Author Archive

Japanese and Korean Prospects in Context

The MLB should be receiving a new crop of far eastern talent in the next year or two, which means it is worth reacquainting ourselves with the standouts and talent levels of these leagues — specifically the Korean Baseball Organization (KBO) and Japan’s Nippon Pro Baseball league (NPB).

Let’s start with Korea and Pittsburgh’s potential new infielder, Jeong-ho Kang:

KBO Hitters

NOTE: I am using wOBA+ here, which is merely my shorthand for wRC+ without park factors and using MLB linear weights. So the numbers are not perfect, but they’re better than OPS or OPS+.

The KBO is a hitter’s league. That is the refrain we hear most often whenever Kang’s surfaces. He slashed a filthy .356/.459/.739 with 40 homers in 2014, and his career stats suggest he’s a middle infielder who hits like he’s from the corner. But how good is he with respect to the league?

Answer: He’s the best. But a wide margin.

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The Thinning Catcher Market

The Phillies re-signed Carlos Ruiz to a 3-year, $26 million deal. Also: Brayan Pena and Geovany Soto have locked down their 2014 teams (the Royals Reds and Rangers respectively). And now it appears Jose Molina is in the final stages of returning to St. Pete for another two years of expertly framed and eh, who cares about blocking? pitches.

So where does that leave the catching market? As far as I have seen, the Yankees, Red Sox, Rockies, Angels, Rangers (still), Blue Jays and Twins have all been connected with free agent catchers on MLBTR. Using their handy free agents leaderboards (with a few additions), we can examine the remaining free agent catchers and try our hand at predicting the right fits for each.
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How David DeJesus Gained a Platoon Split

From David DeJesus‘ 2003 debut through his 2010 season, the versatile outfielder hit 90 wRC+ (1175 PA) against left-handed pitchers. Since then, DeJesus has mustered a lulzwut 29 wRC+ (327 PA) against lefties. Despite his one-sided floundering in the past three seasons, DeJesus managed to procure a three-year contract extension from the Rays.

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A Method for Examining Two-Strike Hitting

Let’s talk about why I love Jamey Carroll. He has had — like most of us would like — his best years after the age of 30; he has played every position except catcher, including an inning of scoreless relief in 2013; he’s short; he spells his name humorously; and he plays a cop in this music video (therabouts of 1:10).

But what impresses me most about him is his rare combination of no power and great plate discipline (as seen here here). There is almost no threat of a homer and only a mild threat of a double when he walks to the plate, but he still induces a walk rate near 10%. Carroll walks more than Robinson Cano and Adrian Gonzalez not because pitchers fear him, but because — as anyone who’s watched Carroll can attest — the 5-foot-11 infielder fights off a half-dozen bad pitches until he finds one he can pop for a single.

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Pinch Hitting Report Card: Reds Pass, Orioles Fail

Monday night, Rays manager Joe Maddon pinch hit James Loney for right-handed Sean Rodriguez. After a foul knubber to the right, Loney went all walk-off on Tommy Hunter.

But as much as pinch-hit walk-off home runs are the soup of Hollywood executives, they are the rarest of meats in the MLB reality. In fact, pinch hitting is most often a choice between lesser evils — a choice between a bad wOBA or a terrible wOBA.

A closer look at the last five seasons of pinch hitting reveals success has not between distributed evenly, and the effectiveness of of some pinch-hitting efforts may be a product of systematic choices rather just tough breaks.
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Lineup Optimization and Multi-Run Homers

Why do some teams hit multi-run homers while other teams struggle? The relationship is not as simple as: better OBP, better rate of multi-run homers. I recently dug through sevens season of WPA logs and determined the baseball gods are not totally logical.

Observing the variation is one thing, but to ascribe it all to purely noise is another. Teams can control their runs per home run rate through constructing rosters and lineups predisposed towards greater home run efficiency. So we can’t consign variations to the random luck spittoon until we’ve more specifically assessed what’s happening in the lineup.

In the previous article, I briefly outlined what I called the Giancarlo Problem — where a team’s best OBP and best HR-rate are located within the same player. The Giancarlo Problem can result in deceiving team-wide statistics. So in this second venture, we are going to examine three dimensions instead of two: 1) OBP, excluding home runs, 2) home run rates, and 3) lineup positions.
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The Determinants of Multi-Run Homers

Let's get digging.

This investigation begins with a simple frustration. I was recently watching the Rays and, after a few solo homers wandered over the fence, I asked myself, “How can a team with such a solid on-base percentage hit so few multi-run homers?”

It makes sense that, if’n a team can matriculate men down to first and second and even third base, they can get more bang for their homer bucks. My frustration reminded me of Jeff Sullivan’s frustrations in 2012, when he wrote the epic monkey’s paw game recap, wherein he bemoaned the Mariners’ solo homeritis.

But, to me, it made sense the Mariners had solo homeritis. The 2012 Mariners had a .296 OBP — worst in the majors by a Deadball Era or two.

So I began a quest, a quest that has lasted several months. I have scaled SQL cliffs, journeyed deep into Pivot Table mines, and waded through the blogger depression swamps. With the support of some eclectic friends, such as Jeff Zimmerman, Matt Hunter, and Steve Staudenmayer, I have concluded that OBP and runs per home run do indeed have a relationship, but that relationship is severely diluted by randomness and unpredictability.
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The Marlins Offense Cannot, Does Not Hit

I would say we are watching history, but the “we” who is actually watching the Marlins has to be limited to just about the 50 players present at any game, the managers, the broadcasters, and the odd Florida resident who fell asleep during the SunSports “Inside the Rays” special on Sam Fuld and then awoke to find a Marlins game on television.

The Marlins offense is bad. It is very bad. If you want to hear about the redeeming elements of the Marlins offense, this article may not be much help. Yes, Giancarlo Stanton is to home runs what Moses is to water-spewing rocks — he hits them — but the remainder of their eclectic crew of rushed prospects and aged veterans has offered little praiseworthy bat-action.

And if the situation deteriorates even a little, if their narrow balance of awful totters or teeters just a bit worse, this offense has a chance to engrave its poor results in the most inglorious stone of history: Worst offense of modern times.
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The Curious Case of Junior Lake

Just 23 years old, Junior Lake has made a heart-shaped impression on Cubs fans. Search his name on Twitter, and you will find a nation of Cubs fans eager to see him play in the 2014 All-Star game and receive his due credit in morning talk shows. With the team holding baseball’s version of a mid-season yard sale, Lake’s heroics have come at a time when the club most needed something fun to watch.

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The Changing Effects of Petco Park

Jeff Sullivan’s recent enjoyable trot through San Diego Padres statistics and history led to a number of commentors thinking about San Diego’s park factors. The Padres changed the outfield dimensions of Petco Park in the off-season, and since park factors are backwards looking and rely on multiple years of data, changing dimensions can throw a bit of a monkey wrench into the calculations. So, it’s possible that our park factors are now somewhat behind the times, and we need to keep this in mind when looking at the park adjusted numbers (such as wRC+, ERA-/FIP-/xFIP-, WAR, etc…) for San Diego players, both hitters and pitchers.

It’s not quite so simple as noting that the changing dimensions have made the old park factors useless, however. Moving in the fences helps home runs, yes. This is undeniable. But it also can decrease triples and doubles, as well as effect the more odd elements of park factors, such as walk-rates, strikeout rates and pop-up rates.

It’s too early in the season to construct terribly useful park factors for the new dimensions, but we can do some harmless back-of-the-napkin mathematics to at least determine if the recent numbers suggest at least the early signs of serious run environment changes.
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