The Diamondbacks entered this year with some legitimate excitement for their starting rotation – led by Robbie Ray, Taijuan Walker, a resurgent Patrick Corbin, and the breakout Zack Godley, they emerged as a rising force in the NL West. Since a nuclear April, almost all of that excitement has slipped away as one by one their big 4 have succumbed to injury: Taijuan Walker is down with Tommy John, Robbie Ray has yet to come back from a lat strain, and Zack Godley seems to have given back all of the gains he made last year, and then some.

Godley, led by a wipeout curveball and a sharp cutter, was universally anointed as a rising star both in fantasy circles and in real life. Two-plus months into the season, and he’s universally disappointed. His command has fallen apart, he’s getting hit hard, and even his xFIP isn’t saying he’s much better than he’s performed (5.12 ERA, 4.19 xFIP). I’m going to go ahead and bury the lede, as well as spoil my findings: I think this is going to get much worse before it gets better for Godley.

Graph dump!

Zack Godley Slugging Against

So what I’m noticing right off the bat is his cutter and sinker suddenly started getting hit this year. Like, legitimately getting smoked. He’s had spikes like that at other times in his career, but something like that definitely puts up some red flags.

2017 Cutter Usage vs LHH

2018 Cutter Usage vs LHH

2017 Cutter Swing% vs LHH

2018 Cutter Swing% vs LHH

2017 Cutter Whiff per Swing vs LHH

2018 Cutter Whiff per Swing vs LHH

Ouch. Godley’s almost completely lost his ability to get chases outside the zone to lefties on his cutter. He’s also leaving the pitch in the zone much more than in 2017, and it’s getting hit when he does. This feels like lefties are seeing the pitch better, or have adjusted. Let’s keep going to the sinker:

2017 Sinker Usage vs RHH

2018 Sinker Usage vs RHH

2017 Sinker Whiff per Swing vs RHH

2018 Sinker Whiff per Swing vs RHH

This is…bad. Godley’s lost the entire bottom of the zone, and completely abandoned his strategy to ride the pitch down and in on right handers. He can’t buy a whiff right now, and guys still aren’t biting when it’s away. He still has decent effectiveness on the pitch over the inside part of the plate, so to me that isn’t suggestive that the pitch lost its ability to tie guys up. He just can’t get it in there.

All of this brings me to this penultimate chart:

Godley Career Horizontal Release Point

Hooooooo boy. Godley’s seen an aggressive change in his release point outwards since the beginning of 2017. His splits in 2017 get worse as he floats more towards the sidearm – BABIP went from .236 to .316; BB% 7.8% in the first half to 8.9% second; HR/9 from .52 to 1.16; only his K% got better – 24.3% to 27.9%. Those rates have continued to move in the wrong direction to start 2018 too: K% – 21%, HR/9 – 1.76, BB% – 11.2%, BABIP – .316.

I’m not sure what this means. The data suggests his pitches are flattening out a little, and he’s having issues locating side-to-side. That tells me his arm is being dragged along and getting off to the side of his pitches, rather than being on time and getting on top of them. His velocity is down across the board 1-2 mph as well, which paired with his release point issues is a major red flag. A side effect of being farther out to the side is off-hand batters get a better look at what you’re throwing (side-armers tend to be on-hand specialists), which explains why lefties have effectively stopped swinging at cutters inside. It also explains why he can’t drive his sinker in on the hands of righties – if his arm is late he’s going to miss either off the plate outside or in the middle of the zone.

To me, and I’m assuming this release point change isn’t an intentional change, there’s likely an injury that he’s pitching through. I dug through news feeds and didn’t see anything mentioning injury or soreness, but I can almost guarantee something is wrong physically. I can’t tell you what’s wrong, but I can tell you this: unless he gets his arm on-time and back in line, his struggles will continue.

Let me preface this: I’m biased. I absolutely LOVE Luis Castillo. His ceiling is near-unmatched in the MLB. He’s got 4 pitches that have plus-to-plus plus upside, elite velo, and age on his side. That being said, he’s been truly atrocious so far in 2018. Allow me to dive into the numbers and graphs and play a little bit of doctor!

