Lucas Giolito

P San Diego Padres

Age 33 RHP 6'6" / 245 lbs MLB peak 0.3 · 2027 ILInjured 60-Day (D60)

Overview

MLBSPIL-6040-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	29	MLBCLE	33	33	184	4.88	4.93	25.7%	8.9%	1.0
2025	31	MIX3 levels	31	31	162	3.60	4.12	19.9%	9.9%	2.5	K 19.7% · BB 9.1% · FIP 4.15
2026	32	MIX3 levels	11	9	47	5.01	4.94	15.3%	13.5%	0.2	K 13.0% · BB 16.7% · FIP 5.66
2027PROJ	33	MLBSD	9	9	56	4.69	5.02	17.7%	11.3%	0.2
2028PROJ	34	MLBSD	8	8	47	5.07	5.33	16.8%	11.8%	0.0
2029PROJ	35	MLBSD	5	5	32	5.26	5.39	17.9%	11.6%	0.0

Last 3 seasons WAR 3.7 · Next 3 projected WAR 0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs11

FB Velo10

K%10

CompsScott Carroll (age 32) · Diego Moreno (age 32) · Chuck Smith (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 129120vR 112

ERA4.69

FIP5.02

K%17.7%

BB%11.3%

K-BB%6.4%

Proj IP56

Value2027 · WAR

Runs saved vs league avg · 56 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1090.8

Whiff%

p4124.0%

p1013.0%

xwOBA vs

p110.358

Contract

No contract data on file. Pre-MLB players and minor-league free agents do not yet have salary projections — entries are added at first 40-man roster placement.

Career Stats + Projections

Real MLB stats plus park-adjusted projections — this season (so far / rest / full), then future seasons and career totals.

show model inputsbasis chips: RAW = as recorded · CTX = with park/league context · A+C = actual + context combined · MLE = level/park-neutral

Methodology▸ how WAR & MLE are computed

Stats are park-neutral MLB equivalents. The "TEAM" column shows current org affiliation only — we do NOT project at that team's park.
Rates (K%, BB%, HR-BF) are park-stripped; MiLB stat lines are translated to their MLB-equivalent via per-level MLE factors.
WAR is computed from projected runs prevented vs a replacement pitcher (the marginal arm a team would call up if this pitcher were unavailable) — calibrated to the standard WAR scale.

