Building a Closer Through Outcomes

There are a sundry of middle/set-up relievers that can succeed in the closer role if given the opportunity. A few obvious: Wade Davis if something happens to Greg Holland… Wil Myers for James Shields and who?!; Ken Giles if the Phillies can somehow find a trade partner for the grundle-grabber; and Brad Boxberger if not Jake McGee. I assume David Robertson signs elsewhere and Dellin Betances steps in.

Lets’ look at potential closers using reliever outcomes. Here are the average contact and balls in play-related outcomes for all relievers that qualified and specifically relievers with 10+ saves:

Command:	K%	BB%	K-BB%	Ct%	SwStr%	Zn%	F-Str%
AVG for RP w/ > 10 SV	0.27	0.07	0.19	0.75	0.12	0.46	0.63
SD for RP w/ > 10 SV	0.08	0.03	0.08	0.06	0.03	0.04	0.04
AVG for all qualified RP’s	0.22	0.09	0.14	0.77	0.11	0.45	0.60
SD for all qualified RP’s	0.07	0.03	0.07	0.05	0.03	0.04	0.05

Balls In Play:	GB%	FB%	IFFB%	GB/FB	HR/FB	LOB%	BABIP
AVG for RP w/ > 10 SV	0.43	0.37	0.11	1.40	0.08	0.78	0.275
SD for RP w/ > 10 SV	0.11	0.10	0.04	1.02	0.05	0.08	0.042
AVG for all qualified RP’s	0.45	0.35	0.09	1.50	0.09	0.75	0.289
SD for all qualified RP’s	0.10	0.09	0.05	0.81	0.05	0.08	0.044

Our Filters: 

• Contact-related outcomes: For K-BB%, Ct% and SwStr%, I filtered simply by the general relief pitcher averages. Everyone below average in these 3 categories was filtered out.
• Command-related outcomes: For zone% and first-pitch-strike%, I used 1 standard deviation below average and 1.25 SD’s below average in BABIP as filters. 1.25 SD allowed me to omit only the relievers that had career BABIP’s higher than we would like to see for a closer or in general. I didn’t want to screen out Jenrry Mejia (1.24 SD below the mean) or Tim Stauffer (1.19 SD below), because there’s a possibility for BABIP regression.
• Balls In Play-related outcomes: I was lax on the balls in play outcomes. I went with a 40% Grounder rate and 45% Flyball rate as my filters versus the averages that you see above because below average fly-rates don’t mean much in places like Tampa (where Boxberger is elite but below average in fly-rates); and above average grounder-rates don’t mean as much with atrocious defense behind you (hence Corey Kluber’s unlucky BABIP, which should have been closer to .299 per end-of-season xBABIP/Inside Edge data), but I digress.

Using these filters, we’re left with a robust list of above-average relievers beyond just closers (scroll down for the noted filters):

What happens if we use the command (K-BB%)/contact (Ct% and SwStr%) related averages for relievers with more than 10 saves this year? 

…We’re left with some elite closers and then a few interesting names.

Last year, Danny Farquhar (just missed the list this year) had a top 20 swinging-strike rate – about a percent better than Fernando Rodney, but it was masked by his BABIP and left-on-base rate that killed his surface stats (4.20 ERA vs. 2.40 xFIP). This year, he actually outperformed his xFIP with a 2.66 ERA. After an early season MASH Report on Rodney’s velocity, I eyed Farquhar. At least keep him in mind next year if anything does happen to Rodney.

Josh Edgin (Mets for those of you that don’t know) has a top 65 contact-rate sandwiched between Mark Melancon and Jake McGee and even induced grounders 50% of the time. He has a pretty extensive repertoire as well. In order of usage: Fourseamer, Slider, Curve, Change, Cutter and Sinker. According to his Brooks Player Card, he has great swing and miss rates on his Curve (>56%), Cutter (50%), Slider (>42%) and Sinker (33%). Even his Change approaches 30%. This isn’t the case on his Fastball, but at 93+ MPH, it induces a decent amount of grounders (1.8 GB/FB). Keep in mind he had late-season elbow issues which effected his velocity by a MPH or so, but he could be called upon to get Mejia out of a jam. I like him better than an unhealthy Bobby Parnell and Jeurys Familia from a command perspective for another year.

