Jack Flaherty

P Detroit Tigers

Age 31 RHP 6'4" / 225 lbs MLB Svc 9.0 · Ctrl thru 2027 peak 1.5 · 2027

Overview

MLBSP40-MANExt / FA-dealFA 2028

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	28	MLBLAD	28	28	162	3.17	3.33	29.9%	5.9%	4.1
2025	29	MLBDET	31	31	161	4.64	3.70	27.6%	8.5%	3.3
2026	30	MIX3 levels	23	22	98	4.24	3.80	26.5%	9.9%	1.9	K 26.0% · BB 10.4% · FIP 3.81
2027PROJ	31	MLBDET	15	15	89	3.97	3.97	27.5%	8.0%	1.5
2028PROJ	32	MLBDET	16	16	98	4.05	4.15	26.7%	8.3%	1.4
2029PROJ	33	MLBDET	15	15	90	4.27	4.42	25.9%	8.8%	0.9

Last 3 seasons WAR 9.3 · Next 3 projected WAR 3.8Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service9.035First FA2028

2027Ext / FA-deal$12M

Skills · percentileStatcast 2026

K%71

xwOBA vs42

FB Velo25

CompsTim Adleman (age 31) · Matt Shoemaker (age 31) · Chase Anderson (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 110102vR 93

ERA3.97

FIP3.97

K%27.5%

BB%8.0%

K-BB%19.6%

Proj IP89

Value2027 · WAR

Runs saved vs league avg · 89 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2592.7

Whiff%

p6227.1%

p7126.0%

xwOBA vs

p420.321

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

Status FA

MLB Debut 2017

Service 9.035 yrs.days

Team Control Through 2027

First FA Year 2028

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext / FA-deal	$12M	+1.5	$12M	+$0.1M
Totals		$12M		$12M	+$0.1M

