in similar Stiffness scores for the firmest hitters and pitchers.
In each case, it isn’t enough for a player to simply suck; somehow the Stiffest of the Stiff must find a way to remain in the lineup or rotation.
Monday, March 03, 2014
Rather than identifying a single strike zone and giving binary credit for each pitch relative to that strike zone’s borders (i.e., strike or no strike), our model gives partial credit for each pitch based on that pitch’s likelihood of being called a ball or a strike. To determine that, we created a probability map of likely calls… To reflect what is best known about the way the size and position of the strike zone shifts from count to count and batter to batter, we ran individual models for each set of batter and pitcher handedness as well as [type of pitch]. The smoothing parameters of each model were allowed to vary by count, so that while the general shape of the strike zone derived for each variable combination did not change, the width and height of it did (reflecting, for example, a larger strike zone on 3-0 counts than on 1-2 or 0-2 counts). We also accounted for the changing size of the strike zone from season to season (although these yearly changes are much smaller than the other changes we measured).
We also corrected the data in several ways before running these models. First, all pitch classifications were hand-labeled by Pitch Info to eliminate variability in pitch labels… To account for batter height differences, we normalized the height of each pitch by the batter’s height using what is now the standard formula (first published by Mike Fast). We also used the correction scheme that Mike published at BP for correcting the X and Y location of each pitch based on the likely distribution of pitch locations that each pitcher would use against left-handed hitters and right-handed hitters…
Rather than simply give a single credit for each pitch (~.14 runs) as has been done in many previous models, we looked at the count in which each pitch was framed and gave credit equal to the difference in runs between framing or not framing that pitch. For example, a frame in an 0-2 count was counted as more valuable than a frame in an 0-0 count, because a frame in an 0-2 count can result in a large change in run expectancy while a frame in an 0-0 count does not have quite the same impact… The run value for a framed pitch is the run value differential for that count… multiplied by the residual of the probability—in other words, if an 0-0 pitch is called a strike in a spot where it’s normally called a strike just 80 percent of the time, the catcher will get 20 percent of the available value (.08) for a total of .0004 runs credited (which will later be adjusted based on the pitcher and umpire impact). Failing to get a strike on the same pitch would result in a .0016 run deduction…
We empirically determined each pitcher’s value—to isolate it from each catcher’s value—by performing a WOWY (“With or Without You”) analysis… We also made systematic but small changes to the data based on the umpire who was calling each game…
we have regressed career totals to the league average… Because seasonal variability is different from career variability, we also regressed seasonal totals to career totals based on a similar formula…
You can find all of this new framing and blocking information in a couple place on the Baseball Prospectus site.
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