How the Best Pitching Tools Translate to the Majors

Intermittently, over the past month or so, the present author — leaning heavily on historical data from Baseball America — has examined the ways in which prospects distinguished for possessing certain tools as minor-leaguers have ultimately fared at the major-league level. The goal: ideally, to develop a better sense of what does and doesn’t correlate to future success, with a view towards better assessing contemporary prospects.

The first of these posts considered the 2005 “class,” as it were, of best-tool prospects and their respective major-league futures; the second post, that collection of prospects from 2005 to -09 who had been recognized both for their hit tool and plate discipline simultaneously; and the third, that subset of the best-hitting, most-disciplined prospects who had also been recognized for their defensive acumen.

While no immutable conclusions have been reached by means of this brief series, it’s also fair to say that certain themes have presented themselves. Like, for example: prospects with readily apparent hitting and plate-discipline skills are more likely to succeed than other manner of prospects (like those, for example, distinguished for power or speed or athleticism). And like also, for example: prospects who pair those sorts of offensive skills with promising defensive abilities become, almost with exception, above-average major-leaguers — regardless of where they’ve appeared on a top-100 list.

What has been absent from the three aforementioned posts, however, has been any consideration of pitchers — which type of baseball player the reader will immediately recognize as a pretty important type of baseball player. To remedy this oversight, I’ve composed this post, which at least begins to examine the relationship between certain pitcher tools and how they translate to the majors.

Pitcher tools, if I might use technical language for a moment, are a little bit kind of weird. While the tools utilized to describe field-playing prospects generally provide a decent portrait of the player in question, those relevant to pitchers — or, at least those presented in Baseball America’s annual handbook — concern repertoire almost exclusively.

Here they are, as follows:

• Best Fastball
• Best Curveball
• Best Slider
• Best Changeup
• Best Control

Four of those five tools are merely different offerings within a pitcher’s repertoire — and two of them, the curve and the slider, exist along a spectrum of similarly shaped pitches. (See conversation regarding Craig Kimbrel’s breaking ball for some idea of the difficulties inherent to distinguishing between the two.)

Despite the peculiarities or seemingly narrow scope of the commonly used pitching tools, I’m also not sure how one would improve upon them. I’m also not prepared to give the matter much constructive thought at the moment, on account of the priority — in this post, at least — is merely to examine how the tools as presently classified have fared in the majors.

As with the first version of these best-tools posts, what I did was to begin by recording which pitchers from the 2005 edition of BA’s Prospect Handbook were distinguished for possessing the aforementioned traits within their respective organizations.

After assembling that list of all the Best Tool pitchers from each of the 30 organizations, I produced five separate custom leaderboards with the metrics that might be most relevant to assessing the quality of a major leaguer. That information appears below, in a number of forms and accompanied by mediocre commentary.

While, as noted in previous posts, one doesn’t expect talent to have been distributed evenly among every minor-league system — and, accordingly, can’t expect the pitcher with the best fastball in a talent-poor system to match the skills of the pitcher with the best fastball in a talent-rich one — the value of the Best Tool designations is that they function as a reasonable proxy for more sophisticated data that isn’t available publicly.

Below are five leaderboards, each containing the pitchers who (a) were distinguished by Baseball America for possessing one of the five relevant tools named above and (b) also recorded at least a single inning in the majors. Players are sorted by career RA9-WAR to date — i.e. WAR as calculated by means of runs-allowed instead of FIP. Also included are the number of pitchers demonstrating the relevant tool to have graduated to the majors and the number of those pitchers to have recorded at least 5.0 RA9-WAR over the course of their respective career.

