With the news that Kenley Jansen is going to miss 8-12 weeks with surgery on his landing foot, the Dodgers are going to need to find someone to close games for them possibly until mid-May. Over the past five seasons, Andrew Friedman has cobbled together bullpens in Tampa that ranked 11th in WAR in the majors. Not stellar, but definitely better than the 24th place the Dodgers have ranked over that same span. Has he given Don Mattingly the right mix to fill the hole left by Jansen, or is he going to go out and add a Rafael Soriano or Francisco Rodriguez through free agency, or will he reach out to the Phillies and try to make a deal for Jonathan Papelbon?

They have a number of holdovers in the mix in Pedro Baez, J.P. Howell, Brandon League and Paco Rodriguez. Friedman has added a plethora of relief arms in former Ray Joel Peralta, Chris Hatcher, Juan Nicasio, and Sergio Santos. After we look at Jansen, we’ll sift through this pile and see who might emerge as the early-season closer, and see if there are any cheap strikeouts or holds.

Kenley Jansen

Prior to undergoing surgery that will shelve him for 8-12 weeks, Kenley Jansen was considered a top closer. He still should be after he gets his boot off and gets back to unleashing his 94 mph cutter against the masses. He’s basically a one-pitch pitcher, as he throws the cutter 88.9% of the time, mixing in the odd slider and sinker.

He’s been one of the most consistent pitchers in baseball over the past three seasons, however, there is a troubling trend:

	K%	BB%	FIP	BABIP	LD%
2012	39.3	8.7	2.40	.221	18.8
2013	38.0	6.2	1.99	.273	24.1
2014	37.7	7.1	1.91	.350	27.6

You’ll notice the K%, BB% and FIP are fairly consistent, but the BABIP and LD% have increased 58.4% and 46.8% over the past two seasons respectively. Not a good recipe for success. He’s going to need to get that LD% back to a more respectable level. If he can do that, then the BABIP should follow. He has no issues emasculating righties, as he held them to a .229 wOBA and struck out 47.5% of them, placing him second behind Aroldis Chapman in that category. If he can get the cutter inside to lefties instead of trying to backdoor it so often, maybe he can break a few more bats. He’s getting groundballs 45.1% of the time against lefties, but they touched him for a .378 BABIP.

vs Lefties	Away/Off	Middle	In/Off
Usage	47%	27%	26%
BAA	.393	.343	.253

If he can keep them from extending against it, he should have even more success.

Joel Peralta

One of the names being bandied about as a possible Jansen replacement is Joel Peralta. Andrew Friedman brought him over after seeing him up close in Tampa. Last year he had batters chase more than most pitchers did, with an O-Swing% of 35.5%. They also swung at less pitches in the zone, with a 64.2% Z-Swing%. How much of that was thanks to the framing skills of Jose Molina and Ryan Hanigan? When Yasmani Grandal is behind the plate, he’ll enjoy the same benefits, but when A.J. Ellis is back there, the zone is going to be smaller.

His 4.41 ERA in 2014 is not one you would expect to see from a guy who could be racking up a few early saves. A 3.11 xFIP and 2.54 SIERA are a little closer to what you’d expect to see from your ninth-inning guy. He’s got a three-pitch mix, with a four-seamer, curve, and splitter, with the splitter being the most effective of the three. If you’re looking for the saves that will be sopped up with Jansen out, I don’t think you’re going to find them here.

Brandon League

Brandon League used a 94 mph sinker and 86 mph splitter to generate an inordinate number of groundballs last year. Hitters didn’t have any problem making contact with the sinker, as they only had a 5.0% whiff rate on the pitch, but when they did put it in play, they smashed it into the ground 71.8% of the time. His overall 67.5% GB% was second amongst relievers last year.

League isn’t going to get many outs via the strikeout, as he only had a 13.9% K rate. Not what you want out of someone pitching the late innings for you. His walk rate of 9.9% isn’t that hot either, but he mitigated it by inducing the most double plays in the majors amongst relievers. Considering the group assembled here, you could do worse with League closing out April and early-May games.

J.P. Howell

Most bullpens would love to house a guy who posts a 1.33 ERA in 47.3 innings, especially a lefty who can retire both RHB and LHB. If you do what we all hate, and take out 1.7 innings against the Cubs in September where he surrendered six earned runs, that’s what you have. He boasts a sinker-curve combo that entices his foes to keep the ball on the ground 57.5% of the time.

One might look at his 49 innings in 68 appearances and see LOOGY. In fact, he was deployed against lefties 52.3% of the time and held them to a 167/284/227 line. Math would tell you he faced righties the other 47.7% of the time, and they only slashed 193/301/284 against him. He has shown he can handle both, and maybe this year you’ll see him get at least three outs in more than 28 of his 68 appearances.

If you’re in a league that likes holds, you could do worse than the 27 he posted in 2014. It will depend how he’s used, however. 2014 saw him come in to a pLI of 1.33, as opposed to 0.81 in 2013. Maybe that was Mattingly realizing what he had, or maybe it was him having to fill the size 14 spikes of Paco Rodriguez(spike size simply an estimate,) but it was a huge leap from the 11 holds he accrued in 2013 under the same regime.

Pedro Baez

Pedro Baez used his 96 mph four-seamer to his advantage in 20 innings in 2014. Using it three quarters of the time, he produced a tiny 0.88 WHIP. Don’t count on that again this season though, as it was fueled by a .197 BABIP. His 19.6% K% and 5.4% BB% aren’t shabby, but don’t expect him to be seeing to many high-leverage opportunities in the early going, if he even breaks camp with the Dodgers. Until he gets a secondary pitch that hitters fear, they’re going to be teeing off on his flaming arrow.

Chris Hatcher

You won’t find many short relievers who display a legit four-pitch mix, especially guys who were calling the pitches as recently as 2010. He throws a four-seamer and a sinker, both at 96 mph, and deviates with a couple of 88 mph offerings in his slider and splitter. The least-used pitch is the slider, at 17%, with the four-seamer topping out as at 42%.

In 56 innings with the Marlins after being recalled in late May, he posted a 25.9% K rate, coupled with a 5.2% BB rate. Not too shabby. His 3.38 ERA isn’t anything special, but his 2.56 FIP is almost a run better. If he can solve his homesickness (5.34 road ERA), and couple that with a stellar 1.32 home ERA, you may have a late-inning stud on your hands here. The difference could be as simple as dumb luck, with a 57.9% road LOB% and a 90.9% home LOB%, even with similar .337 and .313 BABIPs respectively.

With Jansen out, if the Dodgers stay in-house and let the dominoes start to fall, Hatcher could be one to get behind for 70+ innings and 80 or more whiffs. Keep an eye on him in the spring, if he goes west from Camelback Ranch, he could be a cheap source of goodness for you. If they bring in Chamberlain, Rodriguez, Papelbon or another name, however, he could find himself back in AAA. Which would not be smart.

Paco Rodriguez

Remember when Paco Rodriguez burst onto the scene with his Statue of Liberty motion, liberating his way to a 2.32 ERA and 20 holds in 2013? What happened last year? Well his ERA went up to 3.86, but both his FIP and xFIP went DOWN by 0.16 and 0.31 respectively. Maybe his LOB% going from 81.8% to 68.5%, combined with his BABIP exploding from .210 to .324 had something to do with it?

Sure he threw 40 innings less at the Major League level in 2014 and he lost 2 mph off his already pedestrian 89.6 mph fastball, but I’d expect something closer to 2013, if Mattingly hasn’t totally soured on him. He should get you more than a strikeout an inning, and if Howell falters or gets hurt, Rodriguez will be there to sop up those innings.

Sergio Santos

Remember when so many of us rushed to grab him last year because he was going to be closing in Toronto? Five April saves in the first two weeks of the season were pretty hot, but then his history of arm troubles bounced back to take him totally out of fantasy relevance. IF, he has a healthy spring and breaks camp with the Dodgers as an NRI, and IF, you’re in a crazy-deep mixed league, maybe you look to him for some holds. Promise yourself though, that if these unlikely events manage to come together, that you drop him like he’s hot at the first poor outing. Decreased velocity won’t be the first sign, because he actually ticked up one mph across the board through May, before taking two steps back. Nobody wants to see a guy hurt, but be on the lookout for warning signs if you’ve decided to roster him.

Juan Nicasio

The erstwhile Rockie starter seems to be primed for a shift to the pen. He ticked up a couple miles an hour after the Rockies gently placed him there in the second half of 2014, and was able to ditch his change. If he’s able to locate 95 and then subtract ten mph with a slider, that could be a dangerous weapon in the pen. He threw more than one inning a few times in Colorado, let’s see if Mattingly chooses to deploy him in the same way. With a 15.0 K%-BB% in relief, he’s not too bad of an option.

There was an article published in 2013 on FanGraphs that focused on the value of starter inconsistency. The basic idea is relatively simple – a starter who does terribly in one start and very well in the next (e.g., 8 runs in 2 innings followed by 2 runs in 8 innings) gives his team better chances to win than one who is mediocre in two starts (5 runs in 5 innings both outings). Mr. Hunter did some math to illustrate the fact, and quantify it somewhat, but it was a relatively rough measure, and I think the concept is intuitive enough not to gain a ton from a rough demonstration. Definitely read that article, though!

I think the first question that comes to mind upon reading that is: is this sustainable? Is consistent inconsistency possible? To find out, I came up with a relatively simple measure of inconsistency within a season. For every pitcher, I calculated the standard deviation of the Game Scores for each of their starts. If you’re not familiar with Game Score, it’s a Bill James-developed metric that gives pitchers points for outs and strikeouts and docks them points for hits, walks, and runs. It’s mostly a narrative stat, but I think it does a good job of illustrating the quality of a given start. The best start of 2014 by Game Score: Clayton Kershaw’s no-hitter against the Rockies, on June 18^th, in which he didn’t allow a hit or walk (damn you Hanley Ramirez) and struck out 15, good for a Game Score of 102. The worst: Colby Lewis’s July 10^th start, in which he went 2.1 innings, gave up 13 hits and gave up 13 runs. Didn’t walk anybody! Still had the abysmal Game Score of -12. The 2014 Rangers, ladies and gentlemen.

By looking at the standard deviation of a season’s worth of Game Scores, we get a measure of the inconsistency of their quality. I set a minimum of 10 starts to qualify, which ensures no one is being labeled consistent off a single week of pitching. The usual caveats apply – pitchers needed to be good enough to pick up 10 starts, so this is a snapshot of usage, not just skill. Before looking at the year-to-year correlation, I want to look at the most consistent and inconsistent starters of 2014.