You may know me as the guy that does the dScore evaluations of players. While I haven’t done any so far this year, I’ve kept up with the analysis on my Google Doc and I’ll probably release one closer to the All-Star Break. One thing that I’ve noticed is, despite the putrid surface-level numbers (5.64 ERA, 1.45 WHIP) Castillo has consistently scored well on my metric. Not as well as last year when he was a certified stud, but he’s floated around the lower end of the #2 breakpoint (20+ points). This tells me that, purely based on his stuff, he’s getting pretty royally unlucky – or that something else is wonky. There’s been documented evidence that his velo is down from last year and that he had an issue getting his arm slot dialed in. His last month has been measurably better, showing regression towards last year’s outcomes (K% up from 18% to 25%, BB% down from 10% to 8%, BABIP normalized from .330 to .290). The two things that haven’t regressed are pretty key to this analysis: his hard contact has stayed abnormally high (38%) and he’s continuing to not generate ground balls at near the rate he was last year (45% vs 58%).

Here’s where the fun begins.

Last year, if you remember, part of the fun of Castillo was the fact that he learned two new pitches midseason that made his stock and performance explode like it did: the sinker and the slider. His sinker, in particular, was near miraculous due to how quickly it became a vital piece in his arsenal; and the slider was a groudball inducing, line drive avoiding monster. Guess what two pitches are thorns in his side so far this year? Now I’m not here to argue that these two pitches all of a sudden are bollocks and he should consider scrapping them. I’m here to argue that he’s simply struggling to harness two new, difficult pitches to locate consistently, and that simple issue is causing a snowball effect.

I took a look at Brooks, and outside of the noticeable early-season change in his arm angle, I didn’t anything that’s super out of the ordinary there. What was weird, though, was his sinker and slider have virtually stopped generating ground balls. His pitch mix is similar to last year as is his whiff percentages (actually his sinker’s gotten somewhat more whiffy), so it’s not just simply a change in pitch profile.

2018 GB per BIP

Here’s some relevant pitch-specific zone profile graphs:

2017 Sliders vs LHH

2018 Sliders vs LHH

2017 Sinkers vs RHH

2018 Sinkers vs RHH

I chose those profiles specifically. In English, Castillo has had serious problems leaving meatball sliders to lefties and consistently hitting the backdoor sinker to righties. Addressing the sinker, what this has done is allowed right handed hitters to forget about covering the outside part of the plate on fastballs and target anything in. He’s also running it into the barrel, and not really giving the sinker a chance to get pounded into the ground. That’s borne out in the ISO profiles for the pitch:

2017 Sinker ISO

2018 Sinker ISO

In terms of the slider, he’s eliminated its ability to tunnel off anything vs righties, as he’s been quite good at getting that slider down and away from them. Meatballing anything is bad — especially offspeed that needs to be buried down and in vs lefties. He’s consistently been dropping it right into lefties’ nitro zone, and because he’s somewhat lost confidence in his ability to execute a good slider vs offhand, he’s been using it less as the year goes on.

In general his pitch mix and locations haven’t really seen a large change from last year. His velocity being down across the board probably hasn’t helped much at all – although it’s slowly coming back as the season goes on, and I think Castillo is going to see ups and downs the rest of the year. He’s already shown the ability to consistently locate with those pitches last year so I’d take the bet that he’ll find it again. His swinging strikes, contact, and in-zone contact rates are all in the top 10 in the MLB among starting pitchers, which tells me when he hits his spots his stuff is absolutely still intact.

My take on him for fantasy is he’s a hold/buy. I don’t believe this is mechanical, injury-related, or his stuff backing up. This is all about him basically not having the feel for two specific locations of two specific pitches. I wonder how much of this is rooted in him missing most of spring training to the birth of his kid. He maybe never got a chance to iron out his mechanics, causing the arm slot issue. Maybe his arm slot issue caused him to lose feel/command of the sinker and slider, or they didn’t get the reps needed preseason. Whatever the reason he doesn’t have feel I’m confident he’ll figure it out. I’m also confident in his value 2019 and onward, so especially in a dynasty format I’d be looking to buy.

Earlier this season I set out to build a tool similar in nature to my dSCORE tool, except this one was meant to identify swing-change hitters. Along the course of its construction and early-alpha testing, it morphed into something different, and maybe something more useful. What I ended up with was a tool called cHit (“change Hit”, named for swing changers but really I was just too lazy to bother coming up with a more apt acronym for what the tool actually does). cHit, in its current beta form, aims to identify hitters that tend to profile for “impact production” — simply defined as hit balls hard, and hit them in the air. Other research has identified those as ideal for XBH, so I really didn’t need to reinvent the wheel. Although I’d really like to pull in Statcast data offerings in a more refined form of this tool, simple batted ball data offered here on FanGraphs does the trick nicely.

