Giovanny Gallegos

P St. Louis Cardinals

Age 35 RHP 6'2" / 205 lbs MLB

Overview

MLBRP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	30	MLBSTL	57	0	59	3.05	2.81	31.1%	7.2%	1.2
2023	31	MLBSTL	56	0	55	4.42	4.21	25.8%	5.2%	0.2
2024	32	MIX3 levels	40	1	40	6.58	6.15	18.4%	13.0%	-0.7	K 17.7% · BB 12.1% · FIP 6.21
2025	33	No MLB games played
2026	34	No MLB games played
2027PROJ	35	MLBSTL	47	0	49	4.34	4.47	23.5%	9.4%	0.0
2028PROJ91% MLB	36	MLBSTL	30	0	32	4.64	4.84	22.5%	10.0%	-0.1

Last 3 seasons WAR 0.7 · Next 2 projected WAR -0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2024

K%51

xwOBA vs29

FB Velo18

CompsMark Serrano (age 33) · Keiichi Yabu (age 36) · Jean Machi (age 35)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 118110vR 102

ERA4.34

FIP4.47

K%23.5%

BB%9.4%

K-BB%14.1%

Proj IP49

Value2027 · WAR

Runs saved vs league avg · 49 IP

Grades · Statcast · Contract

Scouting Grades

STUF7070

CTRL5555

CMD6565

K†5555

Statcast Percentiles

FB Velo

p1892.2

Whiff%

p5926.7%

p5122.3%

xwOBA vs

p290.337

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	23	MIX MLE	3 levels	35	0	63	241	64	9	2	0%	1.8	20.7%	4.0%	16.7%	1.5%	1.71	2.68	—
2015	23	MIX RAW	3 levels	35	0	63	241	64	9	2	0%	1.8	26.6%	3.7%	22.8%	0.8%	1.71	1.91	—
2016	24	MIX MLE	2 levels	42	0	78	294	106	16	5	0%	1.9	30.5%	5.8%	24.6%	1.9%	1.27	2.31	—
2016	24	MIX RAW	2 levels	42	0	78	294	106	16	5	0%	1.9	36.1%	5.4%	30.6%	1.7%	1.27	1.83	—
2017	25	MIX MLE	2 levels	44	0	64	257	91	15	7	0%	1.4	32.2%	6.1%	26.1%	2.5%	2.97	2.50	+1.6
2017	25	MIX RAW	2 levels	44	0	64	257	91	15	7	0%	1.4	35.4%	5.8%	29.6%	2.7%	2.97	2.38	+1.7
2018	26	MIX MLE	2 levels	36	0	56	219	69	11	3	0%	1.5	28.8%	4.8%	24.0%	0.7%	2.91	1.66	+2.0
2018	26	MIX RAW	2 levels	36	0	56	219	69	11	3	0%	1.5	31.5%	5.0%	26.5%	1.4%	2.91	1.91	+1.8
2019	27	MIX MLE	2 levels	67	0	75	282	94	14	9	0%	1.1	33.3%	5.0%	28.3%	3.5%	2.28	2.82	+1.5
2019	27	MIX RAW	2 levels	67	0	75	282	94	14	9	0%	1.1	33.3%	5.0%	28.4%	3.2%	2.28	2.71	+1.7
2020	28	MLB MLE	St. Louis Cardinals	16	0	15	57	21	4	1	0%	0.9	36.8%	7.0%	29.8%	1.9%	3.60	1.91	+0.5
2020	28	MLB RAW	St. Louis Cardinals	16	0	15	57	21	4	1	0%	0.9	36.8%	7.0%	29.8%	1.8%	3.60	1.97	+0.5
2021	29	MLB MLE	St. Louis Cardinals	73	0	80	310	95	18	6	0%	1.1	30.6%	5.8%	24.8%	2.1%	3.02	2.39	+2.1
2021	29	MLB RAW	St. Louis Cardinals	73	0	80	310	95	18	6	0%	1.1	30.6%	5.8%	24.8%	1.9%	3.02	2.38	+2.1
2022	30	MLB MLE	St. Louis Cardinals	57	0	59	235	73	17	6	0%	1.0	31.1%	7.2%	23.8%	2.7%	3.05	2.90	+1.2
2022	30	MLB RAW	St. Louis Cardinals	57	0	59	235	73	17	6	0%	1.0	31.1%	7.2%	23.8%	2.6%	3.05	2.81	+1.2
2023	31	MLB MLE	St. Louis Cardinals	56	0	55	229	59	12	11	0%	1.0	25.8%	5.2%	20.5%	5.2%	4.42	4.42	+0.1
2023	31	MLB RAW	St. Louis Cardinals	56	0	55	229	59	12	11	0%	1.0	25.8%	5.2%	20.5%	4.8%	4.42	4.21	+0.2
2024	32	MIX MLE	3 levels	40	1	40	185	34	24	9	2%	1.0	17.7%	12.1%	5.6%	5.6%	6.58	6.21	-0.8
2024	32	MIX MLE	3 levels	40	1	40	185	34	24	9	2%	1.0	17.7%	12.1%	5.6%	5.6%	6.58	6.21	-0.8
2024	32	MIX RAW	3 levels	40	1	40	185	34	24	9	2%	1.0	18.4%	13.0%	5.4%	4.9%	6.58	6.15	-0.7
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			311	0	336	1337	396	82	44	0%	1.1	29.6%	6.1%	23.5%	3.3%	3.49	3.18	+5.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	STL	47	0	49	207	49	19	7	0%	1.0	23.5%	9.4%	14.1%	3.3%	4.34	4.47	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	STL	34	0	35	149	34	15	5	0%	1.0	22.5%	10.0%	12.5%	3.6%	4.64	4.84	-0.1
2028	36	MLB CTX	STL	30	0	32	135	30	14	5	0%	1.1	22.5%	10.0%	12.5%	3.6%	4.64	4.84	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		342	0	368	1473	427	96	49	0%	1.1	29.0%	6.5%	22.5%	3.3%	3.59	3.30	+5.5
/ 200 IP	—	RAW		186	0	200	801	232	52	27	0%	1.1	29.0%	6.5%	22.5%	3.3%	3.59	3.30	+3.0
Career	—	MLE		466	1	584	2309	660	140	63	0%	1.3	28.6%	6.1%	22.5%	2.7%	2.93	2.95	+5.4
/ 200 IP	—	MLE		159	0	200	790	226	48	22	0%	1.3	28.6%	6.1%	22.5%	2.7%	2.93	2.95	+2.9