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	19	A MLE	Peoria Chiefs	18	18	95	410	97	31	2	100%	5.3	17.5%	8.9%	8.6%	0.6%	2.84	3.10	—
2015	19	A RAW	Peoria Chiefs	18	18	95	410	97	31	2	100%	5.3	23.7%	7.6%	16.1%	0.5%	2.84	2.31	—
2016	20	A+ MLE	Palm Beach Cardinals	24	23	134	564	126	45	8	96%	5.6	17.2%	9.2%	8.0%	1.8%	3.56	3.80	—
2016	20	A+ RAW	Palm Beach Cardinals	24	23	134	564	126	45	8	96%	5.6	22.3%	8.0%	14.4%	1.4%	3.56	3.00	—
2017	21	MIX MLE	3 levels	31	30	170	675	167	42	16	97%	5.5	21.5%	6.5%	14.9%	2.6%	2.70	3.53	+3.9
2017	21	MIX RAW	3 levels	31	30	170	675	167	42	16	97%	5.5	24.7%	6.2%	18.5%	2.4%	2.70	3.10	+4.9
2018	22	MIX MLE	2 levels	33	33	183	741	223	63	22	100%	5.5	29.4%	8.5%	20.9%	3.2%	3.15	3.47	+4.3
2018	22	MIX RAW	2 levels	33	33	183	741	223	63	22	100%	5.5	30.1%	8.5%	21.6%	3.0%	3.15	3.26	+4.8
2019	23	MLB MLE	St. Louis Cardinals	33	33	196	772	231	53	25	100%	5.9	29.9%	6.9%	23.1%	3.5%	2.75	3.35	+5.0
2019	23	MLB RAW	St. Louis Cardinals	33	33	196	772	231	53	25	100%	5.9	29.9%	6.9%	23.1%	3.2%	2.75	3.21	+5.3
2020	24	MLB MLE	St. Louis Cardinals	9	9	40	170	49	16	6	100%	4.5	28.8%	9.4%	19.4%	3.8%	4.91	3.94	+0.7
2020	24	MLB RAW	St. Louis Cardinals	9	9	40	170	49	16	6	100%	4.5	28.8%	9.4%	19.4%	3.5%	4.91	3.79	+0.8
2021	25	MIX MLE	3 levels	20	18	87	359	96	26	13	90%	4.4	26.4%	7.8%	18.6%	4.0%	3.40	4.05	+1.4
2021	25	MIX RAW	3 levels	20	18	87	359	96	26	13	90%	4.4	26.7%	7.2%	19.5%	3.6%	3.40	3.73	+1.8
2022	26	MIX MLE	3 levels	16	15	63	275	67	29	6	94%	3.9	22.3%	11.4%	10.9%	1.9%	3.71	3.68	+1.3
2022	26	MIX RAW	3 levels	16	15	63	275	67	29	6	94%	3.9	24.4%	10.5%	13.8%	2.2%	3.71	3.59	+1.4
2023	27	MLB MLE	Baltimore Orioles	29	27	144	650	148	64	17	93%	5.0	22.8%	9.8%	12.9%	2.6%	4.99	3.87	+2.6
2023	27	MLB RAW	Baltimore Orioles	29	27	144	650	148	64	17	93%	5.0	22.8%	9.8%	12.9%	2.6%	4.99	3.91	+2.6
2024	28	MLB MLE	Los Angeles Dodgers	28	28	162	649	194	38	24	100%	5.8	29.9%	5.9%	24.0%	3.5%	3.17	3.26	+4.3
2024	28	MLB MLE	Los Angeles Dodgers	28	28	162	649	194	38	24	100%	5.8	29.9%	5.9%	24.0%	3.5%	3.17	3.26	+4.3
2024	28	MLB RAW	Los Angeles Dodgers	28	28	162	649	194	38	24	100%	5.8	29.9%	5.9%	24.0%	3.7%	3.17	3.33	+4.1
2025	29	MLB MLE	Detroit Tigers	31	31	161	681	188	58	23	100%	5.2	27.6%	8.5%	19.1%	3.5%	4.64	3.78	+3.1
2025	29	MLB MLE	Detroit Tigers	31	31	161	681	188	58	23	100%	5.2	27.6%	8.5%	19.1%	3.5%	4.64	3.78	+3.1
2025	29	MLB RAW	Detroit Tigers	31	31	161	681	188	58	23	100%	5.2	27.6%	8.5%	19.1%	3.4%	4.64	3.70	+3.3
2026	30	MIX MLE	3 levels	23	22	98	434	115	43	13	96%	4.2	26.0%	10.4%	15.7%	2.9%	4.24	3.81	+1.9
2026	30	MIX MLE	3 levels	23	22	98	434	115	43	13	96%	4.2	26.0%	10.4%	15.7%	2.9%	4.24	3.81	+1.9
2026	30	MIX RAW	3 levels	23	22	98	434	115	43	13	96%	4.2	26.5%	9.9%	16.6%	3.0%	4.24	3.80	+1.9
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			210	203	1079	4516	1233	382	146	97%	5.1	27.3%	8.5%	18.8%	3.2%	3.85	3.64	+23.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	DET	15	15	89	376	103	30	12	100%	5.9	27.5%	8.0%	19.6%	3.3%	3.97	3.97	+1.5
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	DET	16	16	98	416	111	35	15	100%	6.2	26.7%	8.3%	18.4%	3.6%	4.13	4.23	+1.4
2029	33	MLB MLE	DET	15	15	90	381	99	34	15	100%	6.0	25.9%	8.8%	17.1%	3.8%	4.36	4.51	+0.9
2030	34	MLB MLE	DET	13	13	75	318	79	30	13	100%	5.8	25.0%	9.3%	15.7%	4.1%	4.63	4.81	+0.5
2028	32	MLB CTX	DET	16	16	98	414	111	35	14	100%	6.1	26.7%	8.3%	18.4%	3.4%	4.05	4.15	+1.4
2029	33	MLB CTX	DET	15	15	90	380	98	33	14	100%	6.0	25.9%	8.8%	17.1%	3.7%	4.27	4.42	+0.9
2030	34	MLB CTX	DET	12	12	75	317	79	29	13	100%	6.2	25.0%	9.3%	15.7%	4.0%	4.53	4.71	+0.5
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		254	247	1343	5631	1522	480	187	97%	5.3	27.0%	8.5%	18.5%	3.3%	3.93	3.72	+27.3
/ 200 IP	—	RAW		38	37	200	839	227	71	28	97%	5.3	27.0%	8.5%	18.5%	3.3%	3.93	3.72	+4.1
Career	—	MLE		295	287	1534	6380	1611	529	184	97%	5.2	25.2%	8.3%	17.0%	2.9%	3.55	3.60	+25.8
/ 200 IP	—	MLE		38	37	200	832	210	69	24	97%	5.2	25.2%	8.3%	17.0%	2.9%	3.55	3.60	+3.8

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 AAErie SeaWolves · 6 IPFIP 5.98 → 7.54 (+1.57)K% 31.8% → 25.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.82%	—	×0.810	25.77%
BB% (unintentional) /BF	4.55%	—	×1.100	5.00%
HR / BF /BF	9.09%	÷1.000	×1.200	10.91%

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 5.98 → 7.54, 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 AASpringfield Cardinals · 17 IPFIP 0.89 → 1.54 (+0.65)K% 35.4% → 28.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.38%	—	×0.810	28.66%
BB% (unintentional) /BF	6.15%	—	×1.100	6.77%
HR / BF /BF	0.00%	÷1.069	×1.200	0.00%

Result: FIP 0.89 → 1.54, 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 AAAMemphis Redbirds · 10 IPFIP 4.38 → 4.81 (+0.43)K% 25.6% → 22.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.58%	—	×0.880	22.51%
BB% (unintentional) /BF	6.98%	—	×1.050	7.33%
HR / BF /BF	4.65%	÷1.048	×1.100	4.88%

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 110).