Rank	Name	Games Started	FIP	Game Score StDev
1	Miles Mikolas	10	4.77	24.73
2	Jerome Williams	11	4.09	23.25
3	Brandon Cumpton	10	3.22	21.97
4	Robbie Ross	12	4.74	20.88
5	Juan Nicasio	14	4.18	20.84
…
178	Jordan Lyles	22	4.22	11.40
179	Kyle Hendricks	13	3.32	10.98
180	Marco Estrada	18	4.88	10.46
181	Mike Fiers	10	2.99	10.00
182	David Buchanan	20	4.27	9.85

Not surprisingly, we see a lot of starters with fairly low numbers of starts, since extreme values (either high or low) are likely to regress toward the variance for the whole sample (15.53 in 2014) as the number of starts increases. On the consistent end, David Buchanan started his first game for the Phillies on May 20^th, and between then and the end of the season, his worst start by Game Score came on June 3^rd, when he gave up 7 runs in 6 innings, striking out 2 and walking 6, good for a Game Score of 28. But for a worst start, that’s not that awful, and his best wasn’t that great either – about two weeks later, on June 19^th, he threw 7.2 innings of 1-run ball, with 1 walk and 4 strikeouts, and a Game Score of 70. The rest of his season was extremely consistent in its mediocrity, with 16 of his 20 starts having Game Scores between 40 and 60, so it’s no surprise that he takes the bottom spot on this list.

Miles Mikolas was worse, but also much more erratic, with outings like his on August 25^th (8 innings, 1 walk, 5 strike outs, and no runs, Game Score of 80) and on July 7^th (3.1 innings, 0 walks, 5 strike outs (looks fine so far!) and 9 runs (oh), Game Score of 5). Between those two starts, he had an RA9 of 7.15, but my guess is he gave the Rangers a much higher expected win percentage than if he had evenly distributed those runs across two 6-inning outings.

But does this mean anything when it comes to evaluation? Should a GM view one of the inconsistent starters with a little more optimism for 2015 than one of the consistent starters? In a word, no.

That is a pile of random points, and a resulting R² value that is basically zero. The inconsistency of a pitcher in 2013 had almost nothing to do with their inconsistency in 2014, so while inconsistency is a hidden way for a pitcher’s results to be better than they look, it doesn’t appear to be a skill.

Even if this was predictable, though, this doesn’t seem to be the sort of thing that would swing the needle too far in either direction. The theoretical argument makes sense, but in practice, there are lots of mitigating factors that might make consistency more valuable. Maybe the starter the day before got bombed, and the bullpen really just needs a day off, and a 100% chance of 6 innings/4 runs is more valuable to the team that day than a 50% chance of 8 innings/1 run and 4 innings/7 runs. There’s also just a lot of randomness, probably enough to drown out the small effect. Inconsistency isn’t consistent year-to-year, and it also isn’t predictable. If a pitcher could control what games he was bad, and bank some great innings to use when he needed them, that would be a big deal. They can’t.

Managers, however, can. They can use their bad innings in games where the outcome is already practically decided, and save their best innings for the tightest of moments, with optimal bullpen use. Day-to-day inconsistency of a pitcher isn’t predictable, but pitcher-to-pitcher inconsistency of a bullpen is, and a similar argument for its value applies. A team with a lights-out closer (FIP of 2.00) and a pretty terrible long man (FIP of 5.00) is going to win more games than a team with two okay relievers (FIP of 3.50 for both), if the manager of the first team deploys his closer in close games and lets the other pitcher eat innings in blowouts. The ability to choose those spots makes the effect potentially much larger than among starters.

Balancing that, however, is the fact that relievers just have a much smaller effect on the game, so this still might not be big enough to matter. However, if it did have a noticeable effect, it would give a team an edge that wouldn’t be reflected in measures of collective performance, and so this could be one reason a team beat its BaseRuns estimated record. To see if that was perhaps the case in 2014, I developed a simple measure of bullpen-wide inconsistency. After discarding some more complicated ideas, I settled on calculating the standard deviation for each team’s eight relief pitchers that threw the most innings. This picks up most of each bullpen’s regulars and semi-regulars, and should be an okay measure of the distribution of skill in a bullpen.

Again, I wanted to first look at the most and least consistent bullpens of 2014 by this measure.

Rank	Team	Innings	FIP	WAR	StDev
1	KCR	464.0	3.29	5.9	1.65
2	HOU	468.2	4.11	0.4	1.54
3	OAK	467.1	3.47	4.0	1.35
…
28	MIN	521.2	3.88	2.0	0.51
29	MIA	510.1	3.20	4.6	0.50
30	SEA	498.1	3.24	4.5	0.50

Seeing the Royals as the most inconsistent bullpen of 2014 is not a surprise. On the one hand, Wade Davis (1.19 FIP), Kelvin Herrera (2.69) and Greg Holland (1.83) combined to throw over 200 innings of absurdly good relief. The next five most-used relievers, however, were Aaron Crow (5.40 FIP), Louis Coleman (5.69), Francisley Bueno (3.84), Michael Mariot (3.93), and Tim Collins (4.80). Those are not good pitchers, and that’s a huge gap between the two groups, but by using the top three in close games and letting the other five eat as many non-crucial innings as possible, Kansas City might have been able to win a lot more games than a bullpen with eight relievers with FIPs around 3.30 (the figure for the bullpen as a whole). The Royals are also a good example of why the advantages of inconsistency might just not show up – Ned Yost was (in-)famous for not using his bullpen optimally, and sticking to strictly defined roles with his relievers, which is the sort of thing that could nullify this effect.

The consistent bullpens are pretty boring, so I won’t spend much time on them. Seattle’s worst reliever by FIP in the eight most-used was Joe Beimel (4.18), and the best was Charlie Furbush (2.80), with the other six spread fairly evenly between them. Consistency has advantages, but not being able to turn to a true shutdown reliever when needed, or having to use a fairly valuable arm even in a blowout, might have its own costs, even compared to a bullpen with similar overall skill, such as Kansas City.

Unfortunately, either because of manager incompetence, the smallness of the effect, or something else entirely, bullpen inconsistency does very little to explain BaseRuns over- or under-performance in 2014. In the below graph, teams that beat their BaseRuns record are on the right, while those that fell below are on the left, and more inconsistent bullpens are higher versus consistent bullpens lower.

That, again, is basically a random collection of points. In the top right, the Royals, both the most inconsistent bullpen and the team with the biggest positive gap between their actual winning percentage and the BaseRuns estimate (5.0%). But in the top left, Houston, the second-most inconsistent bullpen and the second-largest negative gap between their actual and BaseRuns winning percentages (-4.6%).

At best, this is inconclusive, but I find the idea really interesting. This does at least show that, on an individual pitcher basis, inconsistency is not predictable, even when looking at previous years, which I think bucks conventional wisdom in a real way. Seeing what bullpens and pitchers were particularly erratic in 2014 is fun, and it’s something I’ll be keeping an eye on in 2015.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Consequently, Mike Piazza is listed on the Dodgers roster for the duration of his career while the Giants claim Bobby Bonds and the Indians declare Roger Maris and Manny Ramirez. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. Additional information and a discussion forum are available at TuataraSoftware.com.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

Assessment

The 2009 Colorado Rockies         OWAR: 44.4     OWS: 297     OPW%: .561

GM Dan O’Dowd acquired 65% (28 of 43) of the ballplayers on the 2009 Rockies roster. 29 players were selected during the Amateur Draft though staff ace Ubaldo Jimenez was signed as an amateur free agent. Based on the revised standings the “Original” 2009 Rockies captured the National League pennant following a fierce battle with the Dodgers for the Western division title.

Chone Figgins accrued 26 Win Shares and received his lone All-Star nomination during the 2009 campaign. He established career-highs with 114 runs scored, 101 walks and a .395 OBP while swiping 42 bags from the leadoff spot. Troy Tulowitzki (.297/32/92) pilfered 20 bases and registered 101 runs on the way to a fifth-place finish in the N.L. MVP balloting. Matt “Big Daddy” Holliday compiled a .313 BA, swatted 24 long balls and knocked in 109 runs. Todd Helton eclipsed the .300 mark for the eleventh time in twelve campaigns, batting at a .325 clip while contributing 38 two-baggers. Brad Hawpe laced 42 doubles and slugged 23 circuit clouts to merit a trip to the Midsummer Classic. 

LINEUP	POS	WAR	WS
Chone Figgins	3B	4.72	26.32
Seth Smith	DH/LF	2.61	13.43
Troy Tulowitzki	SS	5	25.72
Matt Holliday	LF	4.7	26.18
Brad Hawpe	RF	0.45	18.96
Juan Uribe	3B	3.01	16.45
Todd Helton	1B	3.41	23.24
Chris Iannetta	C	2.27	9.76
Craig Counsell	2B	2.53	14.12
BENCH	POS	WAR	WS
Everth Cabrera	SS	2.44	13.48
Clint Barmes	2B	1.34	14.32
Juan Pierre	LF	1.31	11.2
Ian Stewart	3B	1.13	11.98
Dexter Fowler	CF	1.1	14.4
Jeff Baker	3B	1.03	7.96
Jayson Nix	2B	0.7	6.54
Cory Sullivan	LF	0.16	3.14
Josh Bard	C	0.1	4.61
Eric Young Jr.	2B	-0.25	0.21
Jeff Salazar	CF	-0.32	0.13
Jody Gerut	CF	-0.41	4.01
Garrett Atkins	3B	-0.58	5.21
Ryan Spilborghs	LF	-0.91	6.16

Ubaldo Jimenez whiffed 198 batsmen and accrued 15 victories along with a 3.47 ERA in his second full season in the Rockies’ starting rotation. Aaron Cook posted a record of 11-6 with a 4.16 ERA following his All-Star season in ’08. Justin Miller (3-3, 3.18) led an otherwise undistinguished bullpen staff.

