Logan Webb

P San Francisco Giants

Age 30 RHP 6'2" / 221 lbs MLB Svc 7.0 · Ctrl thru 2028 peak 3.0 · 2027

Overview

MLBSP40-MANExtFA 2028

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MLBSF	33	33	205	3.47	2.81	20.5%	5.6%	6.8
2025	28	MLBSF	34	34	207	3.22	2.48	26.2%	5.4%	7.9
2026	29	MIX2 levels	28	28	164	4.23	3.28	19.9%	6.4%	4.3	K 20.1% · BB 6.1% · FIP 3.34
2027PROJ	30	MLBSF	22	22	132	3.60	3.56	21.6%	5.9%	3.0
2028PROJ	31	MLBSF	19	19	112	3.61	3.69	20.9%	6.2%	2.2
2029PROJ	32	MLBSF	15	15	92	3.80	3.93	20.1%	6.6%	1.5

Last 3 seasons WAR 18.9 · Next 3 projected WAR 6.6Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2028Service7.256First FA2028

2027Ext$18M2028Ext$18M

Skills · percentileStatcast 2026

xwOBA vs51

K%36

FB Velo19

Form11.08 ERA (proj 3.60) · K 24.6% (proj 21.6%) · BB 7.7% (proj 5.9%) · last 60d · 13 IP through Oct 27

CompsTom Koehler (age 30) · Masahiro Tanaka (age 30) · Kyle Gibson (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 10294vR 86

ERA3.60

FIP3.56

K%21.6%

BB%5.9%

K-BB%15.8%

Proj IP132

Value2027 · WAR

Runs saved vs league avg · 132 IP

━ projected WAR━ paid WAR ($/8M) surplus deficit━ post-control+$6M ctrl surplus

Statcast · Contract

Statcast Percentiles

FB Velo

p1992.3

Whiff%

p2321.5%

p3620.1%

xwOBA vs

p510.311

Contract ESTIMATED · service-time + WAR-based estimator (needs verification)

Status FA

MLB Debut 2019

Service 7.256 yrs.days

Team Control Through 2028

First FA Year 2028

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext	$18M	+3.0	$27M	+$9M
2028	Ext	$18M	+2.2	$16M	+$0.1M
Totals		$36M		$43M	+$9M

