Yesterday, I share an updated version of Alex Chamberlain’s pitcher xBB% equation and used it to identify the fantasy relevant pitchers whose walk rates should improve this season. Today, I’ll check in on the other side of the coin, those starting pitchers whose xBB% was well above their actual BB% in 2016. This group will find it challenging to fend off the regression monster this year without throwing more strikes.
Introduction
Before I came to Rotographs – I wrote a lot on my own site, and in the FanGraphs community section. My first foray into baseball analysis was developing a metric to try and quantify “Stuff”. A New York Times article by John Branch in October 2015 discussed the elusive definition of the pitching term “stuff”. Talk of “plus stuff” and feelings of “all the stuff being there” was scattered throughout the article.
Despite interesting commentary discussing the ability for pitchers to over-power hitters, there was no true definition of the nastiness of a pitcher’s stuff. My favourite quote from the article is that stuff is “both meaningful and meaningless. There are no synonyms. Like pornography, stuff is defined mostly by example. An only pitchers have stuff. Hitters do not have stuff (Branch, 2015)”.
About three and a half years ago, I shared the bestest starting pitcher xBB% formula yet. Since I mentioned to you recently that I have been on an xEquation binge, I updated that bestest xBB% one too, of course. But as I was working on it with an additional variable, I realized that Alex Chamberlain had literally done the exact same thing about two years ago. That same thing was adding the 3-0% metric from Baseball-Reference.com, which is the percentage of plate appearances in which a 3-0 count is seen. So rather than take credit for developing a better version of my original xBB% metric, I’m now simply updating the coefficients of Alex’s equation.
Robbie Ray is already shaping up to be one of 2017’s most contentious starting pitchers headed into draft day. (This isn’t even my first time writing about him in the last half-year.) His 28-percent strikeout rate (K%) and 3.45 xFIP scream of an elite starter, but his 4.90 ERA and 1.47 WHIP, sustained during more than 170 innings pitched, seem to say otherwise.
Analysts and laymen who have expressed optimism about Ray have done so in regard to his alleged hittability. That 1.47 WHIP didn’t come from nowhere: his .352 batting average on balls in play (BABIP) got him there. You’ll hear a variety of arguments: he struggles on his third time through the zone; he lacks a quality third, or maybe even second, pitch; and so on. I’m not here to argue the validity of those sentiments.
I want to talk exclusively about Ray’s BABIP. Well, his sinker, too. And maybe even his strand rate (LOB%)… But mostly his BABIP. Please, have a seat. I don’t want to fluster you.
Ray’s .352 BABIP in 2016 was the second-worst of the last 15 years. That’s out of 1,281 individual player-seasons posted by qualified starting pitchers. His BABIP was historically bad — strange, you’d think, for a pitcher who has quickly demonstrated a lot of promise. So, I want to approach this whole BABIP thing in a vacuum. Let’s just look at the facts — not even alternative facts, but real facts!
Over the course of the last few weeks of the regular season last year, I had explored different ethical and strategic questions posed to me via email and social media. It was a fun series to write and while some definitely did not like me or my advice, others loved it. So, I am hoping to make this a reoccurring series that will pop up periodically throughout 2017. Feel free to send me more questions at JustinMasonFantasy@gmail.com or on twitter @JustinMasonFWFB and when I have enough, I will do another installment. Thanks for playing along! Read the rest of this entry »
This season, I was lucky enough for a couple print publications, Lindy’s and The Fantasy Baseball Guide, asked me to contribute their fantasy preview magazines. While the quality of both magazines is top notch, print publications have limited room for explanations and no ability for back-and-forth discussions. Today, I am going to go over my contributions which I feel could use more explanation and will answer any questions on my thought process.