ROTATION	POS	WAR	WS
Ubaldo Jimenez	SP	6.05	19.07
Aaron Cook	SP	2.88	11.26
Esmil Rogers	SP	0.06	0.21
Jhoulys Chacin	SW	0.08	0.39
Franklin Morales	RP	0.31	4.19
BULLPEN	POS	WAR	WS
Justin Miller	RP	0.66	4.47
Matt Daley	RP	0.55	3.97
Mark DiFelice	RP	0.43	3.66
Alberto Arias	RP	0.21	3.27
Jason Jennings	RP	0.2	3.92
Ryan Speier	RP	0.02	0.26
Steven Register	RP	0.01	0.08
Sean Green	RP	-0.14	2.73
Pedro Strop	RP	-0.2	0
Manny Corpas	RP	-0.25	0.88
Juan Morillo	RP	-0.36	0
Jorge Sosa	RP	-0.38	0.09
Jamey Wright	RP	-0.53	3.69
Luis Ayala	RP	-0.59	1.47
David Patton	RP	-0.68	0.02

 The “Original” 2009 Colorado Rockies roster

NAME	POS	WAR	WS	General Manager	Scouting Director
Ubaldo Jimenez	SP	6.05	19.07	Dan O’Dowd	Bill Schmidt
Troy Tulowitzki	SS	5	25.72	Dan O’Dowd	Bill Schmidt
Chone Figgins	3B	4.72	26.32	Bob Gebhard	Pat Daugherty
Matt Holliday	LF	4.7	26.18	Bob Gebhard	Pat Daugherty
Todd Helton	1B	3.41	23.24	Bob Gebhard	Pat Daugherty
Juan Uribe	3B	3.01	16.45	Bob Gebhard	Pat Daugherty
Aaron Cook	SP	2.88	11.26	Bob Gebhard	Pat Daugherty
Seth Smith	LF	2.61	13.43	Dan O’Dowd	Bill Schmidt
Craig Counsell	2B	2.53	14.12	Bob Gebhard	Pat Daugherty
Everth Cabrera	SS	2.44	13.48	Dan O’Dowd	Bill Schmidt
Chris Iannetta	C	2.27	9.76	Dan O’Dowd	Bill Schmidt
Clint Barmes	2B	1.34	14.32	Dan O’Dowd	Bill Schmidt
Juan Pierre	LF	1.31	11.2	Bob Gebhard	Pat Daugherty
Ian Stewart	3B	1.13	11.98	Dan O’Dowd	Bill Schmidt
Dexter Fowler	CF	1.1	14.4	Dan O’Dowd	Bill Schmidt
Jeff Baker	3B	1.03	7.96	Dan O’Dowd	Bill Schmidt
Jayson Nix	2B	0.7	6.54	Dan O’Dowd	Bill Schmidt
Justin Miller	RP	0.66	4.47	Bob Gebhard	Pat Daugherty
Matt Daley	RP	0.55	3.97	Dan O’Dowd	Bill Schmidt
Brad Hawpe	RF	0.45	18.96	Dan O’Dowd	Bill Schmidt
Mark DiFelice	RP	0.43	3.66	Bob Gebhard	Pat Daugherty
Franklin Morales	RP	0.31	4.19	Dan O’Dowd	Bill Schmidt
Alberto Arias	RP	0.21	3.27	Dan O’Dowd	Bill Schmidt
Jason Jennings	RP	0.2	3.92	Bob Gebhard	Pat Daugherty
Cory Sullivan	LF	0.16	3.14	Dan O’Dowd	Bill Schmidt
Josh Bard	C	0.1	4.61	Bob Gebhard	Pat Daugherty
Jhoulys Chacin	SW	0.08	0.39	Dan O’Dowd	Bill Schmidt
Esmil Rogers	SP	0.06	0.21	Dan O’Dowd	Bill Schmidt
Ryan Speier	RP	0.02	0.26	Dan O’Dowd	Bill Schmidt
Steven Register	RP	0.01	0.08	Dan O’Dowd	Bill Schmidt
Sean Green	RP	-0.14	2.73	Dan O’Dowd	Bill Schmidt
Pedro Strop	RP	-0.2	0	Dan O’Dowd	Bill Schmidt
Manny Corpas	RP	-0.25	0.88	Bob Gebhard	Pat Daugherty
Eric Young	2B	-0.25	0.21	Dan O’Dowd	Bill Schmidt
Jeff Salazar	CF	-0.32	0.13	Dan O’Dowd	Bill Schmidt
Juan Morillo	RP	-0.36	0	Dan O’Dowd	Bill Schmidt
Jorge Sosa	RP	-0.38	0.09	Bob Gebhard	Pat Daugherty
Jody Gerut	CF	-0.41	4.01	Bob Gebhard	Pat Daugherty
Jamey Wright	RP	-0.53	3.69	Bob Gebhard	Pat Daugherty
Garrett Atkins	3B	-0.58	5.21	Dan O’Dowd	Bill Schmidt
Luis Ayala	RP	-0.59	1.47	Dan O’Dowd	Pat Daugherty
David Patton	RP	-0.68	0.02	Dan O’Dowd	Bill Schmidt
Ryan Spilborghs	LF	-0.91	6.16	Dan O’Dowd	Bill Schmidt

Honorable Mention 

The “Original” 2007 Rockies              OWAR: 42.0     OWS: 264     OPW%: .546

Matt Holliday (.340/36/137) topped the Senior Circuit in batting average, RBI, hits (216) and doubles (50), earning a runner-up finish in the 2007 NL MVP vote. Troy Tulowitzki (.291/24/99) accrued 104 tallies and placed second in the Rookie of the Year balloting. Todd Helton ripped 42 doubles and third-sacker Chone Figgins manufactured a career-best .330 BA. Garrett Atkins (.301/25/111) and Brad Hawpe (.291/29/116) contributed to Colorado’s offensive onslaught. Jeff Francis paced the starting staff with a 17-9 record while Manny Corpas posted an ERA of 2.08 and saved 19 contests.

On Deck

The “Original” 1992 Brewers

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

With less than a month before Opening Day and fantasy baseball prep ramping up, I thought I’d take a look at bullpen roles for each major-league team. Most leagues still use saves as a category and it’s important to know who’s slated for the closer role, as well as the #2 and #3 guy in each pen if you’re in a large league or a league where every team is scrambling for the guy next-in-line to get those precious saves.

I used eight sources to determine how the fantasy experts are projecting each team’s bullpen. The sources are: FanGraphs Bullpen Report, CBS sports, Rotoworld, MLB.com, ESPN.com, Roster Resources, Fantasy Alarm, and Fox. For each team, I’ve listed their closer, setup guy #1, and setup guy #2, with the number of lists they are on out of the eight sources. I’ve also listed the projected saves for each pitcher based on the FanGraphs Depth Charts.

Locked In Their Roles

 

Kansas City Royals

Closer: Greg Holland (all 8 sources)

Setup #1: Wade Davis (all 8 sources)

Setup #2: Kelvin Herrera (all 8 sources)

 

Comment: There’s no question how the Royals’ bullpen is set up, which is not surprising considering how successful they were last season when they rode a tremendous bullpen all the way to the seventh game of the World Series.

 

FanGraphs Depth Charts: Holland—37, Davis—1, Herrera—1

 

Philadelphia Phillies

Closer: Jonathan Papelbon (all 8 sources)

Setup #1: Ken Giles (all 8 sources)

Setup #2: Jake Diekman (all 8 sources)

 

Comment: All eight sources have the Phillies’ pen lineup up as Papelbon, Giles, and Diekman. The one thing to watch for here is if the Phillies can find a taker for Papelbon’s contract. He’s owed $13 million this year and has a vesting option for another $13 million in 2016 if he finishes 55 games this year or 100 games over 2014-2015. If he goes, Giles is the guy to have.

 

FanGraphs Depth Charts: Papelbon—36, Giles—2, Diekman—2

 

Atlanta Braves

Closer: Craig Kimbrel (all 8 sources)

Setup #1: Jason Grilli (all 8 sources)

Setup #2: Jim Johnson (7), James Russell (1)

 

Comment: The Braves are opening a new ballpark in 2017 and Kimbrel is owed $9 million this year, $11 million next year and $13 million in 2017, with a $13 million club option for 2018. Does a team that doesn’t look ready to compete in the next two years really want to spend $20 million on a closer during that time? If Kimbrel gets traded, Grilli is next in line.

 

FanGraphs Depth Charts: Kimbrel—39, Grilli—2

 

 

Two Spots Set, What About That Third?

 

St. Louis Cardinals

Closer: Trevor Rosenthal (8)

Setup #1: Jordan Walden (8)

Setup #2: Seth Maness (5), Matt Belisle (3)

 

Comment: Rosenthal and Walden look to have the late-game roles locked in, but the #2 setup guy isn’t as certain. Maness is a ground-ball machine (career 61.5% GB%) with a low strikeout rate (15.9%) but a career 2.66 ERA. He picked up 3 saves last year. Belisle had a 4.87 ERA last year with the Rockies, but is projected to be much better this year (3.52 ERA—FanGraphs Depth Charts).

 

FanGraphs Depth Charts: Rosenthal—42

 

Cleveland Indians

Closer: Cody Allen (8)

Setup #1: Bryan Shaw (8)

Setup #2: Scott Atchison (5), Marc Rzepczynski (3)

 

Comment: It’s Allen and Shaw, with Atchison the most likely second setup guy. Rzepczynski shouldn’t be allowed to face a right-handed hitter with the game on the line. In his career, righties have hit .272/.366/.441 against him.

 

FanGraphs Depth Charts: Allen—38, Shaw—2, Atchison—2

 

New York Yankees

Closer: Dellin Betances (8)

Setup #1: Andrew Miller (8)

Setup #2: Adam Warren (5), David Carpenter (3)

 

Comment: Betances (3.2 WAR) and Miller (2.3 WAR) were two of the top six relievers by FanGraphs WAR last year. The consensus seems to be that Betances will be the closer with Miller the primary setup guy, but the FanGraphs Depth Charts show Betances with 30 saves to Miller’s 11, so he’s not being projected as the slam-dunk closer just yet. Both Warren and Carpenter are solid setup guys.

 

FanGraphs Depth Charts: Betances—30, Miller—11, Warren—2

 

Los Angeles Angels of Anaheim

Closer: Huston Street (8)

Setup #1: Joe Smith (8)

Setup #2: Fernando Salas (4), Mike Morin (2) Cesar Ramos (1), Vinnie Pestano (1)

 

Comment: If/when Huston Street misses a couple weeks with an injury in the middle of the season, Joe Smith will be the guy. After Smith, Fernando Salas has the most experience picking up saves, as he had 24 saves with the Cardinals back in 2011.

 

FanGraphs Depth Charts: Street—37, Smith—4

 

San Diego Padres

Closer: Joaquin Benoit (8)

Setup #1: Kevin Quackenbush (7), Dale Thayer (1)

Setup #2: Dale Thayer (5), Shawn Kelley (1), Alex Torres (1), Nick Vincent (1)

 

Comment: Benoit and Quackenbush both picked up saves after Huston Street was traded last year, but Benoit is clearly the closer going into this season. Thayer had 7 saves for the Padres in 2012. The FanGraphs Depth Charts have Brandon Maurer getting 4 saves and Shawn Kelley with 2, but Quackenbush with zero, which doesn’t seem quite right to me.

 

FanGraphs Depth Charts: Benoit—37, Maurer—4, Kelley—2

 

Pittsburgh Pirates

Closer: Mark Melancon (8)

Setup #1: Tony Watson (6), Jared Hughes (1), John Holdzkom (1)

Setup #2: Jared Hughes (3), Tony Watson (2), Antonio Bastardo (2), John Holdzkom (1)

 

Comment: Watson is considered the top setup guy for Melancon, with Hughes and Holdzkom falling in place behind him. Holdzkom has the sky-high strikeout rate, while Hughes is a ground ball machine (64.6% GB%).

 

FanGraphs Depth Charts: Melancon—40, Bastardo—2

 

Seattle Mariners

Closer: Fernando Rodney (8)

Setup #1: Danny Farquhar (6), Yoervis Medina (2)

Setup #2: Yoervis Medina (4), Danny Farquhar (2), Charlie Furbush (1), Tom Wilhelmsen (1)

 

Comment: After Rodney, it’s either Farquhar or Medina, with Farquhar the more popular choice among the eight sources used here and also the better pitcher statistically. After Rodney, Wilhelmsen has the most experience at closer.