Best Fastball
Graduated to Majors: 27
Number Above 5.0 RA9-WAR: 7

Name	G	GS	GS%	IP	K%	BB%	GB%	FIP-	ERA-	WAR	RA9-WAR
Felix Hernandez	270	270	100.0%	1830.7	22.7%	7.0%	54.0%	79	78	41.4	42.8
Ervin Santana	268	265	98.9%	1686.7	18.6%	7.4%	39.8%	105	100	19.6	22.1
Jonathan Papelbon	529	3	0.6%	562.3	29.0%	6.4%	37.7%	63	57	17.9	18.8
Scott Kazmir	210	209	99.5%	1187.3	22.5%	10.1%	39.2%	97	98	19.0	17.1
Edwin Jackson	266	236	88.7%	1449.3	17.6%	8.9%	44.8%	101	106	17.8	13.3
Homer Bailey	144	144	100.0%	857.3	19.4%	7.6%	43.8%	99	106	10.4	8.0
Santiago Casilla	385	0	0.0%	383.3	20.2%	10.7%	48.1%	101	84	0.9	5.1
Matt Lindstrom	436	0	0.0%	388.7	18.4%	8.5%	48.8%	80	85	4.9	3.5
Chris Ray	283	0	0.0%	281.0	19.1%	10.2%	39.3%	102	94	1.0	2.8
Brandon League	436	0	0.0%	471.0	17.0%	8.0%	59.6%	97	93	1.9	2.0
Anthony Reyes	67	52	77.6%	293.3	16.1%	9.3%	35.9%	118	118	1.0	1.6
Angel Guzman	88	14	15.9%	157.0	20.8%	10.6%	40.3%	100	105	0.9	1.2
Jason Bulger	125	0	0.0%	133.0	24.2%	13.1%	40.5%	104	101	0.2	0.6
Eude Brito	11	7	63.6%	40.3	13.3%	12.7%	39.8%	114	119	0.2	0.2
Sean Tracey	7	0	0.0%	8.0	8.8%	14.7%	32.0%	168	71	-0.1	0.1
Travis Chick	3	0	0.0%	5.0	6.5%	32.3%	47.4%	191	287	-0.1	-0.2
Jose Capellan	99	2	2.0%	123.3	18.1%	9.2%	33.5%	113	110	-0.4	-0.2
J.D. Durbin	23	11	47.8%	72.7	13.4%	12.5%	48.2%	106	135	0.3	-0.4
Thomas Diamond	16	3	18.8%	29.0	26.1%	13.0%	29.1%	119	165	0.0	-0.5
Juan Morillo	9	1	11.1%	10.7	14.3%	12.5%	26.3%	216	287	-0.4	-0.5
Merkin Valdez	74	1	1.4%	72.7	17.6%	12.5%	43.9%	117	134	-0.3	-0.8
Frankie de la Cruz	26	1	3.8%	32.0	12.7%	16.6%	52.3%	136	192	-0.3	-0.9
Ezequiel Astacio	28	14	50.0%	86.7	18.2%	7.8%	37.5%	146	142	-0.9	-0.9
Yorman Bazardo	25	8	32.0%	60.3	13.1%	12.1%	44.8%	109	162	0.2	-1.1
Collin Balester	73	22	30.1%	185.0	17.5%	9.5%	40.1%	133	128	-1.1	-1.1
Anthony Lerew	20	11	55.0%	61.3	13.8%	11.0%	36.1%	170	175	-1.2	-1.1
Denny Bautista	131	21	16.0%	223.3	17.4%	12.7%	44.5%	108	133	0.8	-1.6
Average	150	48	32.0%	396.0	17.6%	11.4%	41.8%	118	128	4.9	4.8

Best Curveball
Graduated to Majors: 23
Number Above 5.0 RA9-WAR: 11

Name	G	GS	GS%	IP	K%	BB%	GB%	FIP-	ERA-	WAR	RA9-WAR
Justin Verlander	267	267	100.0%	1778.0	22.7%	7.4%	40.1%	79	79	44.2	43.2
Felix Hernandez	270	270	100.0%	1830.7	22.7%	7.0%	54.0%	79	78	41.4	42.8
Matt Cain	267	266	99.6%	1726.0	20.2%	8.2%	37.4%	92	84	28.9	36.2
Adam Wainwright	249	186	74.7%	1321.7	20.9%	6.3%	49.4%	79	78	28.1	30.5
Ubaldo Jimenez	213	212	99.5%	1281.7	21.5%	10.5%	47.4%	88	91	23.5	21.2
John Danks	181	181	100.0%	1109.7	17.8%	7.5%	42.4%	98	95	16.6	17.8
Gio Gonzalez	160	154	96.3%	936.3	22.9%	10.4%	47.0%	92	89	15.0	16.3
Gavin Floyd	199	187	94.0%	1151.3	18.3%	7.9%	44.7%	99	102	15.6	14.0
Jason Hammel	216	159	73.6%	996.0	16.6%	7.9%	45.1%	99	109	12.9	8.0
Homer Bailey	144	144	100.0%	857.3	19.4%	7.6%	43.8%	99	106	10.4	8.0
Bobby Jenks	348	0	0.0%	357.3	23.4%	8.2%	53.6%	71	78	8.1	7.5
Rich Hill	181	70	38.7%	465.7	21.9%	10.9%	35.1%	97	106	5.9	4.2
Manny Delcarmen	298	0	0.0%	292.7	19.5%	10.6%	46.3%	89	87	3.1	3.3
Taylor Buchholz	158	27	17.1%	311.0	17.7%	6.6%	41.9%	95	95	3.1	2.6
Adam Loewen	35	29	82.9%	164.0	17.9%	14.2%	49.3%	106	118	1.7	0.8
Dustin Nippert	119	23	19.3%	268.0	17.6%	11.2%	38.6%	107	116	1.2	0.7
J.D. Martin	24	24	100.0%	125.0	12.3%	6.3%	36.9%	130	105	-0.1	0.6
Charlie Morton	110	109	99.1%	595.3	14.9%	8.8%	54.7%	108	119	4.3	0.4
David Purcey	111	21	18.9%	206.0	18.8%	12.8%	33.9%	108	116	1.1	0.3
Christian Garcia	13	0	0.0%	12.7	31.3%	4.2%	28.6%	96	54	0.1	0.3
J.D. Durbin	23	11	47.8%	72.7	13.4%	12.5%	48.2%	106	135	0.3	-0.4
Ben Hendrickson	14	12	85.7%	58.3	13.2%	10.4%	52.2%	109	169	0.3	-1.0
Denny Bautista	131	21	16.0%	223.3	17.4%	12.7%	44.5%	108	133	0.8	-1.6
Average	162	103	63.6%	701.8	19.2%	9.1%	44.1%	97	102	11.6	11.1