Zach Duke did his best Craig Kimbrel impression prior to the R2M monster hitting him in August. Prior to 8/1, Duke had a 34.9% K-rate and 27.3 K-BB%. Kimbrel ended the year with a 38.9% K-rate and 28.3 K-BB%. I think August and September brought him back to his realistic value (~2.50 ERA, 1.15 WHIP). It will be interesting to see who closes for the Brewers if they let Francisco Rodriguez go. Both Duke and Will Smith have above average (even for RP w/ 10+ saves) swing-and-miss. Will Smith should have additional command next year, but Zack Duke induces grounders better which I like in my closers/in Milwaukee. They also have Jonathan Broxton. The hierarchy seemed to be K-rod-Broxton-Smith late last season. If that’s the case, they should use Duke more (former starter) and in higher leverage situations. He was equally solid against both lefties (.258 wOBA) and righties (.262 wOBA).

Oliver Perez everybody! I thought I could filter him out by his splits being a lefty, but like 2012, he was more effective vs. righties (and faced 44 more of them). The D-backs have Addison Reed, up-and-comer Evan Marshall as well as Daniel Hudson caught touching 97 MPH so if not by outcomes or splits, we can filter Perez out by opportunity.

Darren O’Day and Andrew Miller is part of one dominating Baltimore bullpen – one that gets referenced by anyone who thinks the Orioles can beat the Tigers in the ALDS. Notice that Zach Britton didn’t make either of the above lists! He was filtered out by his below average K-BB% (13.70%). It’s his 75+% grounder rate (hence the 81+% left-on-base rate and .215 BABIP) that keeps him elite in Baltimore. The only concern you can have with O’Day is a fastball velocity almost 2 standard deviations below the mean for relievers, but his arm angle combined with that slider still induces a 30+% whiff-rate on both pitches. Miller’s slider though is a world apart from O’Day’s: only Pedro Strop, Will Smith, Jake Diekman, Greg Holland and Oliver Perez induces more whiffs than Miller’s 55% according to Baseball Prospectus’ Pitchf/x Leaderboards.  I doubt we’ll see a closer-transition next year in Baltimore unless Britton’s GB/FB ratio takes a drastic dive because his HR/FB ratio, which approached 18%, could be an issue.

An xBABIP review

On the last day of the season, @jeffwzimmerman provided me with Pitch xBABIP based on inside edge data. Let’s look at some of the bigger xBABIP differentials to keep in mind:

The last column depicts the z-score for BABIP differential. Francisco Rodriguez was expected to have a BABIP about 120 points above his actual BABIP. I highlighted (red/bad; green/good) the xBABIP z-scores as well so that you know whether or not to actually be concerned meaning sure Aaron Sanchez has the 5th biggest BABIP differential (over 2 standard deviations from the mean), but a .239 xBABIP is still utterly elite (3.34 SD’s from the mean). On the other side of the equation, it’s nice to see Evan Marshall, Carlos Martinez and Adam Ottavino (albeit in Colorado) with large BABIP differentials. Marshall and Martinez even have xBABIP’s over .5SD from the mean.

The last bit of fun

It was a very fun year to be doing bullpen reports for RotoGraphs. Aroldis Chapman broke the single-season strikeout rate of 2012 Craig Kimbrel (50.2%). He struck out 52.5% of the hitters he faced. Andrew Miller (42.6%) and Brad Boxberger (42.1%) also made the top 10 seasons ever. Dellin Betances (39.6%), Wade Davis (39.1%) and Craig Kimbrel (38.9%) made the top 20. Chapman’s swinging-strike% of 20% beat ’12 Kimbrel by .8%, but he couldn’t pass ’04 Lidge, ’03 Gagne, ’04 Gagne, ’02 Gagne or ’05 Lidge. Chapman, Miller, Doolittle, Boxberger, Betances (Wade Davis and Kenley Jansen close behind) all had historical, top 20 K-BB rates. Relievers dominate this list: only ’99 Pedro Martinez (#12), ’00 Pedro Martinez (#21), ’01 Randy Johnson (#27) and ’01 Pedro Martinez (#28) make it into the top 30, but it’s clear that we have a growing list of elite relievers.