 

FanGraphs Depth Charts: Rodney—40, Wilhelmsen—4

 

Detroit Tigers

Closer: Joe Nathan (8)

Setup #1: Joakim Soria (6), Al Alburquerque (1), Ian Krol (1)

Setup #2: Joakim Soria (2), Al Alburquerque (2), Joba Chamberlain (2), Bruce Rondon (2)

 

Comment: Joe Nathan started to show his age in 2014. His strikeout rate dropped; his walk rate rose, and he had the third-worst ERA of his 16-year career (4.81). His FIP (3.94) and xFIP (4.14) weren’t as bad as his ERA, but they weren’t great either. He’ll be 40 this year. In the long history of baseball, relievers 40 and older have a total of 13 seasons with 20 or more saves and eight seasons with 30 or more. The odds are against Joe Nathan. Joakim Soria is ready to take over should Nathan falter. Al Alburquerque looks to be the #3 guy in this pen. The FanGraphs Depth Charts expect Soria to get plenty of save opportunities this year.

 

FanGraphs Depth Charts: Nathan—24, Soria—14, Alburquerque—1, Chamberlain—1

 

Texas Rangers

Closer: Neftali Feliz (8)

Setup #1: Tanner Scheppers (7), Shawn Tolleson (1)

Setup #2: Kyuji Fujikawa (3), Shawn Tolleson (2), Tenner Scheppers (1), Alex Claudio (1), Mendez (1)

 

Comment: Feliz is the #1 guy going into the season, but his peripheral statistics were ugly last year. He had a 1.99 ERA, with a 4.90 FIP and 4.60 xFIP, thanks to a below-average 6.0 K/9, mediocre 3.1 BB/9, and ugly 1.4 HR/9. He had a BABIP of .176 and LOB% of 100%, neither of which are likely to be repeated. Scheppers was injured for much of the year, as was Fujikawa, but either pitcher could get some save chances if Feliz falters.

 

FanGraphs Depth Charts: Feliz—33, Tolleson—4

 

Arizona Diamondbacks

Closer: Addison Reed (8)

Setup #1: Brad Ziegler (7), Evan Marshall (1)

Setup #2: Oliver Perez (5), Evan Marshall (2), Brad Ziegler (1)

 

Comment: Reed is currently having shoulder problems, but the Diamondbacks’ team site reported that the team is optimistic he’ll be ready for Opening Day. Ziegler is most often named as the top setup guy, with Oliver Perez and Evan Marshall among the possibilities for late inning work. The FanGraphs Depth Charts are all over the place with this bullpen, with nine pitchers projected for at least one save.

 

FanGraphs Depth Charts: Reed—7, Ziegler—7, Marshall—7, Perez—6, Delgado—5, D. Hudson—4, M. Reynolds—2, Ch. Anderson—1, M. Stites—1, R. Ray—1

 

Who’s the 8^th Inning Guy?

 

Boston Red Sox

Closer: Koji Uehara (8)

Setup #1: Junichi Tazawa (5), Edward Mujica (3)

Setup #2: Edward Mujica (4), Junichi Tazawa (3), Craig Breslow (1)

 

Comment: Since becoming a reliever in 2010, Uehara has been terrific. Still, he’ll be 40 years old this year and there’s not much history of 40-year-old relievers racking up high save totals. Tazawa is the favored option after Uehara by the eight sources used here, but Mujica is the guy with a history of getting saves (37 in 2013, 8 in 2014).

 

FanGraphs Depth Charts: Uehara—36, Tazawa—2, Mujica—2

 

Oakland Athletics

Closer: Tyler Clippard (8)—injury replacement for Sean Doolittle (DL)

Setup #1: Ryan Cook (5), Eric O’Flaherty (3)

Setup #2: Ryan Cook (3), Eric O’Flaherty (2), Fernando Abad (2), Evan Scribner (1)

 

Comment: While Doolittle is out the Oakland pen should go Clippard-Cook-O’Flaherty, although three sources have O’Flaherty ahead of Cook. Once Doolittle returns, everyone else gets bumped back a spot.

 

FanGraphs Depth Charts: Doolittle—28, Clippard—12

 

Chicago Cubs

Closer: Hector Rondon (8)

Setup #1: Pedro Strop (5), Neil Ramirez (2), Jason Motte (1)

Setup #2: Neil Ramirez (4), Pedro Strop (3), Zac Rosscup (1)

 

Comment: CBS is the only source that has Motte listed as the first setup guy so, despite his experience as a closer in 2012, it’s much more likely that Strop and Ramirez will be the primary setup guys to Hector Rondon.

 

FanGraphs Depth Charts: Rondon—40, Strop—3

 

Washington Nationals

Closer: Drew Storen (8)

Setup #1: Casey Janssen (4), Aaron Barrett (2), Craig Stammen (1), Matt Thornton (1)

Setup #2: Craig Stammen (3), Matt Thornton (2), Aaron Barrett (1), Blevins (1), Tanner Roark (1)

 

Comment: Janssen has earned 81 saves over the last three years and he’s the favorite to be the primary setup guy here, but his strikeout numbers last season were ugly (5.5 K/9). Barrett looks most likely to jump ahead of Janssen in this pen.

 

FanGraphs Depth Charts: Storen—43, Janssen—4

 

Chicago White Sox

Closer: David Robertson (8)

Setup #1: Zach Putnam (4), Jake Petricka (3), Zach Duke (1)

Setup #2: Jake Petricka (4), Zach Duke (3), Nate Jones (1)

 

Comment: There’s a difference of opinion on who will be the primary setup guy to Robertson in the White Sox’ bullpen. Putnam had 6 saves last year and a 1.98 ERA but a 3.08 FIP and 3.64 xFIP. Petricka had 14 saves last year and a 2.96 ERA but 3.60 FIP and 3.76 xFIP. Duke is projected to get more saves than both Putnam and Petricka by the FanGraphs Depth Charts. They also have Robertson with just 25 projected saves, which seems much too low.

FanGraphs Depth Charts: Robertson—25, Duke—8, Putnam—6

 

Cincinnati Reds

Closer: Aroldis Chapman (8)

Setup #1: Jumbo Diaz (4), Sam LeCure (3), Sean Marshall (1)

Setup #2: Sam LeCure (4), Burke Badenhop (2), Jumbo Diaz (1), Oscar Villarreal (1)

 

Comment: The primary setup job is still a question mark here, based on the eight sources. Jumbo Diaz is listed as setup guy #1 by four sources, but LeCure is listed more often as either the primary setup guy or the #2 guy.

 

FanGraphs Depth Charts: Chapman—39, LeCure—3

 

Baltimore Orioles

Closer: Zach Britton (8)

Setup #1: Tommy Hunter (4), Darren O’Day (4)

Setup #2: Tommy Hunter (3), Brian Matusz (3), Darren O’Day (2)

 

Comment: Hunter and O’Day are listed as the primary setup guy by four sources each. Hunter has more experience picking up saves (15 over the last two years), but O’day is the better pitcher.

 

FanGraphs Depth Charts: Britton—36, O’Day—3, Hunter—1

 

Miami Marlins

Closer: Steve Cishek (8)

Setup #1: A.J. Ramos (3), Mike Dunn (3), Bryan Morris (2)

Setup #2: A.J. Ramos (5), Mike Dunn (3)

 

Comment: There isn’t a strong consensus on the setup guy in this pen, but it’s most likely Ramos, then Dunn. Ramos, the right-hander, is most likely to step in if something happens to Cishek.

 

FanGraphs Depth Charts: Cishek—39, Ramos—1, Dunn—1

 

Minnesota Twins

Closer: Glen Perkins (8)

Setup #1: Casey Fien (3), Brian Duensing (3), Michael Tonkin (1), Caleb Thielbar (1)

Setup #2: Casey Fien (2), Brian Duensing (2), Ryan Pressly (2), Michael Tonkin (1), Stauffer (1)

 

Comment: Fien saw his dropout rate drop from 10.6 K/9 in 2013 to 7.3 K/9 in 2014, but he’s the top right-handed setup guy, so he would most likely be the guy to get saves if Perkins is unable to do so for some reason. Duensing is listed as the primary setup guy by three sources. His career 6.1 K/9 and 4.12 ERA suggests he’s not a guy you want to have on your fantasy roster.

 

FanGraphs Depth Charts: Perkins—34, Fien—1, Thielbar—1

 

Colorado Rockies

Closer: LaTroy Hawkins (8)

Setup #1: Rex Brothers (4), Adam Ottavino (3), Boone Logan (1)

Setup #2: Rex Brothers (3), Adam Ottavino (2), Boone Logan (2), Tommy Kahnle (1)

 

Comment: All eight sources have the 42-year-old LaTroy Hawkins listed as the Colorado closer. In the history of baseball, 42-year-old relievers have had more than 15 saves in a season just three times—1965 Hoyt Wilhelm (20), 1997 Dennis Eckersley (36), and 2013 Mariano Rivera (44). Still, Hawkins has been able to keep runs off the board over the last three seasons despite a low strikeout rate. Rex Brothers is listed most often as next in line, with Adam Ottavino also in the mix. The FanGraphs Depth Charts have eight pitchers projected for at least one save.

 

FanGraphs Depth Charts: Hawkins—6, Brothers—6, Ottavino—5, Axford—5, D. Hale—5, B. Logan—3, Bettis—2, J. Diaz—1

 

San Francisco Giants

Closer: Santiago Casilla (8)

Setup #1: Sergio Romo (4), Jeremy Affeldt (4)

Setup #2: Sergio Romo (4), Jeremy Affeldt (4)

 

Comment: The sources agree that the top three guys in the Giants’ pen will be Casilla, Romo, and Affeldt. They are not in agreement on whether it’s Romo or Affeldt as the primary setup guy. Romo has the closer experience, though, so he should be your handcuff with Casilla in leagues where backup closers are rostered.

 

FanGraphs Depth Charts: Casilla—14, Romo—11, Affeldt—9, J. Lopez—7, Petit—5

 

Milwaukee Brewers

Closer: Francisco Rodriguez (5), Jon Broxton (2), Rob Wooten (1)

Setup #1: Jon Broxton (6), Jim Henderson (1), Will Smith (1)

Setup #2: Will Smith (6), Tyler Thornburg (1), Brandon Kintzler (1)

 

Comment: This isn’t as uncertain as it looks. K-Rod is the closer. The Brewers wouldn’t have signed him if he weren’t going to close. He’s still in the process of obtaining his work visa and the hope is that he gets to camp by the end of the week. For now, MLB.com has Rob Wooten listed as the closer, and ESPN.com and Fantasy Alarm both have Broxton listed as the closer. The reality is that the late inning pitchers for the Brewer will be K-Rod, Broxton, and Will Smith.