Best Slider
Graduated to Majors: 22
Number Above 5.0 RA9-WAR: 9

Name	G	GS	GS%	IP	K%	BB%	GB%	FIP-	ERA-	WAR	RA9-WAR
Ervin Santana	268	265	98.9%	1686.2	18.6%	7.4%	39.8%	105	100	19.6	22.1
Jonathan Papelbon	529	3	0.6%	562.1	29.0%	6.4%	37.7%	63	57	17.9	18.8
Chad Billingsley	219	190	86.8%	1175.1	20.6%	9.8%	46.3%	91	92	17.2	18.1
Scott Kazmir	210	209	99.5%	1187.1	22.5%	10.1%	39.2%	97	98	19.0	17.1
Huston Street	516	0	0.0%	533.0	25.3%	6.4%	37.5%	77	70	9.9	12.4
Jesse Crain	532	0	0.0%	532.0	19.8%	9.3%	43.1%	88	70	6.4	10.5
Tom Gorzelanny	236	121	51.3%	820.1	18.2%	9.8%	41.5%	105	105	6.8	6.8
Ramon Ramirez	423	0	0.0%	433.2	19.8%	10.3%	41.6%	89	81	4.6	5.6
Chad Qualls	664	0	0.0%	658.1	18.2%	6.6%	57.8%	91	92	3.8	5.5
Brian Bannister	117	114	97.4%	667.1	13.2%	7.7%	42.3%	109	115	6.8	3.9
Brad Hennessey	148	44	29.7%	360.2	12.0%	9.2%	45.2%	114	108	0.6	2.5
Will Ohman	483	0	0.0%	353.0	21.8%	10.3%	40.7%	98	98	1.4	1.7
Bill Bray	258	0	0.0%	197.1	21.9%	10.3%	37.1%	91	88	1.2	1.6
Scott Olsen	130	127	97.7%	723.0	16.6%	9.2%	40.2%	113	113	3.9	1.5
Jim Miller	48	0	0.0%	64.2	19.9%	12.9%	34.6%	114	69	-0.1	0.9
Dana Eveland	114	61	53.5%	392.2	14.2%	10.7%	50.3%	105	129	3.1	0.0
Chris Oxspring	5	0	0.0%	12.0	22.5%	12.2%	35.5%	124	97	-0.1	-0.2
Travis Hughes	24	0	0.0%	25.2	12.5%	12.5%	46.0%	172	148	-0.7	-0.3
Macay McBride	132	0	0.0%	103.1	20.7%	13.5%	45.0%	88	98	0.9	-0.5
Zack Segovia	9	1	11.1%	15.1	8.6%	10.0%	46.4%	125	190	-0.1	-0.5
Clint Nageotte	16	5	31.3%	41.2	12.3%	14.2%	57.5%	129	176	-0.1	-1.0
Denny Bautista	131	21	16.0%	223.1	17.4%	12.7%	44.5%	108	133	0.8	-1.6
Average	237	53	22.3%	489.2	18.4%	10.1%	43.2%	104	106	5.6	5.7