From a fantasy perspective, thanks to 45+ saves totals out of Holland and Kimbrel, we had two relievers ranked in the top 20 pitchers. If Chapman didn’t miss time and Betances and Davis consumed the closer role, we would have had 3 others. Last year, Craig Kimbrel and his 4 wins, 50 saves, 98 SO’s, 1.21 ERA and .88 WHIP campaign made him the 3rd most valuable pitcher. This year with Kershaw, Cueto, Felix and Kluber, it would have taken even more.

If we combined the 3 more dominating performances exclusive of saves this year: Aroldis Chapman’s K-rate (52.5%) and saves total (36), Dellin Betances IP (90) – who was dominating in his own right, Wade Davis’ ERA (1.00) and Wins total (9) and Sean Doolittle’s WHIP (.73) – let’s call this guy Aroldellin Dooldavis, we would wind up with a 10.95 z-sum…just above Corey Kluber (10.72), but under Clayton Kershaw (13.74), Johnny Cueto (12.95) and Felix Hernandez (12.50). Even 50 saves wouldn’t have done the trick (12.43 z-sum):

Name	Age	IP	WHIP	zWHIP	ERA	zERA	W	zW	SO	zSO	SV	zSV	5×5
Clayton Kershaw	26	198.1	0.86	4.23	1.77	3.53	21	3.38	239	2.95	0	-0.34	13.74
Johnny Cueto	28	243.2	0.96	3.91	2.25	3.22	20	3.16	242	3.01	0	-0.34	12.95
Felix Hernandez	28	236	0.92	4.29	2.14	3.37	15	2.06	248	3.12	0	-0.34	12.50
Aroldellin DoolDavis	25	90	0.73	2.50	1	2.25	9	0.74	177	1.77	50	5.17	12.43
Corey Kluber	28	235.2	1.09	2.14	2.44	2.69	18	2.72	269	3.52	0	-0.34	10.73
Adam Wainwright	32	227	1.03	2.79	2.38	2.72	20	3.16	179	1.80	0	-0.34	10.13
Jon Lester	30	219.2	1.1	1.88	2.46	2.46	16	2.28	220	2.59	0	-0.34	8.86
David Price	28	248.1	1.08	2.40	3.26	0.89	15	2.06	271	3.56	0	-0.34	8.56
Chris Sale	25	174	0.97	2.68	2.17	2.43	12	1.40	208	2.36	0	-0.34	8.52
Madison Bumgarner	24	217.1	1.09	1.97	2.98	1.35	18	2.72	219	2.57	0	-0.34	8.27
Zack Greinke	30	202.1	1.15	1.18	2.71	1.78	17	2.50	207	2.34	0	-0.34	7.46
Max Scherzer	29	220.1	1.18	0.94	3.19	0.93	18	2.72	252	3.20	0	-0.34	7.45
Jordan Zimmermann	28	199.2	1.07	2.02	2.66	1.85	14	1.84	182	1.86	0	-0.34	7.22
Julio Teheran	23	221	1.08	2.13	2.89	1.57	14	1.84	186	1.94	0	-0.34	7.13
Stephen Strasburg	25	215	1.12	1.61	3.14	1.01	14	1.84	242	3.01	0	-0.34	7.12
Garrett Richards	26	168.2	1.04	1.96	2.61	1.64	13	1.62	164	1.52	0	-0.34	6.39
Greg Holland	28	62.1	0.91	1.11	1.44	1.28	1	-1.02	90	0.10	46	4.73	6.21
Craig Kimbrel	26	61.2	0.91	1.09	1.61	1.16	0	-1.24	95	0.20	47	4.84	6.06