The inner workings of this tool takes six different data points (BB%, GB%, FB%, Hard%, Soft%, Spd), compares each individual player’s stat against a league midpoint for that stat, then buffs it using a multiplier that serves to normalize each stat based on its importance to ISO. I chose ISO as it’s a pretty clean catch-all for power output.

Now here’s the trick of this tool: it’s not going to identify “good” hitters from “bad” hitters. Quality sticks like Jean Segura, Dee Gordon, Cesar Hernandez, and others show up at the bottom of the results because their game doesn’t base itself on the long ball. They do just fine for themselves hitting softer liners or ground balls and using their legs for production. Frankly, chances are if a player at the bottom of the list has a high Speed component, they’ve got a decent chance of success despite a low cHit. Nuance needs to be accounted for by the user.

Here’s how I use it to identify swing-changers (and/or regression candidates): I pulled in data for previous years, back to 2014. I compared 2017 data to 2016 data (I’ll add in comparisons for previous years in later iterations) and simply checked to see who were cHit risers or fallers. The results were telling — players we have on record as swing changers show up with significant positive gains, and players that endured some significant regression fell.

There’s an unintended, possible third use for this tool: identifying injured hitters. Gregory Polanco, Freddie Freeman, and Matt Holliday all suffered/played through injury this year, and they all fell precipitously in the rankings. I’ll need a larger sample size to see whether injuries and a fall in cHit are related or if that’s just noise.

Data!

Okay, so here’s the breakdown. I pulled all 2017 hitters with 400 at-bats or more so I could capture some significant hitters that didn’t have qualifying numbers of ABs due to injury. Ball-bludgeon extraordinaire Joey Gallo is a pretty solid name to have heading up this list, as he’s pretty much the human definition of what this tool is trying to identify. JD Martinez, Aaron Judge, Cody Bellinger, Miguel Sano, Trevor Story, and Justin Turner all in the top 10 is pretty much all the proof-of-concept I needed.

Interesting notes:

Brandon Belt at 12 — Someone needs to tell the Giants to trade him to literally any other team, stat.

Giancarlo Stanton at 46 — Surprisingly, the MVP fell off from his stats in 2016. His grounders and soft contact rose by 3 or more percentage points, and shaved off the equivalent from hard and fly balls. His output was fueled by adding almost 200 ABs to his season — he could actually get better if he can stay healthy and add those hard flies back in!

Francisco Lindor at 58 — The interesting part of this is even though Lindor is still a decent way down the list, he actually was the biggest gainer from last season to this, adding 9.52 points to his cHit. We knew he was gunning for flies from the outset of the season, and it looks like his mission was accomplished.

Mike Moustakas at 87 — Frankly, being bookended by Jose Ramirez and Andrew Benintendi should, in a vacuum, should be great company. But this is a prime example of how cHit requires users to not take the numbers at face value. Ramirez and Benintendi aren’t slug-first hitters like Moose. They’ve got significantly better Speed scores, plus aren’t as prone to soft contact. I’d be very wary of Moose regressing, as he seems to rely on sneaking some less-than ideal homers over fences. If he goes to San Francisco I could see his value crater (see Belt, Brandon).

Eric Hosmer at 206 — Nope, negative, pass, I’m trying to sign quality hitters here <— Suggested responses for GMs when approached this offseason by Scott Boras on behalf of Hosmer.

Final Notes:

•  Batted-ball distribution data is noticeably absent. In one of my iterations I added in those stats, and found that they actually regressed the accuracy of the formula. It doesn’t matter where you hit the ball, as long as you hit it hard.
• Medium% and LD% are noisy stats. They also regressed the formula.
• I may look to replace BB% in future iterations. For now though, it does a decent job of capturing plate discipline and selectivity.
• K% doesn’t seem to have much of an impact on cHit (see Gallo, Joey).
• R-squared numbers over the last four years of data hold pretty steady between .65 and .75, which is really encouraging. Also, the bigger the pool of data per year (number of batters analyzed), the higher R-squared goes; which is ultimately the most encouraging result of this whole endeavor.

Input is greatly appreciated! I’m not a mathematician in any stretch of the imagination, so if there’s a better way of going about this I’d love to hear it. I’ll do a writeup about my swing-change findings at a later date.