 

FanGraphs Depth Charts: Broxton—4

 

Toronto Blue Jays

Closer: Brett Cecil (6), Aaron Sanchez (1), Steve Delabar (1)

Setup #1: Aaron Loup (5), Aaron Sanchez (2), Brett Cecil (1)

Setup #2: Steve Delabar (4), Aaron Loup (2), Aaron Sanchez (1), Brett Cecil (1), Steve Delabar (1)

 

Comment: Cecil has been a very good reliever over the last two years and is expected by the majority of these eight sources to be the main man for saves in 2015. Aaron Sanchez is a terrific young pitcher who may get bumped to the bullpen if there’s no room for him in the rotation. Loup is a setup guy with a mediocre strikeout rate. Delabar was great in 2012 and 2013 but really bad last year. Fox has him listed as the Blue Jays’ closer at the moment, but I would say that’s not bloody likely.

 

FanGraphs Depth Charts: Cecil—37, Loup—4

 

Messy Closer Situations

 

Los Angeles Dodgers

Closer: Joel Peralta (6), Brandon League (2)—filling in for Kenley Jansen (DL)

Setup #1: Brandon League (3), Joel Peralta (2), Pedro Baez (2), J.P. Howell (1)

Setup #2: Pedro Baez (3), J.P. Howell (3), Brandon League (1), Paco Rodriguez (1)

 

Comment: With Jansen currently out with a foot injury, six of eight sources like Joel Peralta to close for the Dodgers, with Brandon League the choice by the other two. When Jansen comes back, Peralta and League should be the top setup guys. The FanGraphs Depth Charts have saves scattered among six guys with J.P. Howell projected for the most.

 

FanGraphs Depth Charts: J.P. Howell—8, Jansen—7, Peralta—7, League—6, Baez—3, Paco Rodriguez—2

 

New York Mets

Closer: Jenrry Mejia (6), Jenrry Mejia/Bobby Parnell (2)

Setup #1: Jeurys Familia (6), Bobby Parnell (2)

Setup #2: Jeurys Familia (2), Vic Black (2), Carlos Torres (2), Josh Edgin (1), Rafael Montero (1)

 

Comment: Mejia is listed by himself as the closer by six sources and he shares the job with Parnell on the lists of two other sources. Mejia had 28 saves last year. Parnell had 22 the year before and is coming back from an injury hoping to reclaim his job. Right now, the FanGraphs Depth Charts have Mejia projected for 21 saves and Parnell projected for 19, so it’s a difficult situation to judge at the moment. Familia would be the guy who is third in line.

 

FanGraphs Depth Charts: Mejia—21, Parnell—19, Familia—2

 

Houston Astros

Closer: Luke Gregerson (5), Chad Qualls (3)

Setup #1: Luke Gregerson (3), Pat Neshek (3), Chad Qualls (2)

Setup #2: Pat Neshek (5), Chad Qualls (2), Josh Fields (1)

 

Comment: Five of eight sources have Gregerson listed as the Astros’ closer, with the other three putting Qualls in that spot. Qualls was the team’s closer last year and he had 19 saves with a 3.33 ERA. You would think the spot would be his to lose, but Gregerson has been the better pitcher over the years so it’s not hard to understand why most people would expect Gregerson to become the closer. Neshek is most likely third in line and Fields is a longshot.

 

FanGraphs Depth Charts: Gregerson—26, Qualls—6, Neshek—6

 

Tampa Bay Rays

Closer: Brad Boxberger (4), Grant Balfour (2), Kevin Jepsen (2)—filling in for Jake McGee (DL)

Setup #1: Brad Boxberger (4), Grant Balfour (3), Kevin Jepsen (1)

Setup #2: Grant Balfour (2), Ernesto Frieri (2), Kevin Jepsen (2), Jeff Beliveau, Kirby Yates (1)

 

Comment: McGee is likely to miss most or all of April. It’s hard to know who will get saves in the meantime. The majority likes Boxberger, but Balfour and Jepsen both have their backers. Balfour had 12 saves last year and 38 the year before, so he has experience as a closer. Boxberger was very effective last year, striking out 14.5 batters per nine innings, but the Rays may want to keep him in a setup role.

 

FanGraphs Depth Charts: McGee—38, Boxberger—3, Balfour—1,

Failing to record an out is a starting pitcher’s worst nightmare. Generally, it means that either the pitcher suffered an injury or had absolutely nothing that particular day. In the case that the pitcher is healthy but eminently hittable, one can only imagine the embarrassment the pitcher feels. Additionally, it’s a pretty big letdown to the pitcher’s teammates. Players underperform from time to time, but perhaps nothing hurts a team as much as a starter who gets rocked and subsequently pulled before retiring a batter. In a matter of minutes, the pitcher’s squad can already be a few runs behind, and the bullpen becomes destined for a long day.

From data available at Baseball-Reference (since 1914), in the regular season, there have been 1,282 instances of starting pitchers leaving the game before recording one out (thanks, Play Index). The first time this occurred, on record, was April 24, 1914. The Cubs’ Charlie Smith faced five batters; he beaned one, allowed three hits, and one counterpart reached on error. The last time it happened was August 7, 2013, when Shelby Miller was yanked after taking a line drive to the elbow off the bat of Dodger’s outfielder Carl Crawford. Read the rest of this entry »

Matt Harvey is getting all the hype these days, touching 99 mph on the gun, throwing nasty 84 mph curves, and looking healthy. I think he will have an excellent year. For some reason though, the world at large is still underrating Jacob deGrom.

First off, I recommend you read this FanGraphs article from midsummer, detailing the changes he made to his pitching mechanics to make this “rags to riches” leap into the upper echelon.

I’ve been notoriously high on deGrom since I watched him pitch. I wrote about him on reddit back in July 2014. I’ll update the numbers I used, infra:

He’s been excellent — and not in any flukey kind of way. deGrom’s pitch types and peripherals support that what he did last year is VERY REAL.

Let me reiterate last year’s line: 140 IP (178.1 IP of usage), 9.2 k/9, 2.69 ERA, 1.14 WHIP, 2.67 FIP, 3.03 xFIP, 3.19 SIERA. Those are top-20 numbers. And unlike phenoms that regress with time (see Jesse Hahn in 2014), deGrom only got BETTER as the innings racked up.

That is what we love to see — for three reasons:

(1) His body can withstand the rigors of a 200 IP season,

(2) He IMPROVED, rather than regressing, and

(3) Hey, for those of us in H2H leagues, we want our guy pitching well for the fantasy playoffs!

His control improved with time, with increased strikeouts. As of my last post, he had an 8.8 k/9 and 2.7 K/BB. He ended year with a 9.3 k/9 and 3.4 K/BB. We love to see improvement in both those respects. Keep the walks down and strikeouts up, and success often naturally follows!

He’s generating a lot of swinging strikes. For reference, the league average sw/str% is approximately 8.6%.

Jacob deGrom has an overall 11.9 sw/str%, which is well above league average. Looking at pitch F/X data, his slider (12.4% sw/str%, 46/370 pitches), changeup (20.2%, 55/272), both fastballs (10.8%, 108/1000), and curveball (16.0%, 34/212) are all above-average, strikeout-quality pitches.

deGrom essentially features a five-pitch arsenal. Of 2,225 MLB pitches thrown:

44.9% (1000/2225) Fastballs averaging 93.5 mph. Max Velocity, 97.3 mph.

16.5% (368/2225) 2-Seam Fastballs averaging 93.2 mph, Max Velocity, 97.4 mph.

16.6% (370/2225) Sliders averaging 86.8 mph, Max Velocity 91.3 mph (adding mph to his slider is a huge part of his success).

12.2% (272/2225) Changeups averaging 83.9 mph.

9.5% (212/2225) Curveballs averaging 79.3 mph.

3 Cutters–not really a pitch he uses.

deGrom has a diverse arsenal of pitches, with some legitimate velocity differentials, and a good fastball, topping out at 97+ mph. He has 7 mph between fastballs and slider. 10 mph between fastballs and changeup. 14+ mph between fastballs and curveball. 22.5 mph between the high-end spectrum of his fastball and low-end spectrum of his curve.

Essentially, deGrom is legit. His peripherals and Pitch F/X data don’t really suggest that he’s due for any significant regression. Citi Field is still an excellent pitcher’s park, despite the fact that the fences were recently moved in (3-11 feet). I don’t think it will make a significant difference; maybe a home run or two leaves the park that wouldn’t have before.

It’s worth noting that his top speeds increased late in the year, logging his highest speed fastball in the second half of the season. Again, I love a pitcher that doesn’t fatigue.

Concerns: He had Tommy John surgery in 2010, but it seems he has worked his way back from that. Sophomore slump or hitters figuring him out are worth considering. And of course, a couple fly ball outs might turn into home runs.

Fearless prediction: 32 games, 210 IP, 2.80 ERA, 1.05 WHIP, 234 Ks (10 k/9) – and deGrom finally gains some respect withing the fantasy baseball community as a top-15 fantasy pitcher. That bold prediction being said, I think he’s being criminally underrated in fantasy drafts, with his ADP of 112 in yahoo leagues.

112! At that price, go ahead and reach.

Hunter Pence has been one of baseball’s most durable players since his first full-time season in 2008. Over the last seven years, Pence has never played fewer than 154 games, and he’s coming off a three-year stretch of 160, 162, and 162 games. He is the active leader in consecutive games played, with 382.

Unfortunately, that streak will end when the Giants open their regular season on April 6^th in Arizona. Pence was hit by a pitch in a spring training game on Thursday and will be out six-to-eight weeks with a broken arm. Of course, in the real world, the important thing for Pence and the Giants is that he heals quickly and gets back on the field as soon as possible. In the fantasy baseball world, it’s natural to wonder how the injury affects his value on draft day.

One of the reasons Pence has been valuable in fantasy baseball over the years has been his durability. He has played almost every day for the last seven seasons, and this has allowed him to accumulate counting stats even if his rate stats are not elite. He’s not a 30-homer guy, rarely a 20-steals guy, and has only hit over .300 once since 2008. He’ll generally score 80- to 90 runs and drive in around 90. He’s scored 100 or more runs one time. He’s driven in 100 or more runs one time.

Consider his average season since 2008:

159 G, 671 PA, 172 H, 88 R, 24 HR, 89 RBI, 13 SB, .280 AVG

That’s solid across-the-board production but without any of the big, round numbers that are so exciting to see (100 runs, 30 homers, 100 RBI, 20 steals, .300 average). An interesting comparison is Carlos Gonzalez. Gonzalez is an elite player, when healthy. When he’s in the lineup, he’s a top 5 guy. Unfortunately, Gonzalez is often not healthy.

Consider the average season for Carlos Gonzalez since 2008:

109 G, 444 PA, 118 H, 69 R, 19 HR, 65 RBI, 16 SB, .294 AVG

Now let’s look at both Pence and Gonzalez since 2008, per 162 games played:

162 G, 686 PA, 176 H,   90 R, 25 HR, 91 RBI, 14 SB, .280 AVG—Hunter Pence

162 G, 662 PA, 177 H, 103 R, 29 HR, 98 RBI, 24 SB, .294 AVG—Carlos Gonzalez

Given the same amount of playing time, Carlos Gonzalez beats Hunter Pence across the board. Gonzalez is the guy that you can dream on to achieve the big, round numbers mentioned above. In the real world, though, despite his inferior statistics on a per-plate appearance basis, Pence has been the more valuable fantasy outfielder because of his durability.