Best Changeup
Graduated to Majors: 18
Number Above 5.0 RA9-WAR: 4

Name	G	GS	GS%	IP	K%	BB%	GB%	FIP-	ERA-	WAR	RA9-WAR
Cole Hamels	245	244	99.6%	1596.7	23.2%	6.1%	43.2%	85	82	30.8	35.5
Shaun Marcum	188	161	85.6%	995.0	19.4%	7.3%	38.4%	101	94	13.0	16.5
Jeff Francis	228	216	94.7%	1249.0	15.3%	6.8%	44.7%	95	108	18.1	12.3
Zach Duke	216	169	78.2%	1086.7	11.9%	6.2%	48.9%	102	110	10.1	6.7
Juan Dominguez	32	17	53.1%	109.7	14.9%	8.7%	43.9%	110	98	1.0	1.2
Mike Neu	33	0	0.0%	46.0	10.4%	13.2%	52.9%	111	83	0.0	0.3
Pat Misch	78	24	30.8%	200.7	13.2%	6.5%	44.7%	114	116	0.4	0.2
Cesar Jimenez	62	3	4.8%	65.3	16.5%	10.5%	37.3%	111	121	0.4	0.2
Matt DeSalvo	9	6	66.7%	29.7	7.8%	13.1%	34.5%	125	172	0.1	-0.1
Mike Gosling	58	9	15.5%	117.0	13.4%	13.1%	39.1%	128	108	-0.6	-0.1
Jason Windsor	4	3	75.0%	13.7	9.2%	7.7%	40.4%	119	148	0.1	-0.3
Blake Hawksworth	124	8	6.5%	183.3	15.7%	8.5%	49.6%	116	106	-0.5	-0.3
Abe Alvarez	4	1	25.0%	10.3	9.4%	13.2%	25.0%	224	243	-0.4	-0.4
Mitch Talbot	43	41	95.3%	232.7	12.3%	10.3%	45.8%	124	132	0.5	-0.5
Julio DePaula	16	0	0.0%	20.0	8.1%	10.1%	54.5%	174	192	-0.4	-0.6
Kyle Davies	151	144	95.4%	768.0	15.7%	10.6%	39.1%	112	129	5.3	-1.1
Chad Orvella	69	0	0.0%	82.3	16.3%	13.1%	35.5%	129	132	-0.7	-1.7
Hayden Penn	33	15	45.5%	82.3	12.4%	13.4%	43.9%	153	218	-0.9	-3.5
Average	89	59	66.6%	382.7	13.6%	9.9%	42.3%	124	133	4.2	3.6

Best Control
Graduated to Majors: 24
Number Above 5.0 RA9-WAR: 9

Name	G	GS	GS%	IP	K%	BB%	GB%	FIP-	ERA-	WAR	RA9-WAR
Cole Hamels	245	244	99.6%	1596.7	23.2%	6.1%	43.2%	85	82	30.8	35.5
Shaun Marcum	188	161	85.6%	995.0	19.4%	7.3%	38.4%	101	94	13.0	16.5
Joe Blanton	265	248	93.6%	1567.3	16.2%	6.1%	44.2%	102	109	18.9	14.5
Jeff Francis	228	216	94.7%	1249.0	15.3%	6.8%	44.7%	95	108	18.1	12.3
Brandon McCarthy	176	122	69.3%	796.0	15.7%	6.3%	41.4%	96	97	11.5	10.5
Tyler Clippard	348	8	2.3%	423.7	27.4%	10.4%	27.6%	95	75	4.4	8.6
Zach Duke	216	169	78.2%	1086.7	11.9%	6.2%	48.9%	102	110	10.1	6.7
Roberto Hernandez	217	178	82.0%	1105.3	14.3%	8.5%	57.7%	110	113	8.5	6.6
John Maine	112	105	93.8%	593.0	19.5%	10.5%	37.7%	111	106	4.5	5.6
Tim Stauffer	141	70	49.6%	513.7	17.2%	7.9%	48.7%	111	105	2.0	4.8
Steven Shell	43	0	0.0%	55.0	20.8%	10.0%	36.7%	97	57	0.2	1.3
Mike Hinckley	28	0	0.0%	23.3	12.9%	15.1%	45.3%	110	46	-0.2	0.8
Yusmeiro Petit	81	44	54.3%	284.0	18.2%	7.3%	31.9%	113	122	1.3	0.6
Brad Thompson	201	32	15.9%	405.3	10.8%	6.9%	51.9%	115	106	-0.8	0.6
Kyle Waldrop	24	0	0.0%	32.3	8.3%	8.3%	72.2%	120	89	-0.3	0.4
Pat Misch	78	24	30.8%	200.7	13.2%	6.5%	44.7%	114	116	0.4	0.2
Bobby Livingston	13	10	76.9%	61.3	10.6%	5.0%	42.6%	113	139	0.5	-0.2
Abe Alvarez	4	1	25.0%	10.3	9.4%	13.2%	25.0%	224	243	-0.4	-0.4
Dusty Hughes	80	1	1.3%	83.0	15.9%	10.6%	37.3%	114	119	-0.2	-0.5
Steve Schmoll	48	0	0.0%	46.7	14.2%	10.7%	44.8%	108	121	-0.2	-0.6
Virgil Vasquez	19	10	52.6%	61.3	12.5%	8.0%	37.4%	140	154	-0.4	-0.7
Ezequiel Astacio	28	14	50.0%	86.7	18.2%	7.8%	37.5%	146	142	-0.9	-0.9
Manny Parra	232	74	31.9%	561.7	21.0%	10.9%	48.0%	102	120	4.1	-1.6
Chad Orvella	69	0	0.0%	82.3	16.3%	13.1%	35.5%	129	132	-0.7	-1.7
Average	129	72	56.1%	496.7	15.9%	8.7%	42.6%	115	113	5.2	5.0