I went over the starters version of dScore here, so I’m not going to re-visit that here. I’ll just jump right in with the list!

Risers/Fallers

Danny Salazar (+16) – dScore never gave up on him, despite him being absolute trash early on this year. He came back and dominated, launching him up the ranks even farther in the process. Current status: injured. Again.

Sonny Gray (newly ranked) – If there were any doubts about the Gray the Yankees dealt for, he’s actually surpassed his dScore from his fantastic 2015 season. He’s legit (again).

Alex Wood (-8) – Looks like the shoulder issues took a bit of a toll on his stuff, but dScore certainly isn’t out on him.

Dallas Keuchel (-8) – Keuchel’s stuff isn’t the issue. He’s still a buy for me.

Lance McCullers (-11) – Poor Astros. Maybe not too poor though; their aces have gotten hammered but haven’t fallen far at all. McCullers is going to bounce back.

The Studs

Some light flip-flopping at the top, with Kluber taking over at #1 from Scherzer. The Klubot’s been SO unconscious. Everyone else is pretty much the usual suspects.

The Young Breakouts (re-visited)

Zack Godley (11) – He’s keeping on keeping on. He barely moved since last month’s update, and I’m all-in on him being a stud going forward.

Luis Castillo (9) – He’s certainly done nothing to minimize the hype. In fact, he’s added a purely disgusting sinker to his arsenal and it’s raising the value of everything he throws. Also, from a quick glance at the Pitchf/x leaderboards, two things stand out to me. He seems to have two pitches that line up pretty closely to two top-end pitches: his four-seamer has a near clone in Luis Severino’s, and his changeup is incredibly similar to Danny Salazar’s. That’s a nasty combo.

James Paxton (15) 

The Test Case

Buck Farmer (20) – Okay, so to be honest when he showed up on this list, I absolutely thought it was a total whiff. By ERA he’s been a waste, but he’s really living on truly elite in-zone contact management, swinging strikes, K/BB, and hard-hit minimization. His pitch profile is middling (not bad, but not great either), so I really don’t think he’s going to stay this high much longer. He’s certainly doing enough to earn this spot right now, and I’d expect him to not run a 6+ ERA for much longer.

The Loaded Teams

Yankees – Luis Severino (12), Sonny Gray (17), Masahiro Tanaka (25) / Some teams have guys higher up, but the Yankees are loaded up and down.

Astros – Dallas Keuchel (14), Lance McCullers (19), Brad Peacock (18), Charlie Morton (23) / Similar to the Yankees. Morton and Peacock are having simply phenomenal years.

The Dropouts

Rich Hill (39)

Trevor Cahill (35)

Marcus Stroman (28)

Poor Rich Hill. Lost his perfect game, then lost the game, then lost his spot in the top 25. Cahill’s regressed to #DumpsterFireTrevor since his trade to the Royals. Stroman really didn’t fall that far…and his slider is still a work of art.

The Just Missed

Jordan Montgomery (26) – Too bad the Yankees couldn’t send down Sabathia instead. This kid is good.

Aaron Nola (27) – #Ace

Carlos Martinez (29) – Martinez simply teases ace upside, but frankly I think you can pretty much lump him and Chris Archer (30) in the same group — high strikeouts, too many baserunners and sub-ace starts to move into the top tier.

Dinelson Lamet (32) – He’s absolutely got the stuff. He could stand to work on his batted-ball control though.

Jimmy Nelson (34) – dScore buys his changes. He finished at #148 last year. I’ll call him a #2/3 going forward.

Notes from Farther Down

Jose Berrios is all the way down to 47. His last month cost him 19 spots, but frankly it could be much worse: Sean Manaea lost 39 spots, down to 87. Manaea really looks lost out there. I don’t want to point at the shoulder injury he had earlier this year since his performance really didn’t drop off after that…but I’m wondering if he’s suffering from some fatigue that’s not helped by that. He’s pretty much stopped throwing his toxic backfoot slider to righties, and that’s cost him his strikeouts. Michael Wacha is another Gray-like Phoenix: he’s up to 52 on the list, once again outperforming his 2015 year. I’m cautiously buying him as a #3 with upside. And finally, buzz round: Mike Clevinger (33), Alex Meyer (36), Robbie Ray (38), Rafael Montero (41), and Jacob Faria (43) are already ranked quite highly, and outside of Montero and maybe Meyer I could see all of them bumping up even higher. Clevinger’s really only consistency away from being a legitimate stud.