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)
2016	19	A MLE	Augusta GreenJackets	9	9	42	194	30	12	7	100%	4.7	11.4%	7.3%	4.1%	5.6%	6.21	6.14	—
2016	19	A RAW	Augusta GreenJackets	9	9	42	194	30	12	7	100%	4.7	15.5%	6.2%	9.3%	3.6%	6.21	4.70	—
2018	21	MIX MLE	2 levels	27	26	105	434	100	46	6	96%	3.9	18.0%	12.1%	5.9%	1.9%	2.41	4.15	—
2018	21	MIX RAW	2 levels	27	26	105	434	100	46	6	96%	3.9	23.0%	10.6%	12.4%	1.4%	2.41	3.25	—
2019	22	MIX MLE	5 levels	20	18	103	435	106	29	7	90%	5.2	21.3%	7.7%	13.6%	2.3%	3.15	3.54	+2.3
2019	22	MIX RAW	5 levels	20	18	103	435	106	29	7	90%	5.2	24.4%	6.7%	17.7%	1.6%	3.15	2.77	+3.4
2020	23	MLB MLE	San Francisco Giants	13	11	54	246	46	23	4	85%	4.2	18.7%	9.3%	9.3%	1.8%	5.47	3.67	+1.1
2020	23	MLB RAW	San Francisco Giants	13	11	54	246	46	23	4	85%	4.2	18.7%	9.3%	9.3%	1.6%	5.47	3.63	+1.2
2021	24	MIX MLE	2 levels	28	27	150	603	159	37	9	96%	5.4	26.5%	6.0%	20.5%	1.6%	3.05	2.52	+5.5
2021	24	MIX RAW	2 levels	28	27	150	603	159	37	9	96%	5.4	26.4%	6.1%	20.2%	1.5%	3.05	2.50	+5.6
2022	25	MLB MLE	San Francisco Giants	32	32	192	787	163	49	11	100%	6.0	20.7%	6.2%	14.5%	1.5%	2.90	2.97	+5.9
2022	25	MLB RAW	San Francisco Giants	32	32	192	787	163	49	11	100%	6.0	20.7%	6.2%	14.5%	1.4%	2.90	2.91	+6.1
2023	26	MLB MLE	San Francisco Giants	33	33	216	850	194	31	20	100%	6.5	22.8%	3.6%	19.2%	2.5%	3.25	3.03	+6.5
2023	26	MLB RAW	San Francisco Giants	33	33	216	850	194	31	20	100%	6.5	22.8%	3.6%	19.2%	2.4%	3.25	2.94	+6.8
2024	27	MLB MLE	San Francisco Giants	33	33	205	841	172	47	11	100%	6.2	20.5%	5.6%	14.9%	1.4%	3.47	2.86	+6.6
2024	27	MLB MLE	San Francisco Giants	33	33	205	841	172	47	11	100%	6.2	20.5%	5.6%	14.9%	1.4%	3.47	2.86	+6.6
2024	27	MLB RAW	San Francisco Giants	33	33	205	841	172	47	11	100%	6.2	20.5%	5.6%	14.9%	1.3%	3.47	2.81	+6.8
2025	28	MLB MLE	San Francisco Giants	34	34	207	856	224	46	14	100%	6.1	26.2%	5.4%	20.8%	1.8%	3.22	2.54	+7.7
2025	28	MLB MLE	San Francisco Giants	34	34	207	856	224	46	14	100%	6.1	26.2%	5.4%	20.8%	1.8%	3.22	2.54	+7.7
2025	28	MLB RAW	San Francisco Giants	34	34	207	856	224	46	14	100%	6.1	26.2%	5.4%	20.8%	1.6%	3.22	2.48	+7.9
2026	29	MIX MLE	2 levels	28	28	164	683	136	44	13	100%	5.9	20.1%	6.1%	13.9%	2.1%	4.23	3.34	+4.2
2026	29	MIX MLE	2 levels	28	28	164	683	136	44	13	100%	5.9	20.1%	6.1%	13.9%	2.1%	4.23	3.34	+4.2
2026	29	MIX RAW	2 levels	28	28	164	683	136	44	13	100%	5.9	19.9%	6.4%	13.5%	1.9%	4.23	3.28	+4.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			207	204	1223	5018	1128	287	87	99%	5.9	22.5%	5.7%	16.8%	1.7%	3.50	2.88	+39.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	SF	22	22	132	557	121	33	12	100%	6.0	21.6%	5.9%	15.8%	2.1%	3.60	3.56	+3.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	SF	19	19	112	472	99	29	11	100%	5.9	20.9%	6.2%	14.7%	2.3%	3.71	3.79	+2.2
2029	32	MLB MLE	SF	15	15	92	388	78	26	10	100%	6.1	20.1%	6.6%	13.5%	2.6%	3.91	4.04	+1.5
2030	33	MLB MLE	SF	12	12	72	304	59	21	9	100%	6.0	19.2%	7.1%	12.2%	2.9%	4.16	4.32	+0.9
2031	34	MLB MLE	SF	9	9	52	217	40	16	7	100%	5.7	18.4%	7.6%	10.8%	3.2%	4.44	4.63	+0.5
2032	35	MLB MLE	SF	5	5	33	138	24	11	5	100%	6.6	17.4%	8.1%	9.3%	3.5%	4.75	4.96	+0.2
2028	31	MLB CTX	SF	19	19	112	473	99	29	10	100%	5.9	20.9%	6.2%	14.7%	2.2%	3.61	3.69	+2.2
2029	32	MLB CTX	SF	15	15	92	388	78	26	9	100%	6.1	20.1%	6.6%	13.5%	2.4%	3.80	3.93	+1.5
2030	33	MLB CTX	SF	12	12	72	304	59	21	8	100%	6.0	19.2%	7.1%	12.2%	2.6%	4.04	4.20	+0.9
2031	34	MLB CTX	SF	9	9	52	220	40	17	6	100%	5.8	18.4%	7.6%	10.8%	2.9%	4.31	4.50	+0.5
2032	35	MLB CTX	SF	6	6	33	139	24	11	5	100%	5.5	17.4%	8.1%	9.3%	3.2%	4.61	4.82	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		267	264	1583	6539	1427	391	125	99%	5.9	21.8%	6.0%	15.8%	1.9%	3.60	3.07	+46.3
/ 200 IP	—	RAW		34	33	200	826	180	49	16	99%	5.9	21.8%	6.0%	15.8%	1.9%	3.60	3.07	+5.9
Career	—	MLE		257	251	1438	5929	1289	374	118	98%	5.6	21.7%	6.3%	15.4%	2.0%	3.42	3.16	+43.7
/ 200 IP	—	MLE		36	35	200	824	179	52	16	98%	5.6	21.7%	6.3%	15.4%	2.0%	3.42	3.16	+5.5

