Trade Analysis: Kelly Johnson for Stephen Drew

In a very unprecedented turn of events, the New York Yankees struck a deal with the rival Boston Red Sox today, agreeing to swap infielders Stephen Drew and Kelly Johnson. This marked the first time the two teams agreed to a deal since 1997, when when the Bombers acquired Mike Stanley and Randy Brown for Jim Mecir and Tony Armas. Both Drew and Johnson are signed to one year deals, so this is essentially an exchange of rentals.
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Trade Analysis: Peter O’Brien for Martin Prado

The Yankees solved third base problem two weeks ago when they acquired Chase Headley from the San Diego Padres, but that didn’t stop them from bringing another third-sacker on board. In the waning moments before the non-waiver trade deadline, Brian Cashman and co. swung a deal for Arizona Diamondbacks third baseman Martin Prado.  Prado’s current contract runs through the 2016 season, so while he’ll help this year’s club, he’s also something of a long-term asset. In exchange for Prado, the Yankees sent minor leaguer Peter O’Brien to the Snakes.

Prado’s performed slightly below his usual standards this season, but has still been a productive player. Per Fangraphs, he’s accumulated 1.1 WAR so far this season — more than any Yankee position player not named Jacoby Ellsbury or Brett Gardner. Plus, Prado’s 111 wRC+ from 2012-2013 easily dwarfs his 89 wRC+ from this year, so you have to figure some upward regression will be in order.  Continue reading

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The right field safety net

For the second season in a row, Ichiro‘s depleted skills have been exposed with extended playing time. This isn’t to anyone’s surprise, as Ichiro is well past his prime at 40 years of age. He wasn’t supposed to be a regular this season with the Opening Day roster consisting of four outfielders ahead of him on the depth chart. However, Alfonso Soriano struggled to the point of his release and Carlos Beltran‘s health has been an issue, primarily relegating him to designated hitter in recent weeks.

By the end May, Ichiro had a 112 wRC+ primarily as a spot starter. Since then, coinciding with an increase in playing time, he has a 56 wRC+. Simply put, Ichiro cannot be trotted out as a regular any longer, which really has been the case for some time. Hopefully, Beltran can resume as the everyday right fielder, returning Ichiro to a bench role. Counting on him staying healthy is risky, so it would be wise for the Yankees to look elsewhere for outfield help. Continue reading

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Using Triple-A Stats to Predict Future Performance

Over the last couple of weeks, I’ve been looking into how a players’ stats, age, and prospect status can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there. This hypothesis may be less true for players at the Triple-A level since such a high proportion of these players make it to the majors, but I still think it provides some insight. To address this issue, In the future, I plan to engineer an alternative methodology that takes into account how a player performs in the majors, rather than his just getting there. Continue reading

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Does Aaron Judge’s height put him at risk for striking out too much?

Aaron Judge, the Yankees’ 32nd overall pick from last year’s draft, is having an excellent season at the plate. Through 97 games between two levels of A-ball, the 22-year-old Fresno State product is hitting .320 with 13 dingers. Based on his 6’7” frame, many scouts peg Judge to be a perennial 40 home run threat down the road as long as he’s able to make consistent contact. That seems to be a common narrative for tall hitters. They have plenty of physical strength, but their long arms can lead to long swings, making it difficult to get around on inside pitches.

Up to this point, making contact hasn’t been much of an issue for Judge, who’s struck out a manageable 22%. Keith Law recently noted that Judge has “a surprisingly short path to the ball for a guy his size.” That sounds encouraging enough, but the “for his size” piece might be something worth worrying about. After all, very few players in Major League history have stood as tall as Judge. Of the 8,559 position players who have played in the majors since 1901, just 11 have matched or eclipsed Judge’s height, and just 113 more reached the 6’5″ mark. Continue reading

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David Huff: Diamond in the Rough?

Plenty has gone awry for the Yankees this season. A parade of injuries to their starting rotation and a few underwhelming performances on the hitting side have lead to a somewhat disappointing 53-48 record, leaving them three games off the pace in the AL East. Yet for all of the team’s shortcomings, the Yankee bullpen has been one of the few bright spots. David Robertson has stepped seamlessly into the closer role, while failed starters Dellin Betances and Adam Warren have turned into excellent setup men seemingly overnight. Throw in effective veterans Shawn Kelley and Matt Thornton, and the Yankees have a bullpen that easily runs five deep — a luxury that few teams have at their disposal. Beyond that front five is lefty David Huff, who the Yankees plucked off of waivers from the Giants last month, and owns a tidy 2.18 ERA over 20.2 innings since joining the Yankees. Continue reading

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Using Double-A Stats to Predict Future Performance

Over the last couple of weeks, I’ve been looking into how a players’ stats, age, and prospect status can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there.

Things that were predictive for players in low-A and high-A included age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America in the pre-season. However, a player’s walk rate was not significant in predicting a player’s ascension to the majors. Today, I’ll look into what KATOH has to say about players in double-A leagues. For those interested, here’s the R output based on all players with at least 400 plate appearances in a season in double-A from 1995-2010. Due to varying offensive environments in different years and leagues, all players’ stats were adjusted to reflect his league’s average for that year.