Year	Age	Lvl	Team	G	GS	IP	BF	K	BB	HR	GS%	IP/G	K%	BB%	K-BB%	HR%	ERA	FIP	WAR
▸ Career — actual stats, one row per year (as recorded, no adjustments)
▸ Career — adjusted (each season translated to a park/league-neutral MLB-equivalent line; levels combined per year)
▸ Model inputs — neutral MLE (past 3 seasons + 2027 so far)
2015	21	MIX MLE	2 levels	21	19	117	494	131	37	3	90%	5.6	20.8%	8.4%	12.3%	0.8%	3.15	2.84	—
2015	21	MIX RAW	2 levels	21	19	117	494	131	37	3	90%	5.6	26.5%	7.5%	19.0%	0.6%	3.15	2.14	—
2016	22	MIX MLE	4 levels	28	26	137	589	127	56	14	93%	4.9	18.6%	10.6%	7.9%	2.1%	3.56	4.05	+2.2
2016	22	MIX RAW	4 levels	28	26	137	589	127	56	14	93%	4.9	21.6%	9.5%	12.1%	2.4%	3.56	3.80	+2.6
2017	23	MIX MLE	2 levels	31	31	174	730	168	71	25	100%	5.6	20.8%	10.1%	10.7%	3.3%	3.93	4.44	+1.9
2017	23	MIX RAW	2 levels	31	31	174	730	168	71	25	100%	5.6	23.0%	9.7%	13.3%	3.4%	3.93	4.26	+2.3
2018	24	MLB MLE	Chicago White Sox	32	32	173	775	125	88	27	100%	5.4	16.1%	11.4%	4.8%	3.4%	6.13	5.06	+0.7
2018	24	MLB RAW	Chicago White Sox	32	32	173	775	125	88	27	100%	5.4	16.1%	11.4%	4.8%	3.5%	6.13	5.21	+0.4
2019	25	MLB MLE	Chicago White Sox	29	29	177	705	228	56	24	100%	6.1	32.3%	7.9%	24.4%	3.3%	3.41	3.21	+4.8
2019	25	MLB RAW	Chicago White Sox	29	29	177	705	228	56	24	100%	6.1	32.3%	7.9%	24.4%	3.4%	3.41	3.24	+4.8
2020	26	MLB MLE	Chicago White Sox	12	12	72	288	97	28	8	100%	6.0	33.7%	9.7%	24.0%	2.7%	3.48	2.99	+2.2
2020	26	MLB RAW	Chicago White Sox	12	12	72	288	97	28	8	100%	6.0	33.7%	9.7%	24.0%	2.8%	3.48	3.02	+2.2
2021	27	MLB MLE	Chicago White Sox	31	31	179	720	201	51	27	100%	5.8	27.9%	7.1%	20.8%	3.7%	3.53	3.66	+3.8
2021	27	MLB RAW	Chicago White Sox	31	31	179	720	201	51	27	100%	5.8	27.9%	7.1%	20.8%	3.8%	3.53	3.67	+3.7
2022	28	MLB MLE	Chicago White Sox	30	30	162	698	177	60	24	100%	5.4	25.4%	8.6%	16.8%	3.4%	4.90	3.90	+2.9
2022	28	MLB RAW	Chicago White Sox	30	30	162	698	177	60	24	100%	5.4	25.4%	8.6%	16.8%	3.4%	4.90	3.95	+2.8
2023	29	MLB MLE	Cleveland Guardians	33	33	184	794	204	71	41	100%	5.6	25.7%	8.9%	16.8%	5.2%	4.88	4.90	+1.0
2023	29	MLB RAW	Cleveland Guardians	33	33	184	794	204	71	41	100%	5.6	25.7%	8.9%	16.8%	5.2%	4.88	4.93	+1.0
2025	31	MIX MLE	3 levels	31	31	162	694	138	69	18	100%	5.2	19.7%	9.1%	10.6%	2.8%	3.60	4.15	+2.4
2025	31	MIX MLE	3 levels	31	31	162	694	138	69	18	100%	5.2	19.7%	9.1%	10.6%	2.8%	3.60	4.15	+2.4
2025	31	MIX RAW	3 levels	31	31	162	694	138	69	18	100%	5.2	19.9%	9.9%	9.9%	2.6%	3.60	4.12	+2.5
2026	32	MIX MLE	3 levels	11	9	47	215	33	29	5	82%	4.2	13.0%	16.7%	-3.6%	2.8%	5.01	5.66	-0.1
2026	32	MIX MLE	3 levels	11	9	47	215	33	29	5	82%	4.2	13.0%	16.7%	-3.6%	2.8%	5.01	5.66	-0.1
2026	32	MIX RAW	3 levels	11	9	47	215	33	29	5	82%	4.2	15.3%	13.5%	1.9%	2.3%	5.01	4.94	+0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			213	209	1188	5011	1216	457	187	98%	5.6	24.3%	9.1%	15.1%	3.7%	4.32	4.25	+16.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	SD	9	9	56	236	42	27	7	100%	6.2	17.7%	11.3%	6.4%	2.9%	4.69	5.02	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	SD	8	8	47	199	33	24	6	100%	5.9	16.8%	11.8%	5.0%	3.2%	5.07	5.33	+0.0
2029	35	MLB MLE	SD	5	5	32	137	25	16	5	100%	6.5	17.9%	11.6%	6.3%	3.6%	5.26	5.39	+0.0
2028	34	MLB CTX	SD	8	8	47	198	33	23	6	100%	5.9	16.8%	11.8%	5.0%	3.2%	5.07	5.33	+0.0
2029	35	MLB CTX	SD	5	5	32	135	24	16	5	100%	6.4	17.9%	11.6%	6.3%	3.6%	5.26	5.39	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		226	222	1268	5347	1274	496	198	98%	5.6	23.8%	9.3%	14.5%	3.7%	4.38	4.30	+16.4
/ 200 IP	—	RAW		36	35	200	843	201	78	31	98%	5.6	23.8%	9.3%	14.5%	3.7%	4.38	4.30	+2.6
Career	—	MLE		289	283	1584	6702	1561	632	216	98%	5.5	23.3%	9.4%	13.9%	3.2%	4.17	4.09	+16.0
/ 200 IP	—	MLE		36	36	200	846	197	80	27	98%	5.5	23.3%	9.4%	13.9%	3.2%	4.17	4.09	+2.5

How the minor-league lines were translated

One row per minor-league stint. Open a row to see each step: the raw rate, the park adjustment, the league/level factor, the age adjustment (hitters) and the resulting MLE rate. Factors are the same ones listed on the MLE Factors page.

