Jacob Lindgren’s a future star reliever… and the future may be now

Jacob Lindgren, this year’s second round draft pick, has been about as unhittable as it gets in his first two months as a professional. After agreeing to a contract on June 14th, the 21-year-old lefty reliever has breezed through four levels of the minors, posting a 0.96 ERA and striking out 51% of opposing hitters along the way. In addition to his on-field performance, all scouts agree that his stuff – especially his slider — is absolutely filthy. All things considered, Lindgren would almost certainly be better than both of the team’s current lefties, David Huff and Rich Hill, meaning there’s a very good chance he’ll be summoned to the majors in the next couple of weeks.

Lindgren’s minor league numbers have been great, but pitching in the big leagues is an entirely different animal. The hitters are more discerning at the plate and are much more likely to capitalize on mistake pitches. As a result, dominant minor league numbers don’t always carry over to the big leagues. Take Dan Runzler, for example, who was my top comp for Lindgren when he was drafted based on PITCHf/x and scouting reports. Runzler put up a filthy 0.76 ERA and 36% K% in the minors in 2009, but went on to throw just 72 innings in the majors before being exiled to Japan last month. Continue reading

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Is there any hope for Carlos Beltran’s bat?

Substandard would be an appropriate description of Carlos Beltran‘s 2014 campaign. He was added to be the lineup’s bopper after Robinson Cano‘s departure, but Beltran’s failed to live up to his reputation, batting .235/.292/.420 (94 wRC+) in 370 plate appearances. Age and health are the most obvious factors to assign blame, but is there anything behind the numbers that hint at improvement? Continue reading

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Robinson Cano and the difficulty of replacing a star player

A few days ago, I saw this tweet:

Jacoby Ellsbury, Brian McCann, Carlos Beltran: 4.1 WAR, $53M Robinson Cano: 5.1 WAR, $24M Not second guessing, just saying.

— Dan M. (@LeftFieldDan) August 14, 2014

When Cano left for the Mariners, the Yankees had the daunting task of replacing a five-to-seven win player in 2014. Frankly, this was an impossible task given the fact that Cano is the best second baseman in baseball. Instead, the Yankees reallocated money to other positions of need to make up for Cano’s vacancy.

According to Dan Szymborski’s preseason ZiPS, Cano was projected to amass 5 WAR, while the new trio of new Yankees forecasted for 9 WAR (Ellsbury 4, McCann 3, Beltran 2). In total, it looked like the Yankees would come out ahead immediately while limiting long-term risk. Instead, Ellsbury has been the lone guy carrying his weight. Both Beltran and McCann have underperformed, while the former has also dealt with some health issues. Continue reading

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Don’t worry (too much) about the Yankees bullpen

The Yankees bullpen has really scuffled of late. For the second time in two games, the bullpen cost them the game last night, when Dellin Betances and Shawn Kelley yielded four runs in the bottom of the eighth, turning a 3-1 lead into a 5-3 deficit. Monday night, it was Adam Warren who coughed up three runs in what was a one-run game. Even closer David hasn’t seemed himself lately, and has now walked five batters in his last five outings. Up until recently, the Yankees bullpen had been arguably the team’s biggest strength, but in recent weeks, the wheels have begun to fall off the wagon. Rough outings are to be expected – and have been received — from the guys like Chase Whitley, Matt Daley, David Huff, and Rich Hill, who sit at the bottom of the bullpen totem pole. But the top dogs have also started to struggle. Over the last 20 days, Robertson, Betances, Warren, and Kelley have all averaged a FIP below 4.00. That’s pretty bad, especially for a reliever: The American League average for reliever FIP is 3.63.

Bullpen

As good as the Yankees ‘pen has been this year, its not like its been completely smooth sailing. Betances got off to somewhat of a shaky start, Warren and Robertson both went through rough patches in May and June, and Kelley’s been all over the place. But this is the first time all season that all four have struggled at the same time. This turn of events is particularly troubling as the Yankees have reached a point in their season where there’s little margin for error. In order to make the playoffs, they’ll need just about everything to go their way from here on out, including the performance of their key bullpen pieces, especially following Matt Thornton‘s departure, which has left them without a usable lefty reliever on the roster.

There’s no denying that the Yankees bullpen has performed poorly over the past few weeks, but its more likely to be a fluke than anything else. A reliever only throws around 10-15 innings a month, which isn’t nearly enough to make any definitive conclusions about a player’s abilities. On top of that, there’s no reason to believe these pitchers’ struggles are in any way related. Its not like Adam Warren‘s ineffectiveness somehow caused Robertson to start struggling as well.

Sure, there’s a chance that any one of these pitchers’ slumps will turn out to be more than a blip on the radar. Its not hard to imagine Betances or Warren hitting a wall given the exorbitant amount of innings they’ve thrown this season. But until we start to see something tangible — like a drop in velocity – its probably not worth getting worked up about. Robertson, Betances, Warren, and Kelley have all established themselves as viable bullpen options over the season’s first four months, and I expect nothing less from them over the final six weeks.

Statistics courtesy of Fangraphs.  

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What types of hitters have large platoon splits?

Big league teams today employ a myriad of data-driven strategies to eek every last drop of value from the players on their rosters. Many of these strategies consist of matching up hitters and pitchers based on their handedness. Between lineup platoons and highly-specialized bullpens, managers today go to great lengths to ensure they’re putting their players in the best possible situation to succeed.

