Masahiro Tanaka, Jon Lester, Nelson Cruz, Mark Melancon and Dinelson Lamet are profiled.
Buoyed by the long, winding collective stroll through the charred aftermath of the fantasy landscape one day after the trade deadline, I figure I’ll use the pretense to talk about some of the players I acquired and fired in my own home league, in hopes that those of you playing in leagues with slightly later deadlines will be able to benefit from some insights on a few difficult-to-evaluate players. Let’s take a look at five on the move from one of my rosters.
Masahiro Tanaka (SP)—NYY: Tanaka is having one of the stranger seasons around, with all kinds of strange splits and, underlying everything, an insane home-run rate. His whiff and walk numbers are delicious, and he gets grounders at a good clip to boot. But, much like his fallen comrade Michael Pineda, the contact he does yield is hard contact. And that is, of course, particularly true in the air this year, where his average exit velocity rests around the 20th percentile and is home-run rate has pretty much single-handedly driven his avert-your-eyes ERA.
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Aaron Judge is turning out better than expectations, while Robert Gsellman is not.
My weekly column, the Deep League Report, focuses on deep AL-only leagues and deep NL-only leagues. I play in a deep AL-only keeper league myself with my BP colleague Mike Gianella, and I play in a deep NL-only keeper league, too, where eight of the 12 owners also belong to the AL-only league. I use these leagues to help inform my weekly column, seeing which players are available on the waiver wire or in free agency, which players are being dropped or reserved, which players are being picked up and how much FAAB is being spent on each player. Today I’ll take a look at how each of my teams are doing in these leagues.
Mike Napoli warms up and the Rangers stay hot. Chris Davis just kept hitting go-ahead homers until the Tigers were subdued. Mike Moustakas was the only decent item in the place for the Royals.
The Tuesday Takeaway
There were several components to the Texas Rangers lackluster start this season—a weak bullpen, an injured Adrian Beltre, an ordinary stretch of poor luck—but chief among them was a severely struggling offense. No part of that offense struggled more severely than Mike Napoli: with a sub-.200 TAv, he’s been the team’s weakest everyday player at the plate. That’s a generally unenviable place to be, of course, but it’s a specifically and remarkably unenviable place to be for a first baseman-slash-designated hitter. And Napoli’s been there all season.
The results of the blind BABIP test are in. How did you do? And what can we learn from your answers?
On Friday, many of you took the blind BABIP test. I gave you 18 GIFs, in nine sets of two, each set comprising two batted balls. One was a hit. The other was an out. You guessed which was which, but you couldn’t see the outcome; the GIFs cut off at the frame just as contact was made, or just before contact was made. This was supposed to tell us something. I’ll get to the big result first: We’re the worst at this!
I tallied 82 full sets of answers, which is 738 individual guesses, of which 387 were correct. That is 52 percent correct. Closing our eyes and pointing would theoretically have earned us 369 correct answers. All the wisdom of the 82 of you was worth 18 extra correct answers. So that's the big thing first.
In order to become more right, we have to be wrong. But we also have to acknowledge that we're wrong and attempt to figure out why.
Believe it or not, most of our writers didn't enter the world sporting an @baseballprospectus.com address; with a few exceptions, they started out somewhere else. In an effort to up your reading pleasure while tipping our caps to some of the most illuminating work being done elsewhere on the internet, we'll be yielding the stage once a week to the best and brightest baseball writers, researchers and thinkers from outside of the BP umbrella. If you'd like to nominate a guest contributor (including yourself), please drop us a line.
How does a Google search for, say, "Dodgers" differ between the US and the non-baseball world?
Our view of the world is a bit skewed, especially as Baseball Prospectus readers. For us, it's baseball here, baseball there—we probably even see baseballs in scoops of ice cream or in sunrises. A close encounter of the third kind would likely bring us face to face with Babe Ruth or Harmon Killebrew as we carve Yankee Stadium out of a pile of mashed potatoes. A day without baseball is a day wasted.
But ours is a limited view of the real world. There are whole countries and whole continents who couldn't care less about the infield-fly rule or if Mariano Rivera has gone back to wearing high socks. It's a sad world, yes, but it's a world that exists nonetheless.