2026 AASan Antonio Missions · 10 IPFIP 3.96 → 4.45 (+0.49)K% 11.4% → 9.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	11.36%	—	×0.810	9.20%
BB% (unintentional) /BF	4.55%	—	×1.100	5.00%
HR / BF /BF	2.27%	÷1.000	×1.200	2.73%

Factors used: pitcher AA level factors — K ×0.81, BB ×1.10, HR ×1.20. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 100).

Result: FIP 3.96 → 4.45, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2026 ALake Elsinore Storm · 7 IPFIP 2.08 → 3.02 (+0.94)K% 30.3% → 22.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.30%	—	×0.740	22.42%
BB% (unintentional) /BF	12.12%	—	×1.180	14.30%
HR / BF /BF	0.00%	÷1.000	×1.300	0.00%

Factors used: pitcher A level factors — K ×0.74, BB ×1.18, HR ×1.30. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 100).

Result: FIP 2.08 → 3.02, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2025 AAAWorcester Red Sox · 10 IPFIP 4.20 → 4.16 (−0.04)K% 19.6% → 16.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.57%	—	×0.860	16.83%
BB% (unintentional) /BF	21.74%	—	×0.900	19.57%
HR / BF /BF	0.00%	÷0.984	×1.100	0.00%

Factors used: pitcher AAA level factors (Triple-A 2023+ robo-zone set: walks improve on promotion) — K ×0.86, BB ×0.90, HR ×1.10. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 97).

Result: FIP 4.20 → 4.16, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2025 AAPortland Sea Dogs · 7 IPFIP 3.82 → 4.41 (+0.59)K% 22.9% → 18.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.86%	—	×0.810	18.51%
BB% (unintentional) /BF	8.57%	—	×1.100	9.43%
HR / BF /BF	2.86%	÷1.119	×1.200	3.06%

Result: FIP 3.82 → 4.41, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2017 AAACharlotte Knights · 129 IPFIP 4.10 → 4.34 (+0.24)K% 24.3% → 21.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.32%	—	×0.880	21.40%
BB% (unintentional) /BF	10.71%	—	×1.050	11.24%
HR / BF /BF	3.09%	÷1.150	×1.100	2.95%

Factors used: pitcher AAA level factors — K ×0.88, BB ×1.05, HR ×1.10. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 130).

Result: FIP 4.10 → 4.34, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2016 AAHarrisburg Senators · 71 IPFIP 2.88 → 3.33 (+0.45)K% 23.0% → 18.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.00%	—	×0.810	18.63%
BB% (unintentional) /BF	10.86%	—	×1.100	11.95%
HR / BF /BF	0.64%	÷1.445	×1.200	0.53%

Result: FIP 2.88 → 3.33, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2016 AAASyracuse Chiefs · 37 IPFIP 2.79 → 3.24 (+0.45)K% 26.8% → 23.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.85%	—	×0.880	23.62%
BB% (unintentional) /BF	6.71%	—	×1.050	7.05%
HR / BF /BF	2.01%	÷0.978	×1.100	2.27%

Result: FIP 2.79 → 3.24, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2016 AHagerstown Suns · 7 IPFIP 6.02 → 6.55 (+0.53)K% 15.4% → 11.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.38%	—	×0.740	11.38%
BB% (unintentional) /BF	0.00%	—	×1.180	0.00%
HR / BF /BF	7.69%	÷1.245	×1.300	8.03%

Result: FIP 6.02 → 6.55, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

Career Path Comps

What happened to similar pitchers from this age · Y-axis: ERA+ (100 = league avg, 150 = elite, 80 = below) · Bands: P10–P90 outer, P25–P75 inner · Gold: OGILVIE projection