It’s easy to see why. With very few exceptions, Major League hitters hit much better against opposite-handed pitching. In terms of wOBA (vs. opposite-handed – vs. same-handed), lefties perform about .031 better against righties, while righties hit .043 better against lefties. Yet not all platoon splits are created equal. Players like Shin-Soo Choo, David Wright, and Jonny Gomes are notorious for their drastic splits, while others put up comparable numbers no matter who’s on the mound. Ichiro Suzuki and Alex Rodriguez are a couple of the no-platoon-split poster boys.

Ok, so some batters have bigger platoon splits than others, but is there any particular reason for this? Take Choo for example. Is there something inherent to his skill set or approach that causes him to struggle against  lefties?

Hoping to find an answer, I ran some regressions in search of attributes that might make a player more likely to have an exaggerated platoon split. I tested all sorts of things out there — from walk rate and swing% to a player’s height and throwing arm — but didn’t come away with much. Aside from a hitter’s handedness, attributes that proved statistically significant included: a hitter’s overall wOBA, his line drive rate, his strikeout rate, and his contact rate on pitches out of the zone, but even those relationships are extremely weak. It takes .100 points on a batter’s wOBA, or a 10% increase in K% or LD%, to move a batter’s platoon split  by just .010 points. This tells us something, but not a ton, and at the end of the day, these variables account for a nearly negligible 4% of the variation in hitters’ platoon splits. Here’s the resulting R output. My sample included all batter seasons from 2007-2013 with at least 100 plate appearances against both lefties and righties, excluding switch hitters:

Platoons


Good hitters or guys who strikeout frequently might be a little more prone to having large platoon splits. But for all practical purposes, a player’s ability to hit one type of pitching better than the other seems to be a skill that’s independent of all others. Aside from going by a player’s platoon stats, which can take years to become reliable, there’s little we can do to anticipate which hitters might fare particularly bad against same-handed pitching. And with the exception of players with long track records of unusual platoon splits — like Choo and Ichiro — it’s generally safe to assume that any given hitter’s true-talent platoon split is within shouting distance of the average: .043 for lefties and .031 for righties.

This article originally appeared on Fangraphs.

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What might the Yankees have in Bryan Mitchell

Brian Mitchell didn’t see any game action in his previous two stints with the Yankees, but the 23-year-old finally made his big league debut yesterday, throwing a scoreless eighth and ninth. Mitchell’s command seemed a little shaky, but his stuff was unquestionably good, enabling him to strikeout both Jason Kipnis and Michael Brantley. He primarily threw two pitches — a mid-90′s fastball and an impressive curve — which accounted for 28 of 29 (97%) of the pitches he threw. He also mixed in a token slider.

When Jose Ramirez, Chase Whitley, and Shane Greene debuted with the Yankees this year, I queried the PITCHf/x database in search of pitchers with a comparable arsenal if pitches and similar stuff. Today, I’m repeating this exercise for Mitchell. To find comps for Mitchell, I looked for players (1,000 pitch minimum) since 2008 who threw either a fastball or a curve at least 80% of the time. I turned to PITCHF/x to find out how often these pitcher’s pitches fell within the minimum and maximum values for Mitchell’s velocity, break angle, break length, and spin from yesterday’s game. These are the pitchers who threw the highest ratio of pitches comparable to what Mitchell threw. Continue reading

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Applying KATOH to historical prospects

Over the last few weeks, I have written a series of posts looking into how a player’s stats, age, and prospect status can be used to predict whether he’ll ever play in the majors. I analyzed hitters in Rookie leagues, Short-Season A, Low-A, High-A, Double-A, and Triple-A using 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.

After receiving a few requests, I decided to apply the model to players of years past. In what follows, I dive into what KATOH would have said about recent top prospects, look at the highest KATOH scores of the last 20 years, and highlight some instances where KATOH missed the boat on a prospect. If you’re feeling really ambitious, here’s a giant google doc of KATOH scores for all 40,051 player seasons since 1995 ( minimum 100 plate appearances in a short-season league or 200 in a full-season ball). Continue reading

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Using Short-Season A Stats to Predict Future Performance

Over the last couple of weeks, I’ve been looking into how a player’s stats, age, and prospect status can be used to predict whether he’ll ever play in the majors. So far, I’ve analyzed hitters in Rookie leaguesLow-AHigh-ADouble-A, and Triple-A using 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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Why did the Yankees keep Derek Jeter at shortstop?

It’s no secret that Derek Jeter‘s not much of a defender these days. The wear and tear from playing shortstop everyday for the last 20+ years has done a number on the 40-year-old’s mobility. And while he fields just about everything hit directly at him, his glacial range makes him one of the worst defensive shortstops in baseball according to advanced fielding metrics. A little more than one in every six balls in play is hit to the shortstop’s part of the field, making it one of the worst places to hide someone with such limited mobility. But Jeter continues to man shortstop everyday because… well because he’s Derek Jeter. And some would argue that moving him off of his signature position would be tantamount to a slap in the face, especially during his farewell tour.
Continue reading

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Using Rookie League 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. In the future, I plan to engineer an alternative methodology to go along with this one, that takes into account how a player performs in the majors, rather than his just getting there.

For hitters in Low-A and High-A, age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America all played a role in forecasting future success. And walk rate, while not predictive for players in A-ball, added a little bit to the model foDouble-A and Triple-A hitters. Today, I’ll look into what KATOH has to say about players in Rookie leagues. Due to varying offensive environments in different years and leagues, all players’ stats were adjusted to reflect his league’s average for that year. For those interested, here’s the R output based on all players with at least 200 plate appearances in a season in Rookie ball from 1995-2007. Continue reading

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