Lindy’s
For Lindy’s, I participated in their 12-team mock draft ( standard team except 1 C, 4 OF, 8P) and I picked out of the 3rd position. Here is my team
Position – Name (Round Drafted)
C – Buster Posey (3)
1B – Hanley Ramirez (7)
2B – Rougned Odor (2)
3B – Adrian Beltre (4)
SS – Marcus Semien (12)
MI – Jung Ho Kang (17)
CI – Albert Pujols (10)
OF – Andrew McCutchen (5)
OF – Mark Trumbo (9)
OF – Marcell Ozuna (14)
OF – Matt Holliday (16)
Util – Mike Moustakas (18)
P – Clayton Kershaw (1)
P – Chris Archer (6)
P – Rich Hill (11)
P – James Paxton (13)
P – Michael Pineda (19)
P – Jharel Cotton (20)
P – Andrew Miller (8)
P – Shawn Kelley (15)
Around this time last year (edit: actually, it was more like sometime in 2014), Eno Sarris introduced the Arsenal Score. It was, and still is, a novel concept: for every pitcher, evaluate each of his pitches based strictly on their strikeout- and ground ball-inducing tendencies. Each pitch would be evaluated relative to its contemporaries — in other words, Corey Kluber’s slider would be compared to all other sliders in the league.
I’ll speak for Eno when I say the original Arsenal Scores weren’t meant to be especially rigorous. They received some flak for being mathematically inaccurate — to which I say, it doesn’t really matter. Originally, Eno calculated separate Z-scores for the ground ball rate (GB%) and swinging strike rate (SwStr%) — called “Z-BIP” and “Z-Whiff,” respectively, in the results to follow — of each pitch for every pitcher. The aggregate Z-scores — two Z-scores times X number of pitches — comprise the full Arsenal Score.
This time around, I propose a few tweaks:
My article today spawned after listening to The Sleeper and the Bust podcast when Paul Sporer interviewed NBFC’s Main Event champion Rob Silver. The entire podcast is a must listen, but one part sparked my interest. Rob mentioned he uses infield flyball rate plus strikeout rate minus walk rate to value pitchers (55:45 point). Silver successfully targeted Kevin Gausmann, Marco Estrada, and Rick Porcello late in his draft by using this stat combination. I will create the same filter to find 2017 sleepers.
There is no easier ball to catch than the infield fly. It’s an easy out. In those few instance when they errantly fall to the ground, a fantasy owner shouldn’t worry since the rest of the inning’s runs won’t count because of the error.
Besides being an easy out, a player’s infield fly rate stabilizes with just over a half season’s data. While infield flies don’t stabilize as fast as strikeouts, they do become stable within a season.
Infield fly rate (IFFB%), especially as we represent it here at Fangraphs, misleads the user. The IFFB% listed indicates the percentage of flyballs (FB%), not all batted balls, which are hit in the infield. To get the infield popup rate, the IFFB% must be multiplied by the FB%. The confusion doesn’t end yet.
Don’t you love it when a player’s name makes it super simple to create an absolutely brilliant title? I do! So yesterday, the Mariners continued their fantasy league moves by acquiring 27-year-old southpaw Drew Smyly. Up until 2016, Smyly enjoyed a fantastic beginning to his career, as he owned a 3.24 ERA/3.43 SIERA between the starting rotation and bullpen. But shoulder injuries hit in 2015 and he becaome afflicted with a severe bout of gopheritis during this past season. His ERA ballooned to 4.88 as his strikeout rate fell and he allowed the second highest fly ball rate in baseball among qualified pitchers. Now he moves to Seattle, where perhaps a change of scenery could do him some good. Will he benefit from the park switch? Let’s find out if such a possibility exists.
There are plenty of ways to characterize David Phelps‘ success in 2016. Among pitchers who threw at least 80 innings, he posted the 5th-best ERA (2.28), 6th-best xFIP (3.15) and 7th-best FIP (2.80). That’s a big deal, although it’s a decidedly smaller deal considering the bulk of Phelps’ innings came from the bullpen.
The reason for Phelps’ success — the cause to the effect, that is — is fairly obvious:
Phelps added more than 3 mph to his four-seamer and sinker as well as a tick or two to each of his off-speed pitches.* In an August edition of his NERD game scores, Carson Cistulli quipped, “As with most other pitchers, Phelps at 94-95 [mph] is markedly different than Phelps at 91.” Indeed, Cistulli. Phelps looked like a changed man.
It’s easy to attribute his sudden late-career success to his almost-full-time move to the bullpen. It’s how the narrative typically plays out: a pitcher’s velocity plays up better in short spurts. It’s why we expect failed starters can become elite relievers. It’s a cognitive bias, but it’s a bias we have because it tends to be true. This shorthanded logic, however, undersells Phelps’ gains both under the hood and on the mound.