TJ Shook

P Albuquerque Isotopes

Age 29 RHP 6'4" / 217 lbs AAA

Overview

AAARP40-MANROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	26	AANYM	30	11	85	4.66	2.98	29.2%	7.9%	—	K 23.7% · BB 8.7% · FIP 4.15
2025	27	MIX2 levels	36	2	56	2.41	2.99	26.4%	7.4%	—	K 22.2% · BB 6.5% · FIP 3.35
2026	28	MIX2 levels	53	0	65	4.02	4.56	20.8%	11.3%	-0.0	K 18.0% · BB 10.9% · FIP 4.42
2027PROJ	29	MLBCOL	87	0	91	4.43	4.43	19.6%	8.1%	0.1
2028PROJ76% MLB	30	MLBCOL	75	0	79	4.52	4.59	19.5%	8.4%	-0.0
2029PROJ82% MLB	31	MLBCOL	67	0	70	4.65	4.76	19.3%	8.7%	-0.2

Last 3 seasons WAR -0.0 · Next 3 projected WAR -0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff50Control60Command60

Career WAR0.0 WAR median (P10 -0.2 / P90 0.3, comp-based) · star 0% · didn't stick 96%

CompsDiego Moreno (age 29) · Yoanner Negrin (age 31) · Ramon Delgado (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117109vR 101

ERA4.43

FIP4.43

K%19.6%

BB%8.1%

K-BB%11.5%

Proj IP134

Value2027 · WAR

Runs saved vs league avg · 134 IP

Grades · Contract

Scouting Grades

STUF5050

CTRL6060

CMD6060

K†4545

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)
2021	23	MIX MLE	3 levels	13	13	53	229	71	28	7	100%	4.1	21.7%	13.8%	8.0%	3.4%	4.96	4.90	—
2021	23	MIX RAW	3 levels	13	13	53	229	71	28	7	100%	4.1	31.0%	12.2%	18.8%	3.1%	4.96	3.73	—
2022	24	MIX MLE	2 levels	25	21	114	512	118	57	22	84%	4.6	18.3%	12.4%	6.0%	5.3%	4.74	6.01	—
2022	24	MIX RAW	2 levels	25	21	114	512	118	57	22	84%	4.6	23.0%	11.1%	11.9%	4.3%	4.74	5.04	—
2023	25	AA MLE	Biloxi Shuckers	25	12	97	412	107	36	13	48%	3.9	21.0%	9.6%	11.4%	3.6%	4.62	4.51	—
2023	25	AA RAW	Biloxi Shuckers	25	12	97	412	107	36	13	48%	3.9	26.0%	8.7%	17.2%	3.2%	4.62	3.75	—
2024	26	AA MLE	Binghamton Rumble Ponies	30	11	85	366	107	29	9	37%	2.8	23.7%	8.7%	15.0%	3.5%	4.66	4.15	—
2024	26	AA MLE	Binghamton Rumble Ponies	30	11	85	366	107	29	9	37%	2.8	23.7%	8.7%	15.0%	3.5%	4.66	4.15	—
2024	26	AA RAW	Binghamton Rumble Ponies	30	11	85	366	107	29	9	37%	2.8	29.2%	7.9%	21.3%	2.5%	4.66	2.98	—
2025	27	MIX MLE	2 levels	36	2	56	231	61	17	5	6%	1.6	22.2%	6.5%	15.8%	2.4%	2.41	3.35	—
2025	27	MIX MLE	2 levels	36	2	56	231	61	17	5	6%	1.6	22.2%	6.5%	15.8%	2.4%	2.41	3.35	—
2025	27	MIX RAW	2 levels	36	2	56	231	61	17	5	6%	1.6	26.4%	7.4%	19.0%	2.2%	2.41	2.99	—
2026	28	MIX MLE	2 levels	53	0	65	284	59	32	9	0%	1.2	18.0%	10.9%	7.1%	2.7%	4.02	4.42	+0.1
2026	28	MIX MLE	2 levels	53	0	65	284	59	32	9	0%	1.2	18.0%	10.9%	7.1%	2.7%	4.02	4.42	+0.1
2026	28	MIX RAW	2 levels	53	0	65	284	59	32	9	0%	1.2	20.8%	11.3%	9.5%	3.2%	4.02	4.56	-0.0
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			5	0	8	36	7	2	3	0%	1.5	19.4%	5.6%	13.9%	8.3%	8.22	7.14	-0.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	COL	87	0	91	384	75	31	11	0%	1.0	19.6%	8.1%	11.5%	2.9%	4.43	4.43	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	COL	98	0	103	434	85	36	13	0%	1.1	19.5%	8.4%	11.2%	3.1%	4.52	4.59	-0.0
2029	31	MLB MLE	COL	82	0	86	362	70	31	12	0%	1.0	19.3%	8.7%	10.6%	3.3%	4.65	4.76	-0.2
2028	30	MLB CTX	COL	75	0	79	334	65	28	10	0%	1.1	19.5%	8.4%	11.2%	3.1%	4.52	4.59	-0.0
2029	31	MLB CTX	COL	67	0	70	296	57	26	10	0%	1.0	19.3%	8.7%	10.6%	3.3%	4.65	4.76	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		147	0	157	665	129	55	23	0%	1.1	19.4%	8.3%	11.1%	3.5%	4.76	4.42	+0.2
/ 200 IP	—	RAW		188	0	200	849	165	70	29	0%	1.1	19.4%	8.3%	11.1%	3.5%	4.76	4.42	+0.3
Career	—	MLE		182	59	470	2034	420	212	76	32%	2.6	20.6%	10.4%	10.2%	3.7%	4.35	4.72	-0.5
/ 200 IP	—	MLE		77	25	200	866	179	90	32	32%	2.6	20.6%	10.4%	10.2%	3.7%	4.35	4.72	-0.5