The various sizes of these leaderboards probably help to offer at least an initial idea of the degree to which certain tools have portended major-league success. Among the 30 prospects regarded as possessing the best fastball in 2005, 27 of those have recorded a major-league inning at some point. Meanwhile, despite graduating fewer pitchers to the majors, those prospects distinguished for their curveball have recorded the greatest number of career RA9-WAR marks of 5.0 or above (11, as compared to just seven for pitchers demonstrating their organization’s best fastball). Prospects recognized for their changeup, meanwhile, finished last both in terms of major-league graduates and career RA9-WAR marks over 5.0.

While I’ve included the averages for each group at the bottom of all the leaderboards above, those numbers have limited utility for our concerns here, as they pertain only to those prospects who eventually graduated to the majors. They’re not entirely without use, those figures; however, if our aim is to assess the future production of all the Best Tool prospects, it’s better to find the median figures, instead, for all 30 players named by BA in each tool category.

With that in mind, I’ve included below a table including the median figures (or 15th-best, at least) for several relevant metrics among each tool category — along with career WAR and RA9-WAR figures, as well.

Tool	GS%	IP	K%	BB%	GB%	FIP-	ERA-	WAR	RA9-WAR	Tot WAR	Tot RA9-WAR
FA	16.0%	133.0	17.6%	10.7%	40.1%	109	118	0.2	0.1	133.6	129.9
CU	47.8%	292.2	17.8%	10.4%	42.4%	99	106	3.1	0.8	266.5	255.7
SL	0.0%	223.1	17.4%	10.3%	40.2%	109	108	0.9	0.9	122.8	124.9
CH	4.8%	29.2	9.4%	13.1%	37.3%	129	172	-0.5	-0.6	76.3	64.3
CO	30.8%	86.2	14.3%	8.5%	38.4%	113	116	0.2	0.4	124.2	118.9

Now, in lieu of further serious commentary, here are some observations presented by means of bullet point:

• By total WAR and total RA9-WAR, prospects recognized for their curveballs have more or less doubled the figures produced by pitchers recognized for their fastball, their slider, or their control.
• Perhaps unsurprisingly, prospects recognized for their sliders have been most likely to assume a relief role (as suggested by that group’s lowest-overall median games-started rate). Sliders, which typically feature more lateral than vertical movement, tend to neutralize same-handed batters, but are vulnerable to opposite-handed ones. Accordingly, pitchers who rely on that pitch might find themselves relegated to relief roles, in which they might be more easily deployed in situations where they possess the platoon advantage.
• Perhaps it’s because the curveball typically demonstrates more vertical break — and therefore produces a less pronounced platoon split — that prospects distinguished for the curves have recorded the highest rate of games started and most innings. Because the pitchers in question would suffer less against opposite-handed batters, I mean.
• Were that the case, however — i.e. that pitchers with curves have benefited from neutralizing batters’ platoon advantages more ably than pitchers with sliders — then one might reasonably expect prospects recognized for their changeups to have performed more admirably by the methodology utilized here. Indeed, such prospects have produced about one quarter the WAR recorded by prospects recognized for the curves and one half the totals recorded by pitchers from the other three groups.
• Ultimately, this is probably a matter that demands a larger sample, as it’s entirely possible that the contributions of a few (Felix Hernandez and Justin Verlander, for example) are distorting the outcomes as a whole.