Dylan Smith

P San Francisco Giants

Age 27 RHP 6'2" / 180 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	A+DET	14	13	57	4.40	4.25	23.1%	12.2%	—	K 17.8% · BB 14.0% · FIP 5.49
2025	25	MIX5 levels	37	1	53	2.05	2.28	30.7%	11.1%	1.4	K 33.2% · BB 11.4% · FIP 2.30
2026	26	MIX2 levels	57	0	60	3.90	3.37	26.2%	11.2%	0.8	K 24.9% · BB 10.8% · FIP 3.51
2027PROJ	27	MLBSF	33	0	35	4.05	4.10	26.9%	11.5%	0.2
2028PROJ83% MLB	28	MLBSF	31	0	33	4.16	4.21	26.4%	11.4%	0.1
2029PROJ85% MLB	29	MLBSF	27	0	28	4.29	4.38	25.6%	11.4%	0.1

Last 3 seasons WAR 2.3 · Next 3 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%67

FB Velo65

xwOBA vs56

Form4.76 ERA (proj 4.05) · K 24.0% (proj 26.9%) · BB 13.5% (proj 11.5%) ▼ last 90d · 23 IP through Oct 27

CompsJunior Guerra (age 30) · JT Chargois (age 27) · Matt Barnes (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 108100vR 91

ERA4.05

FIP4.10

K%26.9%

BB%11.5%

K-BB%15.4%

Proj IP45

Value2027 · WAR

Runs saved vs league avg · 45 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p6595.6

Whiff%

p4925.1%

p6725.4%

xwOBA vs

p560.306

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)
2022	22	MIX MLE	2 levels	22	21	88	367	89	21	6	95%	4.0	19.0%	6.9%	12.1%	2.5%	3.77	3.77	—
2022	22	MIX RAW	2 levels	22	21	88	367	89	21	6	95%	4.0	24.3%	5.7%	18.5%	1.6%	3.77	2.68	—
2023	23	MIX MLE	3 levels	12	11	37	171	38	13	3	92%	3.1	15.3%	5.0%	10.3%	3.7%	5.30	4.45	—
2023	23	MIX RAW	3 levels	12	11	37	171	38	13	3	92%	3.1	22.2%	7.6%	14.6%	1.8%	5.30	3.15	—
2024	24	A+ MLE	West Michigan Whitecaps	14	13	57	255	59	31	7	93%	4.1	17.8%	14.0%	3.8%	3.9%	4.40	5.49	—
2024	24	A+ MLE	West Michigan Whitecaps	14	13	57	255	59	31	7	93%	4.1	17.8%	14.0%	3.8%	3.9%	4.40	5.49	—
2024	24	A+ RAW	West Michigan Whitecaps	14	13	57	255	59	31	7	93%	4.1	23.1%	12.2%	11.0%	2.7%	4.40	4.25	—
2025	25	MIX MLE	5 levels	37	1	53	199	61	22	1	3%	1.4	33.2%	11.4%	21.8%	1.0%	2.05	2.30	+1.4
2025	25	MIX MLE	5 levels	37	1	53	199	61	22	1	3%	1.4	33.2%	11.4%	21.8%	1.0%	2.05	2.30	+1.4
2025	25	MIX RAW	5 levels	37	1	53	199	61	22	1	3%	1.4	30.7%	11.1%	19.6%	0.5%	2.05	2.28	+1.4
2026	26	MIX MLE	2 levels	57	0	60	260	68	29	5	0%	1.1	24.9%	10.8%	14.1%	2.1%	3.90	3.51	+0.7
2026	26	MIX MLE	2 levels	57	0	60	260	68	29	5	0%	1.1	24.9%	10.8%	14.1%	2.1%	3.90	3.51	+0.7
2026	26	MIX RAW	2 levels	57	0	60	260	68	29	5	0%	1.1	26.2%	11.2%	15.0%	1.9%	3.90	3.37	+0.8
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			49	0	53	225	49	24	3	0%	1.1	21.8%	10.7%	11.1%	1.3%	3.25	3.35	+0.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	SF	33	0	35	148	40	17	4	0%	1.1	26.9%	11.5%	15.4%	2.6%	4.05	4.10	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	SF	38	0	40	167	44	19	5	0%	1.0	26.4%	11.4%	15.0%	2.8%	4.16	4.21	+0.2
2029	29	MLB MLE	SF	31	0	32	137	35	16	4	0%	1.0	25.6%	11.4%	14.1%	2.9%	4.29	4.38	+0.1
2028	28	MLB CTX	SF	31	0	33	139	37	16	4	0%	1.1	26.4%	11.4%	15.0%	2.8%	4.16	4.21	+0.1
2029	29	MLB CTX	SF	27	0	28	118	30	14	3	0%	1.0	25.6%	11.4%	14.1%	2.9%	4.29	4.38	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		106	0	113	480	115	53	10	0%	1.1	24.0%	11.1%	13.0%	2.1%	3.77	3.64	+1.2
/ 200 IP	—	RAW		188	0	200	849	203	94	18	0%	1.1	24.0%	11.1%	13.0%	2.1%	3.77	3.64	+2.2
Career	—	MLE		142	46	296	1252	272	120	33	32%	2.1	21.7%	9.6%	12.1%	2.6%	3.80	3.92	+1.0
/ 200 IP	—	MLE		96	31	200	847	184	81	22	32%	2.1	21.7%	9.6%	12.1%	2.6%	3.80	3.92	+1.6