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 AAAAlbuquerque Isotopes · 57 IPFIP 4.19 → 4.42 (+0.23)K% 21.0% → 18.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.97%	—	×0.860	18.03%
BB% (unintentional) /BF	12.10%	—	×0.900	10.89%
HR / BF /BF	2.42%	÷1.000	×1.100	2.66%

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 4.19 → 4.42, 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).

2025 AABinghamton Rumble Ponies · 50 IPFIP 2.60 → 3.35 (+0.74)K% 27.5% → 22.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.45%	—	×0.810	22.24%
BB% (unintentional) /BF	5.88%	—	×1.100	6.47%
HR / BF /BF	1.96%	÷0.990	×1.200	2.38%

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 98).

Result: FIP 2.60 → 3.35, 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 AAASyracuse Mets · 6 IPFIP 5.91 → 5.78 (−0.13)K% 18.5% → 15.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.52%	—	×0.860	15.93%
BB% (unintentional) /BF	18.52%	—	×0.900	16.67%
HR / BF /BF	3.70%	÷1.167	×1.100	3.49%

Result: FIP 5.91 → 5.78, 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 AABinghamton Rumble Ponies · 85 IPFIP 2.98 → 4.15 (+1.16)K% 29.2% → 23.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.23%	—	×0.810	23.68%
BB% (unintentional) /BF	7.92%	—	×1.100	8.72%
HR / BF /BF	2.46%	÷0.834	×1.200	3.54%

Result: FIP 2.98 → 4.15, 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 AABiloxi Shuckers · 97 IPFIP 3.75 → 4.51 (+0.76)K% 26.0% → 21.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.97%	—	×0.810	21.04%
BB% (unintentional) /BF	8.74%	—	×1.100	9.61%
HR / BF /BF	3.16%	÷1.055	×1.200	3.59%

Result: FIP 3.75 → 4.51, 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).

2022 AABiloxi Shuckers · 80 IPFIP 5.58 → 6.54 (+0.96)K% 19.8% → 16.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.84%	—	×0.810	16.07%
BB% (unintentional) /BF	12.17%	—	×1.100	13.39%
HR / BF /BF	4.76%	÷1.012	×1.200	5.65%

Result: FIP 5.58 → 6.54, 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).

2022 A+Wisconsin Timber Rattlers · 34 IPFIP 3.07 → 4.52 (+1.46)K% 32.1% → 24.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.09%	—	×0.770	24.71%
BB% (unintentional) /BF	8.21%	—	×1.150	9.44%
HR / BF /BF	2.99%	÷0.885	×1.250	4.22%

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 77).

Result: FIP 3.07 → 4.52, 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).

2021 ACarolina Mudcats · 28 IPFIP 3.10 → 4.48 (+1.38)K% 31.3% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.30%	—	×0.740	23.17%
BB% (unintentional) /BF	9.57%	—	×1.180	11.29%
HR / BF /BF	2.61%	÷0.974	×1.300	3.48%

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 95).