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.

2019 AARichmond Flying Squirrels · 41 IPFIP 2.35 → 3.07 (+0.72)K% 26.3% → 21.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.26%	—	×0.810	21.27%
BB% (unintentional) /BF	6.70%	—	×1.100	7.37%
HR / BF /BF	1.12%	÷0.890	×1.200	1.51%

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 78).

Result: FIP 2.35 → 3.07, 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).

2019 AAugusta GreenJackets · 10 IPFIP 2.07 → 2.79 (+0.72)K% 24.3% → 18.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.32%	—	×0.740	18.00%
BB% (unintentional) /BF	8.11%	—	×1.180	9.57%
HR / BF /BF	0.00%	÷1.082	×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 116).

Result: FIP 2.07 → 2.79, 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).

2019 AAASacramento River Cats · 7 IPFIP 0.91 → 1.17 (+0.26)K% 25.9% → 22.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.93%	—	×0.880	22.81%
BB% (unintentional) /BF	0.00%	—	×1.050	0.00%
HR / BF /BF	0.00%	÷1.027	×1.100	0.00%

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 105).

Result: FIP 0.91 → 1.17, 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 A+San Jose Giants · 74 IPFIP 2.90 → 3.73 (+0.83)K% 24.5% → 18.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.50%	—	×0.770	18.87%
BB% (unintentional) /BF	11.92%	—	×1.150	13.71%
HR / BF /BF	0.66%	÷0.924	×1.250	0.90%

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 85).

Result: FIP 2.90 → 3.73, 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 AARichmond Flying Squirrels · 31 IPFIP 4.06 → 5.11 (+1.05)K% 19.7% → 16.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.70%	—	×0.810	15.95%
BB% (unintentional) /BF	7.58%	—	×1.100	8.33%
HR / BF /BF	3.03%	÷0.869	×1.200	4.18%

Result: FIP 4.06 → 5.11, 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 AAugusta GreenJackets · 42 IPFIP 4.56 → 6.14 (+1.59)K% 15.5% → 11.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.46%	—	×0.740	11.44%
BB% (unintentional) /BF	6.19%	—	×1.180	7.30%
HR / BF /BF	3.61%	÷0.834	×1.300	5.62%

Result: FIP 4.56 → 6.14, 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).