			Profile at Match Age					Next 3 Yrs				Career
Comp	Age	Role	FIP	K%	BB%	HR/BF	BABIP	FIP	K%	K-BB%	IP	ΔFIP	Low FIP	IP	Match
Lucas Giolito (OGILVIE)	—	—	—	—	—	2.9%	—	5.35	17.3%	5.5%	80	+0.33	—	—	—
Scott Carroll R 2017 · 5.71 FIP	32	SP	5.71	14.4%	8.6%	5.0%	.300	—	—	—	—	—	3.92	377	68
Diego Moreno R 2019 · 4.08 FIP	32	SP	4.08	12.4%	6.9%	2.1%	.376	4.75	18.6%	9.2%	30	+0.67	2.29	187	67
Chuck Smith R 2001 · 3.99 FIP	31	SP	3.99	18.4%	8.1%	2.6%	.289	—	—	—	—	—	3.08	211	66
Juan Delgadillo R 2015 · 4.54 FIP	32	SP	4.54	15.5%	7.2%	3.3%	.316	4.47	10.9%	4.7%	39	-0.07	4.47	143	65
Walter Silva R 2009 · 5.72 FIP	32	SP	5.72	9.1%	12.4%	3.3%	.330	—	—	—	—	—	3.67	453	64
J.D. Martin R 2016 · 4.85 FIP	33	SP	4.85	9.4%	11.0%	2.1%	.302	5.42	12.2%	-0.2%	249	+0.57	4.79	414	63
Miguel Ruiz R 2017 · 5.60 FIP	31	SP	5.60	10.6%	11.0%	3.6%	.279	—	—	—	—	—	5.60	117	63
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	62
Jason Simontacchi R 2007 · 4.96 FIP	33	SP	4.96	13.0%	5.9%	4.0%	.331	—	—	—	—	—	4.79	356	60
Michael Nix R 2016 · 4.60 FIP	33	SP	4.60	11.9%	11.6%	1.9%	.322	—	—	—	—	—	3.82	234	60
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	59
Armando Galarraga R 2015 · 3.92 FIP	33	SP	3.92	10.6%	7.3%	1.4%	.320	—	—	—	—	—	3.92	584	59
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	59
Cesar Carrillo R 2017 · 4.97 FIP	33	SP	4.97	12.0%	11.9%	2.5%	.320	6.22	11.0%	1.8%	66	+1.25	4.14	336	59
Mitch Atkins R 2017 · 4.77 FIP	31	SP	4.77	14.3%	8.1%	3.4%	.303	—	—	—	—	—	3.65	215	58
David Fletcher R 2024 · 5.67 FIP	30	SP-	5.67	7.5%	10.3%	3.5%	.294	—	—	—	—	—	5.67	100	57
Osvaldo Fernandez R 2001 · 4.53 FIP	32	SP-	4.53	9.6%	8.2%	2.2%	.324	—	—	—	—	—	4.25	387	56
Sergio Valenzuela R 2017 · 5.66 FIP	32	SP	5.66	10.6%	6.5%	4.8%	.355	—	—	—	—	—	3.66	244	56
Francisco Cordova R 2016 · 4.59 FIP	32	SP	4.59	7.1%	7.7%	2.0%	.300	—	—	—	—	—	4.59	115	56
Elih Villanueva R 2017 · 5.48 FIP	31	SP	5.48	14.1%	6.0%	5.1%	.365	—	—	—	—	—	3.83	225	54

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 historical, 1901-2023), also matched on career-to-date at the same age. * Historical (Lahman) careers: rates era-adjusted to 2001-24 league levels; WAR is an OGILVIE proxy.

Player	162s	IP/yr	K%	BB%	HR/9	ERA
OGILVIE CAREER PROJECTION	6.7	102	23.5%	9.5%	1.42	4.39
Bobby Bolin ('61-'73, 12 WAR*)	7.9	121	22.7%*	9.6%*	1.33*	4.11*
Derek Holland ('21)	7.3	113	19.7%	8.4%	1.35	4.62
Diego Segui ('62-'77, 10 WAR*)	9.0	121	22.2%*	9.9%*	1.19*	4.40*
Pete Richert ('62-'74, 13 WAR*)	5.8	90	24.4%*	8.8%*	1.26*	4.04*
Ian Kennedy ('23)	9.5	146	21.9%	8.0%	1.32	4.16
Bud Norris ('18)	6.1	122	21.7%	9.1%	1.15	4.45