Result: FIP 3.10 → 4.48, 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
TJ Shook (OGILVIE)	—	—	4.43	19.6%	8.1%	2.9%	—	4.67	19.4%	10.9%	189	+0.24	—	—	—
Diego Moreno R 2016 · 3.75 FIP	29	SU	3.75	21.0%	8.2%	2.5%	.347	3.13	17.8%	11.9%	54	-0.62	2.29	187	77
Yoanner Negrin R 2015 · 3.79 FIP	31	SW	3.79	15.1%	7.5%	1.9%	.256	3.29	15.0%	8.5%	270	-0.50	3.15	485	75
Ramon Delgado R 2016 · 4.60 FIP	29	SW	4.60	13.8%	5.9%	3.5%	.332	4.31	11.2%	7.2%	74	-0.29	4.31	135	74
Angel Castro R 2015 · 4.50 FIP	32	SU	4.50	15.9%	8.0%	3.1%	.268	4.28	13.6%	5.5%	231	-0.22	3.51	292	71
Neil Wagner R 2013 · 4.02 FIP	29	SU	4.02	20.5%	7.5%	3.1%	.306	4.05	19.8%	7.5%	43	+0.03	3.11	135	71
Tyler Sturdevant R 2015 · 4.37 FIP	29	SU	4.37	21.3%	8.5%	3.6%	.241	3.97	18.5%	11.9%	118	-0.40	3.44	149	70
Dustin Molleken R 2015 · 3.74 FIP	30	SU	3.74	20.4%	11.7%	1.6%	.287	3.90	20.6%	8.4%	77	+0.16	3.38	129	70
Rodolfo Aguirre R 2015 · 4.29 FIP	30	SW	4.29	12.4%	6.9%	2.5%	.288	4.81	11.4%	1.3%	125	+0.52	3.39	212	69
Jim Miller R 2012 · 4.56 FIP	30	SU	4.56	20.9%	11.8%	3.1%	.243	3.95	18.8%	14.7%	74	-0.61	3.95	140	69
Jason Urquidez R 2015 · 3.59 FIP	32	SU	3.59	15.6%	9.3%	1.1%	.386	1.81	25.5%	20.7%	90	-1.78	1.25	123	69
Carlos Fisher R 2011 · 4.33 FIP	28	SU	4.33	15.5%	9.1%	2.5%	.275	—	—	—	—	—	2.48	245	69
Gus Schlosser R 2016 · 3.92 FIP	27	SU	3.92	18.7%	5.6%	3.1%	.364	—	—	—	—	—	3.92	107	69
Evan Scribner R 2013 · 4.06 FIP	28	SU	4.06	16.7%	6.1%	2.9%	.271	4.59	26.9%	25.2%	60	+0.53	3.23	136	69
Fredy Quintero R 2017 · 4.45 FIP	29	SW	4.45	14.0%	8.7%	2.6%	.335	4.62	17.2%	3.5%	43	+0.17	4.29	241	69
Marc Magliaro R 2018 · 3.94 FIP	28	SU	3.94	19.6%	7.3%	2.9%	.307	—	—	—	—	—	3.94	125	69
Wirfin Obispo R 2015 · 4.80 FIP	30	SU	4.80	18.6%	12.2%	3.1%	.292	1.31	28.6%	24.5%	126	-3.49	1.30	218	68
Theo Denlinger R 2024 · 4.23 FIP	28	SU	4.23	15.3%	5.4%	3.2%	.324	—	—	—	—	—	2.92	140	68
Ryan Jensen R 2005 · 4.35 FIP	29	SW	4.35	15.7%	5.2%	3.5%	.310	—	—	—	—	—	4.35	253	68
Thad Weber R 2013 · 5.18 FIP	28	SU	5.18	16.4%	13.1%	3.3%	.244	4.52	12.2%	6.0%	308	-0.66	4.40	378	68
Michael Mariot R 2018 · 4.62 FIP	29	SU	4.62	21.8%	7.5%	4.4%	.328	4.65	17.7%	9.5%	136	+0.03	2.40	701	68

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	1.7	110	19.5%	8.3%	1.24	4.60
James Kaprielian ('23)	1.6	106	20.5%	9.7%	1.30	4.61
Daniel Mengden ('22)	1.5	77	17.5%	7.8%	1.25	4.65
Colby Lewis ('16)	6.1	135	18.4%	7.2%	1.32	4.70
Tommy Boggs ('76-'85, 3 WAR*)	2.9	97	16.4%*	8.1%*	1.24*	4.77*
John Dopson ('85-'94, 6 WAR*)	3.6	145	16.5%*	8.2%*	1.25*	4.36*
Dave Stenhouse ('62-'64, 2 WAR*)	1.9	124	17.9%*	10.5%*	1.33*	4.51*