My next update will be the end-of-season update, so I think I’m going to do a larger ranking than just the top 25; maybe all the way down to 100. Enjoy the last month-plus!

Baseball Prospectus, in their 2015 scouting report of Maikel Franco, had this to say:

“Extremely aggressive approach; will guess, leading to misses or weak contact against soft stuff; gets out in front of ball often—creates hole with breaking stuff away; despite excellent hand-eye and bat speed, hit tool may end up playing down due to approach…”

We saw early this year, and even last year, that exact prediction come to life. Franco seemed to be flailing about vs the soft stuff, beating too many pitches into the ground, and even popping too many up. He never really stopped hitting the ball hard, but we saw too many of those hit in non-ideal ways. For most of the first part of this year the slider gave him absolute fits, and Alex Stumpf wrote about that here. He’s striking out at a career-low rate (13% on the year), but he still isn’t really walking that much although it’s bounced up a percentage point from last year (7.3% in 2017).

Here’s a rundown of his career batted-ball profiles:

I was watching the Phillies game vs. the Marlins on the 18th, and Franco went 3-4 with the go-ahead HR off Dustin McGowan. His HR came on a slider middle-away — literally the exact pitch that’s done nothing but given him fits all year. I also noticed that his batting stance seemed to be different. More upright, quieter. I pulled up a highlight video of an at-bat from early May. Here’s a screencap of his stance just before the pitcher starts his delivery:

That AB ended in an RBI line drive to right. Here’s a screencap of the HR in question from Tuesday, at a similar point in the pitcher’s delivery:

Now if that’s not a mechanical change, I don’t know what is. He’s closed off his stance, eliminated a lot of the knee bend, and seems to have raised his hands juuuuuust a touch. It could be the difference in the camera angle though. Phillies hitting coach Matt Stairs mentioned they’d been trying to get Franco to cut down on his leg kick, so let’s look at that too:
Old leg kick:

New:

Shortly after contact, old:

and the recent HR, similar point:

The “leg kick” seems to be more of a toe tap, and hasn’t changed. What did change, though, is the quality of his follow-through. His head is on ball, he’s better transferred his weight to his front foot, and the results follow. The old AB was a line-drive single opposite field, which looks less of an intentional opposite-field hit and more of a product of bad mechanics. Being so open, he really could only go to right field with authority. If he tried to pull it he’d roll over the pitch. That also would cause him to struggle with the breaking pitch away, which he’d bounce to second. Closing off has allowed him to better get the bat head into a more ideal position to cover the whole plate with authority. He’s always had the bat control to make contact everywhere, but it looks now like he’s improved his chances of making quality contact all over the zone. Here’s the same look at his batted-ball profile since the start of July:

Here’s some assorted metrics, same time period:

vs. his career metrics:

He’s cut his grounders by over 10%, raised his liners by 3%, and turned the rest into fly balls (8%). He’s likely always going to have a pop-up issue, but his pull/center/oppo profile is back to where he was at in 15/16, and he’s hitting the ball hard at a higher rate than ever. Also, his strikeout rate is 6%(!!!!!!)!!!!! He’s making more contact than ever, and that contact is better than ever.

We’ve seen Franco get us hyped before, but never before has there been this type of major mechanical change to point to. Miguel Sano did something similar preseason by raising his hands and quieting his pre-swing load, and it’s paid dividends. Since I started this article, Franco went 2-4 with a single, double, and sac fly; and three of those batted-ball events were hit at 100+mph (the single and double; he was robbed by the 3B on a sharp liner as well).

Going back to his 2015 scouting report: Franco’s still aggressive, if not slowly becoming less aggressive the more he’s in the majors. By changing up his stance, however, he’s closed up the two major holes in his report: getting out in front of the breakers away, and bad contact on soft stuff. Keep an eye on this. One of the more frustrating hyped prospects seems to have made the transformation we all hoped he would, right in front of our eyes.

Early this spring I did a writeup on dScore (“Dominance Score), an algorithm that aims to identify early on pitcher “true talent.” That article reviewed RP performance for 2016.