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 AAASacramento River Cats · 20 IPFIP 3.61 → 3.92 (+0.31)K% 27.7% → 23.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.71%	—	×0.860	23.83%
BB% (unintentional) /BF	12.05%	—	×0.900	10.84%
HR / BF /BF	2.41%	÷1.000	×1.100	2.65%

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 100).

Result: FIP 3.61 → 3.92, 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 AAErie SeaWolves · 20 IPFIP 1.19 → 1.91 (+0.71)K% 37.0% → 30.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.99%	—	×0.810	29.96%
BB% (unintentional) /BF	9.59%	—	×1.100	10.55%
HR / BF /BF	0.00%	÷1.056	×1.200	0.00%

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 111).

Result: FIP 1.19 → 1.91, 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).

2025 AAAToledo Mud Hens · 12 IPFIP 2.26 → 2.89 (+0.63)K% 44.0% → 37.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	44.00%	—	×0.860	37.84%
BB% (unintentional) /BF	14.00%	—	×0.900	12.60%
HR / BF /BF	2.00%	÷0.872	×1.100	2.52%

Result: FIP 2.26 → 2.89, 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 A+West Michigan Whitecaps · 6 IPFIP 1.69 → 2.42 (+0.72)K% 29.2% → 22.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.17%	—	×0.770	22.46%
BB% (unintentional) /BF	8.33%	—	×1.150	9.58%
HR / BF /BF	0.00%	÷0.850	×1.250	0.00%

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 70).

Result: FIP 1.69 → 2.42, 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).

2024 A+West Michigan Whitecaps · 57 IPFIP 4.19 → 5.49 (+1.29)K% 23.1% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.14%	—	×0.770	17.82%
BB% (unintentional) /BF	12.16%	—	×1.150	13.98%
HR / BF /BF	2.75%	÷0.889	×1.250	3.86%

Result: FIP 4.19 → 5.49, 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).

2023 A+West Michigan Whitecaps · 27 IPFIP 3.39 → 4.45 (+1.06)K% 19.8% → 15.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.83%	—	×0.770	15.27%
BB% (unintentional) /BF	4.31%	—	×1.150	4.96%
HR / BF /BF	2.59%	÷0.882	×1.250	3.67%

Result: FIP 3.39 → 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).

2023 AAErie SeaWolves · 6 IPFIP 2.46 → 3.14 (+0.68)K% 30.3% → 24.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.30%	—	×0.810	24.55%
BB% (unintentional) /BF	15.15%	—	×1.100	16.67%
HR / BF /BF	0.00%	÷1.055	×1.200	0.00%

Result: FIP 2.46 → 3.14, 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).

2023 ROKFCL Tigers · 5 IPFIP 2.91 → 3.95 (+1.05)K% 22.7% → 15.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.73%	—	×0.680	15.45%
BB% (unintentional) /BF	13.64%	—	×1.250	17.05%
HR / BF /BF	0.00%	÷0.924	×1.400	0.00%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 85).