So, what about 2015? How much does Pence’s broken arm affect his fantasy value?

I created dollar values using composite projections from Fantasy411.com (a combination of 12 sources). These projections are based on a 12-team league with 21 players, including 9 active hitters (no MI or CI), 7 active pitchers (2 SP, 2 RP, 3 P), and 5 bench spots. There were 63 outfielders projected for positive value (a little more than 5 per team). Using these projections, a healthy Hunter Pence is projected for the following stats:

644 PA, 159 H, 81 R, 20 HR, 82 RBI, 12 SB, .270 AVG

This puts him #12 among outfielders, but a dollar more in value would move him as high as ninth and a dollar less would drop him to 15^th, so you could say he’s in the 9-15 range when it comes to outfielders. Others in that same range based on these projections are Ryan Braun, Jacoby Ellsbury, Corey Dickerson, Matt Kemp, Justin Upton, and Matt Holliday. With these stats (per this set of projections), Pence would be a late fourth-round pick.

Healthy Hunter Pence

$21

#12 OF (range is from 9 to 15)

Late 4^th round

Comparable to: Ryan Braun, Corey Dickerson, Matt Kemp, Justin Upton

This year we know Pence will miss some time. The initial estimates say six to eight weeks until he’s ready to play. Pence seems to me to be the type of guy who will do whatever he can to get back on the field as soon as possible. In fact, I can’t imagine Pence could sit still for five minutes, let alone an entire baseball game. He’s probably going to drive his teammates crazy.

So let’s say Pence misses the month of April. That leaves him with five months of playing time. Some simple math would suggest the injured Pence will get 83% of the playing time a healthy Pence would get, so we’ll pro-rate his projection above to 83% of the playing time:

535 PA, 132 H, 68 R, 17 HR, 68 RBI, 10 SB, .270 AVG—83% of the season

Losing a month of playing time drops Pence’s value into the mid-30s among outfielders, around such players as Brandon Moss, Denard Span, Marcell Ozuna, and Alex Rios.

Injured Hunter Pence (missing one month of the season)

$9

#36 OF (range is from 33 to 39)

Early 13^th round

Comparable to: Brandon Moss, Denard Span, Marcell Ozuna, Alex Rios

But wait, there’s more! We know Pence will miss time. It could be a couple weeks, it could be a month, it could be a month-and-a-half. We also know that we can replace him for that time, so we can factor in his replacement to get a better value for Pence. If you drop him all the way down to 83% of his projected stats, he drops too far on your cheat sheet and you’ll never acquire him.

Let’s factor in the value of a replacement outfielder for the time Pence is going stir-crazy on the Giants’ bench. Based on the composite projections from Fantasy411, the first five replacement outfielders are Michael Saunders, Michael Morse, Curtis Granderson, Angel Pagan, and Dexter Fowler. If you combine the stats for these five players and pro-rate them to one month’s worth of playing time, you get the following:

87 PA, 20 H, 11 R, 2 HR, 9 RBI, 2 SB, .258 AVG—Pence one-month replacement

Add this to our “83% of the season” numbers for Pence from above:

535 PA, 132 H, 68 R, 17 HR, 68 RBI, 10 SB, .270 AVG—83% of the season

And we get:

622 PA, 152 H, 78 R, 19 HR, 77 RBI, 12 SB, .268 AVG—Pence + Replacement

This batting line moves Pence back up the rankings. He becomes the #20 outfielder, in the range of Alex Gordon, Nelson Cruz, and Jason Heyward.

Injured Hunter Pence + Replacement Player for One Month

$17

#20 OF (range is from 18 to 23)

6^th round

Comparable to: Alex Gordon, Nelson Cruz, Jason Heyward

Of course, your numbers may vary, but the process is the important part. A healthy Hunter Pence is a late 4^th-round pick. An injured Hunter Pence with no replacement is an early 13^th round pick. An injured Hunter Pence with a replacement player for one month is a mid 6^th round pick.

The recent injury to Hunter Pence hurts his value, but he could still be someone to target if other owners shy away from him and he’s still around in the 7^th round or later.

I have every reason to hate Dee Gordon.

Prior to the 2012 season, I found myself struggling to figure out who would get the final keeper slot in a longtime, highly competitive fantasy league I played in. It came down to two players: Mike Trout and Dee Gordon. They both would have cost me the same, but Gordon was coming off a rookie campaign where he batted .304 with 24 steals in a miniscule 224 at-bats. Trout, on the other hand, was heading into 2012 with what seemed to me like a more clouded future. He had just posted a pedestrian .671 OPS with a 22.2 K%–albeit as a 19-year old–the year prior. He was also blocked in LF at the time by the great Bobby Abreu, and was looking at possibly another year of seasoning in the minors. In the end I chose Gordon, and the rest is terrible, nightmare-inducing history.

So how strange that I find myself here now, defending Dee Gordon, the very man who hoodwinked me into choosing him over Mike mother-flippin’ Trout.

Ironically, I think the hate for Gordon has gone a bit too far this year. It’s odd to think that there’s any hate for a guy coming off a season where he led all of baseball in steals while also posting a top-25 batting average of .289. But some people seem awfully down on the guy coming into 2015. Perhaps they too were burned by his 2011 breakout, and refuse to make the same mistake twice. Though I can’t fault them if that is the case, there is reason to believe that Dee Gordon’s days of breaking our hearts are over.

The first thing to point out are his batted-ball rates. As the graph illustrates, there weren’t any earth-shattering changes occurring here. It is worth noting, however, that Gordon set a career high in groundball percentage and a career low in fly-ball percentage. And if you’re willing to consider 2013 an aberration like I am (he only managed 106 plate appearances that year), he has actually been gradually trending in the right direction with both his fly-ball and groundball percentages while maintaining a fairly steady line-drive rate. Spikes in groundball percentages are rarely considered ideal, but when a player has the elite speed Gordon does, the odds of turning a weak dribbler or a grounder towards the hole into a hit get a very favorable bump.

Which brings me to perhaps the most eyebrow-raising aspect of Gordon’s 2014 season: his bunt-hit percentage (BUH%). After averaging a 28.5 BUH% over the prior three seasons, Gordon posted a ridiculous 42.6 BUH% in 2014. To put that number into perspective, here’s how it stacked up against the league’s other elite speedsters:

Bunting for hits is a skill. The fact that his success rate rose by nearly 15% last year tells me that he worked on and dramatically improved this skill. Perhaps more importantly, though, it tells me that he’s keenly aware of how dangerous a weapon this skill can be for him when used effectively. When paired with his declining fly-ball rates–and especially his new career low IFFB% of 8%, down from 13.2%–the numbers start to paint the picture of a player who may have finally begun to consciously tailor his plate approach to his strengths.

While I will never forgive Dee Gordon for what he did to me, I do see reasons to be optimistic about his 2015 season. Should his elite ability to bunt for hits carry over into this season, his .346 BABIP shouldn’t see as much regression as people seem to think, and another year of plus average and a stolen-base crown seems well within his reach.

One of the founding notions of sabermetrics has been the emphasis of the walk. Before sabermetrics, in the dark ages, people hardly paid attention to the walk. Teams would pay players based on there batting average, HR, and RBIs and no one really put a lot of stock on the “scrappy” player who would draw walks and get on base. Sabermetrics essentially started around the mid 1900s and one of their founding principals was that the walk was way undervalued. Now the walk is deemed as an extremely valuable tool, and organizations will often pay a heavy hand for someone with a good walk rate. But what if the value of the walk was dropping, what if a walk in today’s game was not nearly as valuable as it use to be? Baseball you see is a living organism and is prone to change, just because something was valuable in the past, doesn’t mean it’s valuable in the present. We constantly need to be adjusting to the value of certain strategies and skills in order to stay ahead of the game.

This essentially all started when I looked at the correlation between pitches per plate appearance (Pit/PA) and runs scored per game (R/G), for 2014, and found that there was no real correlation (You can find the article here). I therefore decided to expand the data pool, look through a twenty year span to examine if 2014, was an anomaly, part of a consistent trend, or if Pit/PA never really had any correlation with (R/G).

So what I did was, I calculated the correlation coefficient of Pit/PA and R/G dating all the way back to 1994, for each individual year. If you don’t know what correlation coefficient is, or what is a strong or week correlation coefficient, I explain it, in my previous article. Anyways, the data that I found had a high level of variance. I did, however display two labels, the largest correlation coefficient in the last twenty years and the smallest. Why? Because although there is a large variation in the data from year to year, and it wouldn’t be unreasonable to believe that Pit/PA has a much higher correlation to R/G in 2015, it still is displaying a downward trend.

1994 had the highest correlation, while 2014 had the lowest correlation. So at this point you’ve probably noticed the variation and downward trend. Essentially what this tells us is that Pit/PA’s correlation with R/G is basically unpredictable. If your team, for example, sees a lot of pitches, it doesn’t mean that they will have a good offense. In fact if someone says that this team sees a lot of pitches and it’s a good thing, well he’s probably just blurting crap out. This is not to suggest that that individual is wrong, it is rather to suggest that seeing pitches doesn’t have a consistent correlation with runs scored. It is rather difficult then or impractical to come to any conclusion from this data set.

Now, what follows is an examination of similar trends and stronger trends of data. Oh, and I almost forgot, you’re also probably wondering well what about the base on balls, what was the point of that introduction? Well after I looked at the correlation between Pit/PA and R/G, I took a look at the correlation between BB% and R/G for 2014.

This basically shows no distinct correlation between BB% and R/G in 2014. Then I calculated the correlation coefficient to get an exact number, and got R=0.0908. Essentially this displays that there was no correlation between BB% and R/G in 2014.

I therefore ran the numbers again, for 20 years, to see if this was just an abnormality in the data. I also wanted to get a sense of whether there was a specific trend.

 

For this chart I decided to display all the data sets, to give you an idea of what the correlations looked like. The two, however, that I really want you to focus on are the 2012 correlation (R=0.083) and 2014 (R=0.0908) correlation. Both of these years show a significant drop-off in the correlation between BB% and R/G. Before there was always a positive correlation between the two data points, even at times strong correlations. In 2014 and 2012, however, there was essentially no correlation between BB% and R/G.

So what does this mean? Why the sudden drop in data correlation and will it continue? I also found it odd that in 2013, the correlation went all the way back up to R=0.4749, which is not the strongest correlation, but still a good one.

First, however, before we try to answer the two questions I’ve asked, let’s look at another set of correlation data, and that’s the correlation between BB% and OBP. Why? Well my hypothesis was if the correlation between BB% and OBP is getting smaller than naturally the correlation between BB% and R/G would get smaller as well.

As you might be able to tell although less drastic the correlation between BB% and OBP has similar results to the correlation between BB% and R/G. Again the part of the graph, which you should focus on is the two outlier data points. Again they are 2012 (R=0.2317) and 2014 (R=0.3570). This at this point gives us some explanation for the two outlier data points in the previous graph.