AA Output Unlike in the A-ball iterations of KATOH, a player’s double-A walk rate is predictive — albeit only slightly — of whether or not he’ll make it to the show. While walk rate is statistically significant, it still matters much less than the other stats: It takes 3 or 4 percentage points on a player’s walk rate to match what 1 percentage point of strikeout rate does to a a player’s MLB probability.

This version is also different in that there are a couple of significant interaction termssignified by the last two coefficients in the above output. The “I(Age^2)” term adds a little bit of nuance into how a players’ age can predict his future success. While the “ISO:BA.Top.100.Prospect” term basically says that if you’re a top 100 prospect, hitting power is slightly less important than it would be otherwise. Hitting for power and making Baseball America’s top 100 list both make a player much more likely to make it to the majors, but if he does both, he’s a tad less likely to make it than his power output and prospect status would suggest independently. Put another way, a few top 100 prospects hit for power in double-A, but never cracked the majors — such as Jason Stokes (.241 ISO), Nick Weglarz (.204 ISO) and Eric Duncan (.173 ISO). But virtually all of the low-power guys made it, including Elvis Andrus (.073 ISO), Luis Castillo (.076 ISO), and Carl Crawford (.078). For non-top 100 guys, many more punchless hitters topped out in double-A and triple-A.

By clicking here, you can see what KATOH spits out for all current prospects who logged at least 250 PA’s in double-A as of July 7th, as well as a few that fell short of the cutoff — most notably Joey Gallo, Kevin Plawecki, and Rob Refsnyder. Topping the list is Mookie Betts with a probability of 99.95%, and of course the prophesy was fulfilled when the Red Sox called up the 21-year-old last month. Here’s an excerpt of the top players from double-A this year:

Player Organization Age MLB Probability
Mookie Betts BOS 21 100%
Francisco Lindor CLE 20 100%
Gary Sanchez NYY 21 99%
Austin Hedges SDP 21 99%
Alen Hanson PIT 21 99%
Jorge Bonifacio KCR 21 98%
Blake Swihart BOS 22 98%
Kris Bryant CHC 22 93%
Ketel Marte SEA 20 91%
Rangel Ravelo CHW 22 90%
Rob Refsnyder NYY 23 86%
Jake Lamb ARI 23 85%
Jake Hager TBR 21 84%
Darnell Sweeney LAD 23 83%
Joey Gallo TEX 20 82%
Preston Tucker HOU 23 81%
Scott Schebler LAD 23 79%
Kevin Plawecki NYM 23 79%
Cheslor Cuthbert KCR 21 78%
Kyle Kubitza ATL 23 77%
Michael Taylor WSN 23 76%
Christian Walker BAL 23 76%
Ryan Brett TBR 22 75%

Keep an eye out for the next installment, which will dive into what KATOH says about hitters at the triple-A level.  

Statistics courtesy of Fangraphs, Baseball-Reference, and The Baseball Cube; Pre-season prospect lists courtesy of Baseball America.

This article originally appeared on Fangraphs.

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Trade Analysis: Yankees acquire another rebound candidate in Headley

Chase Headley, perhaps the most mentioned name in trade rumors in recent years, is the latest Yankee acquisition in this summer’s trade season. In exchange for a good but not great pitching prospect (Rafael de Paula) and a winter scrap heap pickup (Yangervis Solarte), the Yankees bolstered their outlook at the hot corner for the next couple of months.

Pre-trade, Headley’s rest of season ZiPS projection stood at .247/.328/.395 (.723 OPS, .321 wOBA). After the deal, accounting for park and league adjustments, the forecast is up to .253/.331/.426 (.757 OPS). That’s not a big jump in on-base ability, but a nice boost in pop. The other internal candidates projected OPS for the remainder of the season? Certainly worse:

Name ZiPS RoS OPS
Kelly Johnson .713
Scott Sizemore .700
Yangervis Solarte .690
Zelous Wheeler .679

Headley, 30, is an upgrade defensively at third, too. You don’t need any advanced metrics to see that the Yankees’ infield defense has been pitiful. Yankees’ scouts have Headley as an average defender, while advanced metrics peg him to be above average at the position. Either way, it’s an upgrade. Continue reading

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Using High-A Stats to Predict Future Performance

Last week, I looked into how a player’s low-A stats — along with his age and prospect status at the time — can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there. Continue reading

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Using Low-A Stats to Predict Future Performance

In a piece I wrote a couple of weeks ago, I used historical minor league stats to to construct a model that predicts how likely it is that a teenager in A-ball will make it to the major leagues. While this method produced some interesting results, it also had some flaws, most notably that it didn’t take scouting or defense into account. This basically meant that a great defensive player — or a raw, toolsy player — could easily get an undeserving low rating if he had a poor year at the plate. Another drawback was that it only applied to teenaged players in low-A, who represent a pretty small portion of players at the level, and just a sliver of the prospect population. 

With these shortcomings in mind, I’ve taken another stab at predicting which players from the South Atlantic and Midwest leagues are most and least likely to make it to the show. Like last time, I ran a probit regression, which tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. But instead of limiting my analysis to players under the age of 20, I considered all players and included age as a variable in my model. I also attempted to quantify scouting by taking into account whether or not a player made Baseball America’s pre-season prospect rankings. The model still relies heavily on offensive performance, but isn’t entirely guilty of “scouting the stat line.” Continue reading

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