Result: FIP 2.91 → 3.95, 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
Dylan Smith (OGILVIE)	—	—	—	—	—	2.6%	—	4.29	26.0%	14.6%	72	+0.19	—	—	—
Junior Guerra R 2015 · 3.18 FIP	30	SW	3.18	26.8%	10.3%	1.9%	.269	4.15	20.8%	10.9%	390	+0.97	3.18	646	73
JT Chargois R 2018 · 3.36 FIP	27	SU	3.36	24.4%	9.9%	2.0%	.282	3.48	25.9%	17.5%	108	+0.12	2.58	362	72
Matt Barnes R 2017 · 3.27 FIP	27	SU	3.27	28.9%	9.8%	2.5%	.296	3.03	36.3%	24.3%	149	-0.24	2.54	495	71
Heath Hembree R 2016 · 3.74 FIP	27	SU	3.74	21.1%	7.2%	2.8%	.292	4.03	27.3%	18.8%	162	+0.29	2.52	382	71
Zach Franklin R 2024 · 3.24 FIP	25	SU	3.24	26.4%	10.9%	1.8%	.301	4.15	27.5%	18.3%	117	+0.91	3.24	158	71
Fernando Rodriguez Jr. R 2011 · 3.85 FIP	27	SU	3.85	24.7%	10.8%	2.7%	.315	3.90	25.2%	16.5%	70	+0.05	3.34	335	71
Kyle Keller R 2019 · 3.83 FIP	26	SU	3.83	28.7%	9.4%	3.6%	.293	4.88	28.0%	17.8%	52	+1.05	2.72	296	71
Kiko Calero R 2003 · 3.54 FIP	28	SU	3.54	31.5%	11.1%	3.1%	.273	3.11	26.1%	18.9%	159	-0.43	2.27	298	70
Matt Pushard R 2024 · 3.87 FIP	26	SU	3.87	24.1%	8.9%	3.0%	.242	3.29	22.2%	14.0%	115	-0.58	2.84	220	70
Taylor Williams R 2018 · 3.52 FIP	27	SU	3.52	24.2%	8.9%	2.4%	.309	4.35	22.5%	12.7%	108	+0.83	2.51	309	70
Peyton Gray R 2021 · 4.27 FIP	26	SU	4.27	29.2%	14.1%	3.4%	.307	—	—	—	—	—	3.62	160	70
James Bourque R 2019 · 4.15 FIP	26	SU	4.15	25.7%	13.3%	2.8%	.325	—	—	—	—	—	3.36	312	70
James Sherfy R 2019 · 3.62 FIP	27	SU	3.62	27.1%	10.8%	2.6%	.355	4.80	19.0%	12.7%	15	+1.18	2.64	271	70
Simon Castro R 2015 · 3.28 FIP	27	SU	3.28	26.4%	8.5%	2.4%	.320	3.58	26.0%	17.6%	128	+0.30	3.01	185	69
Kirby Yates R 2014 · 3.25 FIP	27	SU	3.25	26.9%	7.7%	2.6%	.296	4.20	30.0%	21.2%	160	+0.95	0.66	526	69
Jacob Bosiokovic R 2021 · 3.93 FIP	27	SU	3.93	26.4%	14.3%	2.3%	.274	4.80	21.7%	10.2%	69	+0.87	3.84	181	69
JD Hammer R 2021 · 4.05 FIP	27	SU	4.05	27.0%	11.9%	3.1%	.312	5.50	14.6%	2.2%	85	+1.45	1.72	285	69
Ryan Garton R 2017 · 3.67 FIP	27	SU	3.67	25.2%	11.3%	2.3%	.252	4.15	23.5%	10.8%	108	+0.48	3.14	307	69
Marcos Mateo R 2011 · 3.40 FIP	27	SU	3.40	25.5%	10.2%	2.1%	.361	—	—	—	—	—	3.12	104	69
Connor Sadzeck R 2019 · 4.20 FIP	27	SU	4.20	25.2%	13.1%	2.9%	.238	4.61	22.7%	10.8%	94	+0.41	3.20	549	69

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	0.8	42	24.9%	11.6%	0.90	3.87
Chaz Roe ('21)	1.1	43	26.4%	11.1%	0.79	3.89
José Cisnero ('24)	1.4	46	24.0%	11.3%	1.01	4.16
Michael Wuertz ('11)	2.1	52	25.3%	11.3%	0.96	3.71
Ray Searage ('81-'90, 4 WAR*)	1.4	41	21.0%*	10.8%*	0.84*	3.70*
Dave Geisel ('78-'85, 2 WAR*)	1.0	42	23.5%*	10.6%*	0.84*	4.12*
Ed Vosberg ('86-'02, 1 WAR*)	1.2	47	21.0%*	9.6%*	0.86*	4.06*