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
Logan Webb (OGILVIE)	—	—	—	—	—	2.1%	—	4.01	20.2%	13.6%	276	+0.45	—	—	—
Tom Koehler R 2016 · 4.38 FIP	30	SP	4.38	19.0%	9.8%	3.0%	.291	4.66	22.4%	13.4%	110	+0.28	3.76	822	69
Masahiro Tanaka R 2019 · 4.01 FIP	30	SP	4.01	19.6%	5.3%	3.5%	.292	4.08	22.3%	18.2%	48	+0.07	2.82	1,054	66
Kyle Gibson R 2018 · 3.97 FIP	30	SP	3.97	21.7%	9.3%	2.8%	.282	3.91	21.2%	12.9%	409	-0.06	3.56	1,920	65
Corey Kluber R 2016 · 3.10 FIP	30	SP	3.10	26.4%	6.5%	2.6%	.267	2.73	29.6%	25.0%	454	-0.37	2.16	1,636	63
Tanner Roark R 2017 · 3.88 FIP	30	SP	3.88	21.4%	7.6%	3.0%	.294	4.48	20.3%	13.2%	393	+0.60	2.40	1,194	61
Rodrigo Lopez R 2006 · 4.67 FIP	30	SP-	4.67	16.1%	6.7%	3.8%	.325	4.30	13.2%	7.9%	109	-0.37	4.06	1,344	59
Miguel González R 2014 · 4.49 FIP	30	SP	4.49	16.5%	7.5%	3.4%	.268	4.26	16.6%	9.5%	457	-0.23	3.72	905	58
Rolando Arrojo R 2000 · 4.47 FIP	32	SP	4.47	16.1%	8.0%	3.1%	.291	3.68	16.4%	9.2%	185	-0.79	3.50	700	58
Orlando Hernandez R 2000 · 4.68 FIP	30	SP	4.68	17.2%	6.0%	4.2%	.255	4.38	18.6%	11.3%	241	-0.30	3.35	1,315	58
Lance Lynn R 2017 · 4.68 FIP	30	SP	4.68	19.7%	9.4%	3.7%	.237	3.25	25.9%	17.8%	449	-1.43	2.85	2,006	58
Doug Fister R 2014 · 3.80 FIP	30	SP	3.80	14.8%	3.6%	2.7%	.259	4.23	16.0%	8.8%	389	+0.43	2.92	1,438	55
Roberto Hernandez R 2011 · 4.32 FIP	30	SP	4.32	13.1%	6.8%	2.6%	.284	4.48	15.4%	8.5%	330	+0.16	3.63	1,435	54
Collin McHugh R 2017 · 3.60 FIP	30	SP	3.60	21.0%	7.2%	2.5%	.309	3.35	29.5%	21.0%	147	-0.25	1.77	1,008	54
Cory Lidle R 2002 · 3.67 FIP	30	SP	3.67	13.9%	4.5%	2.1%	.275	4.13	14.1%	8.3%	589	+0.46	3.48	1,318	54
Adam Wainwright R 2012 · 3.04 FIP	30	SP	3.04	22.1%	5.9%	1.9%	.310	2.58	21.3%	17.1%	497	-0.46	2.03	2,666	53
Marco Estrada R 2013 · 3.81 FIP	30	SP	3.81	23.0%	5.7%	3.5%	.260	4.34	20.4%	12.6%	508	+0.53	3.28	1,244	53
Kris Benson R 2005 · 4.56 FIP	30	SP	4.56	12.9%	6.0%	3.3%	.256	5.38	11.3%	4.1%	183	+0.82	3.53	1,244	52
Robert Person R 2000 · 3.80 FIP	30	SP	3.80	22.1%	12.7%	1.8%	.278	4.75	19.5%	9.1%	308	+0.95	3.41	885	52
Todd Ritchie R 2002 · 4.66 FIP	30	SP	4.66	12.4%	8.0%	2.9%	.331	4.78	11.5%	3.9%	28	+0.12	3.03	828	51
Mike Fiers R 2015 · 3.86 FIP	30	SP	3.86	23.7%	7.8%	3.2%	.277	4.73	19.9%	13.3%	494	+0.87	2.83	1,140	51

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	8.6	122	21.8%	6.0%	0.77	3.63
Alex Wood ('24)	6.3	114	22.1%	7.1%	0.91	3.78
Bob Moose ('67-'76, 14 WAR*)	6.5	145	20.0%*	7.0%*	0.80*	4.31*
John Smoltz ('09)	16.0	168	21.9%	6.9%	0.74	3.31
Wilbur Wood ('61-'78, 39 WAR*)	13.4	179	17.8%*	6.3%*	1.04*	3.75*
Julio Urías ('23)	3.6	102	24.3%	6.6%	1.05	3.11
Ken Forsch ('70-'86, 26 WAR*)	10.6	152	17.2%*	6.6%*	0.99*	4.00*