Here’s a quick review of dScore and how it works:

dScore takes each pitcher and divides them up into a bunch of stats (K-BB%, Hard/Soft%, contact metrics, swinging strikes; as well as breaking down each pitch in their arsenal by weights and movements). We then weight each metric based on indication of success–for relievers, having one or two premium pitches, missing bats, and minimizing hard contact are ideal; whereas starters tend to thrive with a better overall arsenal, minimizing contact, and minimizing baserunners. Below is a breakdown of the metrics we used in our SP evaluations:

Performance metrics: WHIP, K/BB%, Soft%, Hard%, GB%, Contact%, SwStk%, Z-Contact%, O-Contact%

Pitch metrics: wPitch, vPitch (where “Pitch”= FA, FT, CU, SL, CH)

Our current weighting for SPs is a bit more subjective and complex than our RP weighting system, but I’m looking to implement a similar weighting system to the way we weight RP metrics in this evaluation in the near future.

dScore has been around for a year or so now, and one thing I was asked when I initially posted was whether or not it has any “predictive” tendencies. The answer is a pretty clear “no”–BUT what it does do very, very well is validate performance. There’s a fine line between saying “the numbers say pitcher X’s going to stay good” and saying “pitcher X has been good, and this confirms he’s been good”. The problem with the metric is it uses per-pitch statistics, rather than Fielding-Independent metrics. What that means is at a technical level, dScore views the pitcher as directly responsible for everything that happened after a pitch is thrown. There’s been a few outside cases that I’ll get into in a later article; but generally if a pitcher’s been bad, he’s generally viewed as having been bad, or vice versa. It seems particularly bad at projecting regression from underperformance, although I haven’t been tracking pitcher movement as well as I should. I’ll look to implement some sort of evaluation by next year.

The Studs

The top eight guys are really a who’s-who. Scherzer, Wood, Kluber, Sale, Kersh, Keuchel, Syndergaard…Only guy I’m touching on here is Thor, who’s close to begin throwing again. Lat injuries are a whole lotta “?????” for pitchers, but he’s certainly worth a buy if someone is (stupidly) wanting to sell.

The Loaded Teams

Astros – Dallas Keuchel (6), Lance McCullers (8), Brad Peacock (24) / McCullers has broken out. Consider him a stud going forward.

Diamondbacks – Randall Delgado (9), Zack Godley (10), Zack Greinke (18) / Delgado is likely more of a bullpen option at this point. Godley had an awful first outing off the break, but dScore really believes in him.

Dodgers – Alex Wood (2), Clayton Kershaw (5), Kenta Maeda (17), Rich Hill (21) / Come on, really? Give some other team a chance!

The Young Breakouts

Zack Godley (10) – I touched on him above. Although I’m pretty sure he’s due for regression, dScore continues to think he’s got premium stuff. Continue to roll with him.

Luis Castillo (14) – He’s 29 innings into his big-league career, but that’s also 29 innings vs. the Nationals (twice), Rockies (once, in Coors), and the Diamondbacks (once, in Chase). All three teams rank in the top five in the NL in runs scored. BUY. / FUN FACT: The Rockies rank third in runs scored, but are tied with the Padres for dead last in the NL in wRC+ at 81.

James Paxton (16) – He is who we thought he is.

The Still Believin’

Kenta Maeda (17)

Masahiro Tanaka (22)

Danny Salazar (23)

Tanaka’s been god-awful. dScore agrees with his 3.73 xFIP though, and says he should’ve been significantly better than he is. Salazar has somehow been worse, but once again dScore sides with his 3.57 xFIP and says BUY when he comes back from the minors, although I feel like that’s what Salazar’s always been. Every metric says he should be significantly better than he actually is. In 10 years I feel like his career is going to spawn the ultimate sabermetric “what could have been” from FanGraphs.

The Just Missed

Jacob Faria (26)

Jose Berrios (28)

Mike Clevinger (29)

Jordan Montgomery (30)

Chris Archer (31)

A whole bunch of kids and Archer, aka the pitcher we all want Danny Salazar to be.