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.

2024 AAASt. Paul Saints · 17 IPFIP 5.97 → 6.07 (+0.10)K% 14.1% → 12.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.10%	—	×0.860	12.13%
BB% (unintentional) /BF	15.38%	—	×0.900	13.85%
HR / BF /BF	3.85%	÷1.039	×1.100	4.07%

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 108).

Result: FIP 5.97 → 6.07, 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).

2018 AAAMemphis Redbirds · 44 IPFIP 1.23 → 1.66 (+0.43)K% 32.8% → 28.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.76%	—	×0.880	28.83%
BB% (unintentional) /BF	4.60%	—	×1.050	4.83%
HR / BF /BF	0.57%	÷0.909	×1.100	0.70%

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 82).

Result: FIP 1.23 → 1.66, 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 AAAScranton/Wilkes-Barre RailRiders · 43 IPFIP 1.78 → 2.08 (+0.30)K% 40.8% → 35.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.83%	—	×0.880	35.93%
BB% (unintentional) /BF	6.51%	—	×1.050	6.83%
HR / BF /BF	2.37%	÷1.247	×1.100	2.09%

Result: FIP 1.78 → 2.08, 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 AAAScranton/Wilkes-Barre RailRiders · 45 IPFIP 2.43 → 2.91 (+0.48)K% 31.2% → 27.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.18%	—	×0.880	27.44%
BB% (unintentional) /BF	5.29%	—	×1.050	5.56%
HR / BF /BF	2.35%	÷0.996	×1.100	2.60%

Result: FIP 2.43 → 2.91, 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 AATrenton Thunder · 33 IPFIP 0.65 → 1.49 (+0.84)K% 42.7% → 34.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	42.74%	—	×0.810	34.62%
BB% (unintentional) /BF	5.65%	—	×1.100	6.21%
HR / BF /BF	0.81%	÷1.015	×1.200	0.95%

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 103).