Essentially what one needs to understand from this is, since BB% is becoming less correlated with OBP, it’s evidently going to have a lesser correlation with R/G. Since the primary value of a BB is the effect it has on the OBP (obviously though not the only). Also generally and through the 20 years of data there has been a strong correlation between BB% and OBP. Apart from 2012 and 2014 where their correlation is weaker, although still a positive correlation.

So now we need to understand this, if the walk has a small correlation with OBP, then its value will be significantly affected. The problem here is trying to figure out why in 2012 and 2014 there was a sudden drop in its correlation with OBP. My first hypothesis was that it had something to do with the overall BB% of the league.

In hindsight this was probably a simplistic hypothesis. At this point you’ve probably figured out that this was not the answer. Yes, the overall BB% is trending down, just like the previous charts, but the difference is that it doesn’t have the outliers of 2012 and 2014. (I included this to dispel a possible easy assumption to the answer.)

There are in fact several possibilities for the drop in correlation between BB% and OBP. Perhaps it’s the shift, perhaps it’s the low run environment, perhaps it’s high rise in strikeouts. I think another interesting element to look at it is how are hitters doing later in the count. Considering the rise in strikeouts, it’s probably not unreasonable to assume that hitters are performing worse than ever when hitting with two strikes, although this of course is just a hypothesis. The answer to that question is for another study, for another day. What is certain, however, is that this upcoming season will be a fascinating data point. Will the correlations keep getting smaller or are these two data points just truly abnormalities? In any case I think it’s important to consider this, baseball is an ever changing game, and just because something has value one year, doesn’t mean it has value another. Teams need to keep changing and mixing their strategies in order to stay ahead in this wacky game.

Finally, something to note: these data sets are not meant to arrive to any conclusion. I have not arrived at any conclusions about baseball through this data. What it does is, it raises more questions for further and more detailed and elaborate studies. For, example it would be interesting, for Pit/PA to look at it from a pitchers point of view, although I’m not sure that would give us different results. These data sets are also general; they give us a general idea of the situation. Perhaps there are specific teams or players that thrive on seeing a lot of pitches or that do translate a high number of BBs into runs. Also and this might be the most important element to note, correlations aren’t always linked with causation. For example, pop fly’s may have a positive correlation with Pit/PA, that doesn’t mean that pop fly’s caused Pit/PA. What correlations, however, can do is direct us into the right direction to finding the causation. It is a measure or a way of advancing and creating more elaborate and specific research.

So I conclude, now that one has digested all this data, is it time to re-evaluate the value of a walk?

 

All data courtesy of baseball reference.

Brandon Inge, Superstar.

How many wins is chemistry worth? Do nice guys really finish last?

As a Pirates fan since birth, I’ve grown used to my baseball fandom engendering a sense of sympathy in others. Born in 1989, I came of baseball-loving age in the mid-nineties, immediately following the halcyon Bonds/Bonilla/Van Slyke & co. days and immediately preceding the less-halcyon days of the Aramis Ramirez-for-Bobby Hill trade, “Operation Shutdown,” the expansion-drafting of Joe Randa, Pat Meares’ general existence, the Moskos pick, the Matt Morris trade . . . (list of soul-crushingly depressing baseball stories truncated for reader’s mental health).

And yet I remained faithful, despite having no conscious memory of a Pirates team being anything other than heartbreakingly awful. I’ve since likened this experience, in conversations with friends, to Linus sitting in the pumpkin patch each year, waiting for the Great Pumpkin to appear. It sometimes seemed that the Great Pumpkin would never come.

It’s ironic, then, that in the year that finally saw the Great Pumpkin arrive in Pittsburgh (2013), the same city also witnessed the end of the career of one Charles Brandon Inge.

Inge, nicknamed ‘Cringe’ by some of the crueler Pittsburgh faithful for his anemic .181/.204/.238 batting line during the 2013 campaign, was at that point in his thirteenth season as one of baseball’s premiere utility men, playing every position on the diamond during his career. During his peak, he was a slick-fielding third baseman who also clubbed 27 HRs en route to a 4.1 fWAR season in 2006. But by 2013, Inge was 36 and on his way out of the league. Signed before the season to provide depth behind Pedro Alvarez and Neil Walker, Inge’s poor performance eventually led to his unceremonious release by the Pirates at the end of July.

And yet, this article has less to do with Inge’s on-field merits (which, as the previous paragraph suggests, were both significant and significantly variable), and more to do with Inge’s impact off the field. Inge won the 2010 Marvin Miller Man of the Year Award, given to the player whose “performance and contributions to his community inspire others to higher levels of achievement,” for his work with C.S. Mott Children’s Hospital. A frequent visitor to C.S. Mott, Inge also donated $100,000 for a new infusion center to treat pediatric cancer and twice hit home runs for young cancer patients. Dude’s a nice guy.

Perhaps more relevant, though, is pitcher and noted stathead Brandon McCarthy’s statement that Inge and fellow veteran Jonny Gomes had been worth twenty-four wins to the 2012 Athletics through chemistry alone. Normative ethics aside, it’s impossible to measure the moral character of a man—but we can measure, or at least attempt to quantify, the impact he has on his teammates.

Intrigued, I set out to determine whether Inge, patron saint of chemistry and all-around good guy, really made such a gigantic difference to his teammates’ performance. Mine is not the first investigation into this topic—Baseball Prospectus’ Russell A. Carleton examined the same issue in March of 2013, and there have been numerous attempts to place a valuation on chemistry over the years. But as you’ll see, there are some methodological differences to our approaches, and the differences expose some interesting conclusions.

Methodology

There is no ironclad way to assess Inge’s potential effect on his teammates, short of cloning entire teams of players, randomly assigning Brandon Inges to some of them, and having them play a large number of seasons.

In order to determine Inge’s value as accurately as possible, I can’t simply measure his teammates’ performance—I’d just be concluding that Inge played with good or bad teammates. Instead, I need to develop a counterfactual, or a method of estimating how we could’ve reasonably expected Inge’s teammates to play in his absence. Fortunately, an excellent one already exists—a ZiPS projection. ZiPS, to my knowledge, does not have a ‘played with Brandon Inge variable,’ so it should be unbiased. Carleton instead used an AR(1) covariance matrix to try to adjust for player talent, but given that ZiPS explicitly incorporates past performance with a view to projecting, as accurately as possible, how a player will perform in the upcoming season, I believe it is a suitable tool.

I chose wOBA as the dependent variable for our study—while Carleton looked at multiple indicators (BB%, K%, etc), one, all-encompassing measure of players’ offensive performance seems best suited to answering the question, “Do players perform better with Brandon Inge on their team?”

In order to develop the requisite dataset for this analysis, I downloaded every player-season since 2006[1] from FanGraphs’ leaderboards and filtered the data to include only those players who amassed at least 200 plate appearances. This yielded 3130 player-seasons. Next, I created a binary variable called ‘IngeTeammate,’ with a value of ‘1’ if the player was on Inge’s team during the given season (and not Inge himself), and ‘0’ if he wasn’t. For the 2012 season, the only one in which Inge played for multiple teams, I counted Inge as having played for the Athletics, with whom he spent the majority of the season.

The next part was a bit tricky—bringing in the ZiPS projections. The latest years, the ones for which ZiPS has been featured on FG, were easy—data was readily available, wOBA already calculated, and records already associated with a player id. But wading deeper into the past unearthed some issues—in order to match records, I had to manually match player names (including the two Chris Carters, and, apparently, two Abraham Nunezes . . . Nunezii . . . who knows?) and hand-calculate ZiPS-projected wOBA for older player-seasons using the weights provided on the FanGraphs Guts page. One potential issue with some of the oldest data is the lack of projections for things like intentional walks and sacrifice flies.

However, forging through all of the record-matching and manual wOBA-calculating eventually yielded ZiPS wOBA projections matched to 3088 of the 3130 player-seasons. Of the 42 unmatched seasons, only one was an Inge teammate (2010 Brennan Boesch). 81 of the 3088 matched seasons were Inge teammates. So unless you think ZiPS would have pegged Boesch, a relatively unknown 25-year-old at the time, for a significantly better performance than the .322 wOBA he posted in 2010, the unmatched records probably didn’t have a huge effect.

What we’re left with is data that look like this:

Year	Name	Team	Age	PA	IngeTeammate	ZiPS wOBA	wOBAdiff	wOBA
2010	Jose Bautista	Blue Jays	29	683	0	0.322	0.100	0.422
2010	Jim Thome	Twins	39	340	0	0.343	0.096	0.439
2010	Wilson Betemit	Royals	28	315	0	0.302	0.084	0.386
2010	Josh Hamilton	Rangers	29	571	0	0.365	0.080	0.445
2010	Chris Johnson	Astros	25	362	0	0.286	0.067	0.353
2010	Carlos Gonzalez	Rockies	24	636	0	0.350	0.063	0.413
2010	Justin Morneau	Twins	29	348	0	0.387	0.061	0.448
2010	Paul Konerko	White Sox	34	631	0	0.361	0.056	0.417
2010	Joey Votto	Reds	26	648	0	0.383	0.055	0.438
2010	Danny Valencia	Twins	25	322	0	0.299	0.052	0.351
2010	Giancarlo Stanton	Marlins	20	396	0	0.305	0.051	0.356
2010	Miguel Cairo	Reds	36	226	0	0.288	0.051	0.339
2010	Will Rhymes	Tigers	27	213	1	0.288	0.050	0.338
2010	Tyler Colvin	Cubs	24	395	0	0.301	0.050	0.351
2010	Michael Morse	Nationals	28	293	0	0.328	0.049	0.377
2010	Adrian Beltre	Red Sox	31	641	0	0.343	0.048	0.391
2010	Ryan Hanigan	Reds	29	243	0	0.321	0.048	0.369
2010	Yorvit Torrealba	Padres	31	363	0	0.279	0.044	0.323
2010	Matt Joyce	Rays	25	261	0	0.321	0.043	0.364
2010	Aubrey Huff	Giants	33	668	0	0.344	0.043	0.387
2010	Drew Stubbs	Reds	25	583	0	0.295	0.043	0.338
2010	Andres Torres	Giants	32	570	0	0.316	0.042	0.358
2010	Corey Patterson	Orioles	30	341	0	0.274	0.042	0.316
2010	Austin Jackson	Tigers	23	675	1	0.288	0.041	0.329
2010	Brett Gardner	Yankees	26	569	0	0.306	0.040	0.346
2010	Colby Rasmus	Cardinals	23	534	0	0.329	0.040	0.369
2010	Andruw Jones	White Sox	33	328	0	0.323	0.039	0.362

In the above table, wOBAdiff refers to the amount by which the player outperformed his ZiPS wOBA projection. A negative number would indicate that a player underperformed his projection. So Jose Bautista outperformed his 2010 projection by .100—multiplying by 1000 tells us that this was 100 points of wOBA. It was good to be Joey Bats in 2010.