R.I.P

Nathan Karns (19) – Thoracic Outlet Syndrome. Well, it was a good idea for the Royals…

Notes From Farther Down

Newly-minted Cubs ace Jose Quintana is sitting at 76th. Remember how I said this metric was bad at projecting regression from underperformance? Quintana was sitting just inside the top 100 before his last start. Even though dScore agrees he’s been bad, I’m still buying Quintana in bulk. Old Cubs ace Jon Lester is still getting love from dScore, even after his absolute meltdown vs the Pirates. He’s at 39th. Fellow lefties Sean Manaea and Eduardo Rodriguez bookend him at 38th and 40th respectively. Manaea was sitting in the high-teens for most of the season, then seemed to lose feel for his slider and effectively stopped throwing it. That really hurt his hittability and K’s. It came back around last start vs. Cleveland. I’m continuing to buy him as a #2 ROS. Boston activated Rodriguez recently. Adam Wainwright (104), Julio Teheran (108), Jake Odorizzi (123), Matt Harvey (137), Aaron Sanchez (140), Cole Hamels (143) are a whole bunch of ughhhhh. I’m out on all but Hamels, who I’d argue to hold. His strikeouts disappeared before getting shelved with an oblique strain, then got shelled in his first start back vs. Cleveland. His last three starts have been vintage, and I’m anticipating dScore to catch back up.

Confession: fantasy baseball is life.

Second confession: the chance that I actually turn out to be a sabermetrician is <1%.

That being said, driven purely by competition and a need to have a leg up on the established vets in a 20-team, hyper-deep fantasy league, I had an idea to see if I could build a set of formulas that attempted to quantify a pitcher’s “true-talent level” by the performance of each pitch in his arsenal. Along with one of my buddies in the league who happens to be (much) better at numbers than yours truly, dSCORE was born.

dSCORE (“Dominance Score”) is designed as a luck-independent analysis (similar to FIP) — showing a pitcher might be overperforming/underperforming based on the quality of the pitches he throws. It analyzes each pitch at a pitcher’s disposal using outcome metrics (K-BB%, Hard/Soft%, contact metrics, swinging strikes, weighted pitch values), with each metric weighted by importance to success. For relievers, missing bats, limiting hard contact, and one to two premium pitches are better indicators of success; starting pitchers with a better overall arsenal plus contact and baserunner management tend to have more success. We designed dSCORE as a way to make early identification of possible high-leverage relievers or closers, as well as stripping out as much luck as possible to view a pitcher from as pure a talent point of view as possible.

We’ve finalized our evaluations of MLB relievers, so I’ll be going over those below. I’ll post our findings on starting pitchers as soon as we finish up that part — but you’ll be able to see the work in process in this Google Sheets link that also shows the finalized rankings for relievers.

Any reliever list that’s headed up by Chapman and Miller should be on the right track. Danny Duffy shows up, even though he spent most of the summer in the starting rotation. I guess that shows just how good he was even in a starting role!

We had built the alpha version of this algorithm right as guys like Edwin Diaz and Carl Edwards Jr. were starting to get national helium as breakout talents. Even in our alpha version, they made the top 10, which was about as much of a proof-of-concept as could be asked for. Other possible impact guys identified include Grant Dayton (#14), Matt Bush (#19), Josh Smoker (#26), Dario Alvarez (#28), Michael Feliz (#29) and Pedro Baez (#30).

Since I led with the results, here’s how we got them. For relievers, we took these stats:

Set 1: K-BB%

Set 2: Hard%, Soft%

Set 3: Contact%, O-Contact%, Z-Contact%, SwStk%

Set 4: vPitch,

Set 5: wPitch Set 6: Pitch-X and Pitch-Z (where “Pitch” includes FA, FT, SL, CU, CH, FS for all of the above)

…and threw them in a weighting blender. I’ve already touched on the fact that relievers operate on a different set of ideal success indicators than starters, so for relievers we resolved on weights of 25% for Set 1, 10% for Set 2, 25% for Set 3, 10% for Set 4, 20% for set 5 and 10% for Set 6. Sum up the final weighted values, and you get each pitcher’s dSCORE. Before we weighted each arsenal, though, we compared each metric to the league mean, and gave it a numerical value based on how it stacked up to that mean. The higher the value, the better that pitch performed.

What the algorithm rolls out is an interesting, somewhat top-heavy curve that would be nice to paste in here if I could get media to upload, but I seem to be rather poor at life, so that didn’t happen — BUT it’s on the Sum tab in the link above. Adjusting the weightings obviously skews the results and therefore introduces a touch of bias, but it also has some interesting side effects when searching for players that are heavily affected by certain outcomes (e.g. someone that misses bats but the rest of the package is iffy). One last oddity/weakness we noticed was that pitchers with multiple plus-to-elite pitches got a boost in our rating system. The reason that could be an issue is guys like Kenley Jansen, who rely on a single dominant pitch, can get buried more than they deserve.