Result: FIP 0.65 → 1.49, 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).

2015 A+Tampa Yankees · 53 IPFIP 1.82 → 2.68 (+0.86)K% 26.9% → 20.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.87%	—	×0.770	20.69%
BB% (unintentional) /BF	3.48%	—	×1.150	4.00%
HR / BF /BF	1.00%	÷0.832	×1.250	1.50%

Factors used: pitcher A+ level factors — K ×0.77, BB ×1.15, HR ×1.25. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 66).

Result: FIP 1.82 → 2.68, 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).

2015 AATrenton Thunder · 7 IPFIP 1.94 → 2.41 (+0.47)K% 24.1% → 19.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.14%	—	×0.810	19.55%
BB% (unintentional) /BF	6.90%	—	×1.100	7.59%
HR / BF /BF	0.00%	÷1.153	×1.200	0.00%

Result: FIP 1.94 → 2.41, 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).

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
Giovanny Gallegos (OGILVIE)	—	—	4.47	23.5%	9.4%	3.3%	—	4.84	22.5%	12.5%	35	+0.37	—	—	—
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	81
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	78
Jean Machi R 2017 · 4.12 FIP	35	SU	4.12	18.6%	6.6%	3.2%	.285	—	—	—	—	—	2.16	306	77
Brendan Donnelly R 2006 · 4.21 FIP	35	SU	4.21	19.1%	9.0%	2.9%	.260	3.19	18.6%	10.1%	60	-1.02	2.23	385	77
Toby Borland R 2004 · 4.28 FIP	35	SU	4.28	20.9%	8.1%	3.5%	.259	—	—	—	—	—	2.92	248	75
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	73
Hector Navarro R 2015 · 4.35 FIP	35	SU	4.35	15.0%	5.4%	3.3%	.281	3.00	15.2%	9.0%	81	-1.35	2.30	126	72
Leo Rosales R 2015 · 3.99 FIP	34	SU	3.99	18.1%	10.8%	1.9%	.261	4.24	7.8%	-0.4%	22	+0.25	3.85	130	72
Yoshihisa Hirano R 2019 · 3.72 FIP	35	SU	3.72	26.2%	8.6%	3.2%	.303	5.02	17.5%	4.8%	12	+1.30	3.45	132	71
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	71
Brian Schlitter R 2021 · 4.70 FIP	35	SU	4.70	14.9%	9.1%	3.1%	.318	—	—	—	—	—	3.27	300	70
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	70
Spencer Patton R 2023 · 4.33 FIP	35	SU	4.33	18.9%	9.7%	2.9%	.295	—	—	—	—	—	2.79	239	68
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	68
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	68
Fernando Rodriguez Jr. R 2019 · 5.11 FIP	35	SW	5.11	25.0%	11.2%	4.9%	.312	—	—	—	—	—	3.34	335	67
Kazuhiro Sasaki R 2003 · 3.30 FIP	35	SU	3.30	19.3%	8.7%	1.3%	.274	—	—	—	—	—	2.76	223	66
Rodolfo Aguirre R 2019 · 4.50 FIP	34	SU	4.50	21.3%	5.1%	4.7%	.244	—	—	—	—	—	3.39	212	66
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	65
Chris Nichting R 2001 · 4.11 FIP	35	SU	4.11	21.2%	3.7%	4.2%	.351	4.67	16.6%	13.3%	36	+0.56	4.11	103	64

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 1,234 reliever careers (754 from 1990-2025 + 480 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	2.1	53	28.2%	7.2%	1.28	3.68
Blake Parker ('21)	1.7	58	26.7%	7.9%	1.30	3.47
Chad Green ('25)	2.5	62	30.0%	6.4%	1.42	3.43
Shawn Kelley ('19)	2.3	46	27.0%	7.3%	1.47	3.80
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Joe Hoerner ('63-'77, 8 WAR*)	2.8	51	23.2%*	7.9%*	1.21*	3.52*
Joe Sambito ('76-'87, 8 WAR*)	3.1	70	26.8%*	7.5%*	1.06*	3.65*