In case you missed Mike Fast's extraordinary research into quantifying the heretofore hidden contributions of catchers, we're moving it back to the top of the list for the weekend.
I Was Framed Catchers play a central role in the game of baseball through their involvement with every pitch that their pitchers throw. One of their key tasks is receiving borderline pitches without discouraging the umpire from calling strikes.
To pitch well in save situations, or to not pitch well in non-save situations? That is not the question.
For the past several years, the perception that closers perform poorly in non-save situations has increased. These relief aces fail to look particularly sharp unless they're under pressure and have the game's fate in their hands. Our own experiences have helped fuel this idea; we've all been witness to an untouchable pitcher entering a game with a 3-0 deficit and allowing a few more runs to score while pitching an ineffective inning. Unfortunately, with the memory of these negative events in mind, a categorical bias emerges where every example only provides further evidence of closer ineptitude when the game is not on the line. Is this strictly a categorical bias, or are the results and discrepancies in data between save situations and non-save situations real and significant?
Do teams that went without rookies for extended periods of time have something to tell about organizational behavior?
I attend perhaps two baseball games a month during the regular season. I really ought to go to more, because a lot of my column topics come when I'm sharing a couple of beers with a friend and exchanging ideas, enjoying the leisurely pace of live baseball without the distractions of TV or the net. On Tuesday night, I took in the Sox-Royals game with Josh Orenstein of the MLBPA, and one of the subjects that came up was how long a team can conceivably go without developing a rookie.
The effects of last week's appeals-court ruling could go beyond MLB.
No, what transpired last week has to do with test results from 2003 and a United States appeals court. It has to do with the BALCO investigation. It has to do with the MLBPA. It also has to do with the Fourth Amendment and the Bill of Rights. The events of last week could create case law that impacts far more than professional sports and the use of illegal performance-enhancing drugs. If this ruling is upheld, it has the capacity to determine what is permissible as reasonable search-and-seizure in the electronic domain.
Last year at this time, when we were first unveiling PECOTA, I was besieged with questions about the system's accuracy. From the very start, the system has always had its believers and its skeptics; all of them wanted to know whether the damn thing worked. My evasive answers to these questions must surely have seemed like a transparent bit of spin doctoring. One of my readers suggested to me, quite seriously, that I had a future in PR or politics. But I was convinced--and remain convinced--that a forecasting system should not be judged by its results alone. The method, too, is important, and PECOTA's methodology is sound. It presents information in a way that other systems don't, explicitly providing an error range for each of its forecasts--which, importantly, can differ for different types of players (rookies, for example, have a larger forecast range than veterans). Its mechanism of using comparable players to generate its predictions is, I think, a highly intuitive way to go about forecasting. Besides, all of the BP guys seemed to appreciate the system, and getting the bunch of us to agree on much of anything is an accomplishment in and of itself. Now that it has a season under its belt, however, we can do the good and proper thing and compare PECOTA against its competition.
Last year at this time, when we were first unveiling PECOTA, I was besieged with questions about the system's accuracy. From the very start, the system has always had its believers and its skeptics; all of them wanted to know whether the damn thing worked.
My evasive answers to these questions must surely have seemed like a transparent bit of spin doctoring. One of my readers suggested to me, quite seriously, that I had a future in PR or politics. But I was convinced--and remain convinced--that a forecasting system should not be judged by its results alone. The method, too, is important, and PECOTA's methodology is sound. It presents information in a way that other systems don't, explicitly providing an error range for each of its forecasts--which, importantly, can differ for different types of players (rookies, for example, have a larger forecast range than veterans). Its mechanism of using comparable players to generate its predictions is, I think, a highly intuitive way to go about forecasting. Besides, all of the BP guys seemed to appreciate the system, and getting the bunch of us to agree on much of anything is an accomplishment in and of itself.
Since the chat session, I've received dozens of e-mails asking for additional details. The response from BP readers finally
prodded me into finishing some related projects I'd had in progress for a while, which I'll present later in this article.