Results

If we look at the mean wOBA deviation (in terms of points of wOBA) Inge teammates and non-teammates experienced from their ZiPS projections, we see the following results:

	Player-Seasons	Total PA	Mean Weighted Diff. (wOBA pts)	Mean Unweighted Diff. (wOBA pts)
Non-Teammate	3007	1,378,732	-3.09	-4.62
Teammate	81	37,965	4.30	4.24

In other words, if we weight by plate appearances, Inge teammates outperformed their ZiPS projections by an average of about 4.30 points of wOBA. All other players underperformed their projections by an average of about 3.09 points. Which might not seem like a lot, but if you were to apply that 7.4 wOBA difference to an average-hitting team over a 6000 PA team-season, that’s roughly 34 runs. So 3.4 wins. Which is, you know, quite a bit. The unweighted version is even more extreme, suggesting that players with lower numbers of PA have outperformed their projections even more when teamed with Inge.

If we simply run a regression including the independent variables IngeTeammate (binary) and age and the dependent variable wOBAdiff (unweighted), we can express the story another way:

wOBAdiff = 0.0127064 + (IngeTeammate* 0.0090544) + (age* -0.0005993)

I included age as a control because ZiPS projections, as you can see from the model above, tended to slightly overproject older players in comparison to younger players, and therefore I needed to consider the possibility that Inge simply benefitted from playing only with young players (he didn’t).

Note that in the model above, 0.001 corresponds to one point of wOBA (i.e. a hitter moving from .323 to .324 would have gained a point of wOBA). The r-squared of the model is absurdly low (0.006), but that’s to be expected—after all, I’m not trying to assert that Brandon Inge is responsible for all or even a significant part of the variation between MLB players’ expected and actual performance. More importantly, the variable ‘IngeTeammate’ is significant at a 98.4% threshold.

Considering the possible influence of aging is interesting, as the Inge difference is even more pronounced among younger players, or those whom he allegedly mentored while playing with the A’s. If we filter the data above to include only players 27 and younger, the table looks like this:

	Player-Seasons	Total PA	Mean Weighted Diff. (wOBA pts)	Mean Unweighted Diff. (wOBA pts)
Non-Teammate	1241	568,944	-0.50	-2.09
Teammate	30	14,298	16.58	17.27

We’re starting to run into some serious sample size issues that make me uncomfortable drawing any particularly bold conclusions, but young players who play with Inge have done really, really well, collectively knocking the snot out of their ZiPS projections. There are problems with extrapolating this to a 6000 PA team-season, given that presumably an entire team won’t be composed of young players, but if one did so the result would be a ridiculous 78.6 runs of additional value.

The table below lists every 27-and-under player season for which the player was an Inge teammate:

Year	Name	Team	Age	PA	ZiPSwOBA	wOBAdiff	wOBA
2008	Matt Joyce	Tigers	23	277	0.275	0.084	0.359
2011	Alex Avila	Tigers	24	551	0.308	0.076	0.384
2013	Jordy Mercer	Pirates	26	365	0.282	0.051	0.333
2010	Will Rhymes	Tigers	27	213	0.288	0.050	0.338
2012	Chris Carter	Athletics	25	260	0.319	0.050	0.369
2011	Brennan Boesch	Tigers	26	472	0.300	0.048	0.348
2007	Curtis Granderson	Tigers	26	676	0.344	0.044	0.388
2010	Austin Jackson	Tigers	23	675	0.288	0.041	0.329
2012	Yoenis Cespedes	Athletics	26	540	0.328	0.040	0.368
2010	Miguel Cabrera	Tigers	27	648	0.399	0.032	0.431
2013	Jose Tabata	Pirates	24	341	0.308	0.032	0.340
2012	Josh Reddick	Athletics	25	673	0.296	0.030	0.326
2013	Andrew McCutchen	Pirates	26	674	0.365	0.028	0.393
2013	Starling Marte	Pirates	24	566	0.317	0.027	0.344
2006	Omar Infante	Tigers	24	245	0.306	0.016	0.322
2008	Curtis Granderson	Tigers	27	629	0.358	0.015	0.373
2009	Clete Thomas	Tigers	25	310	0.302	0.015	0.317
2012	Josh Donaldson	Athletics	26	294	0.286	0.014	0.300
2011	Andy Dirks	Tigers	25	235	0.297	0.011	0.308
2013	Neil Walker	Pirates	27	551	0.328	0.005	0.333
2013	Pedro Alvarez	Pirates	26	614	0.327	0.003	0.330
2006	Curtis Granderson	Tigers	25	679	0.335	0.000	0.335
2009	Miguel Cabrera	Tigers	26	685	0.407	-0.005	0.402
2010	Alex Avila	Tigers	23	333	0.306	-0.007	0.299
2011	Austin Jackson	Tigers	24	668	0.315	-0.010	0.305
2012	Jemile Weeks	Athletics	25	511	0.304	-0.028	0.276
2012	Derek Norris	Athletics	23	232	0.304	-0.029	0.275
2006	Chris Shelton	Tigers	26	412	0.380	-0.033	0.347
2013	Travis Snider	Pirates	25	285	0.310	-0.039	0.271
2008	Miguel Cabrera	Tigers	25	684	0.419	-0.043	0.376

It’s not as if one year is hugely skewing the results—pretty much every year, whichever young players happen to be playing with Brandon Inge outperform their projections. The graph below illustrates the mean wOBA differential younger Inge teammates exhibited each season. I would’ve imagined, prior to viewing these results, that Inge’s positive ‘effect’ might’ve been almost entirely a product of the 2012 Athletics, but this doesn’t seem to be the case—outside of the 2006 Tigers (when Omar Infante, Curtis Granderson, and Chris Shelton collectively underperformed their ZiPS projections by a modest average of ~5 points of wOBA), Inge’s younger teammates have outperformed ZiPS every single year in the sample.

Perhaps, one could say, Inge has simply benefitted from playing on teams run by intelligent front offices. After all, the Tigers, Athletics, and (more recently) the Pirates all have reputations as relatively savvy management teams. Maybe they’re just collectively able to out-forecast ZiPS.

When we look at ZiPS wOBA differentials by team, however, the Tigers (+1.36 points of wOBA), Athletics (+0.11) and Pirates (-0.31) all had weighted mean differentials less than the Inge gap. The average over all teams was -2.89, so while all three front offices ‘beat the market,’ so to speak, they still don’t explain the huge Inge effect. It looks as though there’s something here.

After observing the results for Inge, I was curious about whether other veteran players might also exhibit similar correlations—while we’d expect to find no correlation with ZiPS wOBA differential for most players, it might be the case that, as with Inge, patterns emerge. Specifically, I looked at two players with diametrically opposite reputations—A.J. Pierzynski and Jonny Gomes. Below, I replicate the initial summary table used for the Inge analysis and note the magnitude of the effect:

A.J. Pierzynski

	Player-Seasons	Total PA	Mean Weighted Diff. (wOBA pts)	Mean Unweighted Diff. (wOBA pts)
Non-Teammate	3004	1,375,450	-2.75	-4.29
Teammate	84	41,247	-7.65	-7.87

The game’s most hated player didn’t fail to disappoint, as his teammates collectively underperformed their ZiPS projections by an additional of 4.9 points of wOBA when compared to non-teammates, an effect worth -22.6 runs to the team over the course of a full season. I should note that I assigned Pierzynski to the 2014 Red Sox (with whom he spent considerably more time) instead of the 2014 Cardinals—both teams underperformed their ZiPS projections, but the Red Sox did so by a larger margin.

Pierzynski’s unweighted results, while still negative, are less damning, and using a regressed model reflects this:

wOBAdiff = 0.0128794+ (AJTeammate* -0.0033689) + (age* -0.0005939)

The intercept and coefficient for age are, understandably, almost identical to those I observed in the Inge model. The significance level for AJTeammate, however, is only 64.1%, suggesting that we can’t really conclude much of anything with the same level of confidence as for Inge.

Still, twenty-plus runs is a non-negligible amount, and Pierzynski’s numbers have been negative across all four teams for whom he’s played (White Sox, Rangers, Red Sox, Cardinals). It may be that more historical data would reveal a broader trend, given that we’ve limited our sample size to only the latter half of Pierzynski’s career.

Jonny Gomes

	Player-Seasons	Total PA	Mean Weighted Diff. (wOBA pts)	Mean Unweighted Diff. (wOBA pts)
Non-Teammate	3000	1,376,613	-3.05	-4.56
Teammate	88	40,084	2.58	1.52

The phenomenally-bearded Gomes, Inge’s running partner in the Brandon McCarthy quote that triggered this analysis, also appears to be a potential chemistry star, though his results are less extreme than Inge’s. His teammates outperformed non-teammates by 5.6 points of wOBA, worth an estimated 26 runs per season.

wOBAdiff = 0.0124387+ (GomesTeammate* 0.0055032) + (age* -0.0005873)

The effect, as with Pierzynski, is not statistically significant—the significance level is 87.4%.

Conclusions

We can’t make firm statements about causality from this analysis, but we can say pretty conclusively that being on the same team as Inge during the last nine years correlates positively with hitting better than ZiPS projects you to hit.

Maybe you don’t believe Inge should get credit for the extra 3.4 wins of value each year. We don’t have a ‘chemistry above replacement’ metric to account for the fact that some other player with a modicum of veteranosity might plausibly have a positive effect if analyzed the same way. And there’s no feasible way to develop one on the horizon—you can only start to do this sort of analysis retrospectively, and it requires a large number of plate appearances and player-seasons before we can conclude that any pattern has emerged. I’m not really arguing that Inge deserves all the credit for his teammates’ overperformance, only that we have reason to believe a nonzero effect may exist.

But let’s entertain, for a minute, the possibility that the 3.4 win-per-season gap we see *is* entirely attributable to Inge. That maybe all the minute, unnoticed interactions between players over the course of a season can add up to improved performance at the plate. The effect could even be greater than 3.4 wins—I didn’t examine pitching and fielding at all. After all, everything we know about human psychology suggests that happier workers are more productive, and I’ve yet to hear any compelling reason that ballplayers constitute an exception. We sometimes, in the analytics community, fall into the trap of assuming that because we can’t measure something accurately, it doesn’t deserve a meaningful place in our analysis. And yet our inability to measure a phenomenon is not proof of its nonexistence—just ten years ago, we lacked meaningful metrics for catcher framing, for instance.

Perhaps Inge contributed more hidden value over the last decade than anyone this side of Jose Molina, and Brandon McCarthy’s twenty-four wins were, if still hyperbole, grounded in a subtle truth. 3.4 wins currently has a market value north of $20M, making Inge a substantially underpaid man over the course of his career.

It’s a shame, on some level, that it’s only after he’s retired that we recognize the unheralded Inge for who he might secretly have been: Brandon Inge, Superstar.

 

[1] Before 2006, I struggled to find ZiPS projections in a readable format to develop the counterfactuals.

Data retrieved from FanGraphs and Baseball Think Factory.