Result: FIP 4.38 → 4.81, 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 AAAMemphis Redbirds · 5 IPFIP 0.49 → 0.90 (+0.42)K% 38.1% → 33.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.10%	—	×0.880	33.52%
BB% (unintentional) /BF	4.76%	—	×1.050	5.00%
HR / BF /BF	0.00%	÷0.949	×1.100	0.00%

Result: FIP 0.49 → 0.90, 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 · 32 IPFIP 1.93 → 2.47 (+0.55)K% 32.5% → 28.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.54%	—	×0.880	28.63%
BB% (unintentional) /BF	5.56%	—	×1.050	5.83%
HR / BF /BF	1.59%	÷0.909	×1.100	1.92%

Result: FIP 1.93 → 2.47, 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 AAAMemphis Redbirds · 85 IPFIP 3.42 → 3.92 (+0.49)K% 25.1% → 22.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.07%	—	×0.880	22.06%
BB% (unintentional) /BF	6.49%	—	×1.050	6.81%
HR / BF /BF	2.95%	÷0.980	×1.100	3.31%

Result: FIP 3.42 → 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).

2017 AASpringfield Cardinals · 63 IPFIP 1.97 → 2.39 (+0.43)K% 25.6% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.62%	—	×0.810	20.75%
BB% (unintentional) /BF	4.55%	—	×1.100	5.00%
HR / BF /BF	0.83%	÷1.315	×1.200	0.75%

Result: FIP 1.97 → 2.39, 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 A+Palm Beach Cardinals · 134 IPFIP 3.00 → 3.80 (+0.80)K% 22.3% → 17.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.34%	—	×0.770	17.20%
BB% (unintentional) /BF	7.98%	—	×1.150	9.18%
HR / BF /BF	1.42%	÷0.979	×1.250	1.81%

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 96).

Result: FIP 3.00 → 3.80, 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
Jack Flaherty (OGILVIE)	—	—	—	—	—	3.3%	—	4.49	25.9%	17.2%	264	+0.52	—	—	—
Tim Adleman R 2019 · 4.30 FIP	31	SP	4.30	22.8%	7.8%	3.9%	.266	4.71	18.5%	11.1%	90	+0.41	3.63	605	65
Matt Shoemaker R 2018 · 3.17 FIP	31	SP	3.17	25.4%	7.7%	2.3%	.310	4.87	19.3%	11.5%	188	+1.70	3.12	728	62
Chase Anderson R 2019 · 4.39 FIP	31	SP	4.39	20.9%	8.1%	3.7%	.259	5.39	19.2%	10.0%	218	+1.00	3.30	1,260	62
Miguel González R 2015 · 4.53 FIP	31	SP	4.53	17.5%	7.9%	3.5%	.289	4.23	15.9%	9.0%	325	-0.30	3.72	905	59
Yu Darvish R 2018 · 4.50 FIP	31	SP	4.50	27.2%	11.7%	4.0%	.282	3.48	30.5%	24.0%	421	-1.02	1.97	1,798	59
Kenta Maeda R 2019 · 3.68 FIP	31	SP-	3.68	27.1%	8.0%	3.4%	.240	3.44	27.8%	22.0%	173	-0.24	2.87	1,092	58
Hideki Irabu R 2000 · 4.38 FIP	31	SP	4.38	17.0%	5.7%	3.6%	.374	5.19	17.2%	11.0%	64	+0.81	3.78	514	57
Mike Fiers R 2016 · 4.30 FIP	31	SP	4.30	18.5%	5.8%	3.7%	.308	4.96	19.2%	12.2%	510	+0.66	2.83	1,140	54
Brad Peacock R 2019 · 4.22 FIP	31	SP-	4.22	25.1%	8.1%	4.0%	.261	4.15	22.8%	12.5%	96	-0.07	2.97	748	53
Merrill Kelly R 2020 · 3.87 FIP	31	SP	3.87	23.2%	3.2%	4.2%	.241	3.79	22.6%	15.0%	536	-0.08	3.60	1,170	50
Chuck Smith R 2001 · 3.99 FIP	31	SP	3.99	18.4%	8.1%	2.6%	.289	—	—	—	—	—	3.08	211	50
Tanner Roark R 2018 · 3.99 FIP	31	SP	3.99	19.2%	6.2%	3.2%	.289	4.57	21.1%	13.1%	259	+0.58	2.40	1,194	50
Hideo Nomo R 2000 · 4.66 FIP	31	SP	4.66	21.9%	10.6%	3.7%	.303	4.13	22.0%	11.4%	637	-0.53	2.76	1,972	49
Stan Spencer R 2000 · 4.42 FIP	30	SP	4.42	19.2%	8.7%	3.4%	.259	—	—	—	—	—	3.61	119	49
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	47
Jose Samayoa R 2019 · 4.05 FIP	29	SP	4.05	19.4%	7.0%	3.1%	.314	—	—	—	—	—	3.44	262	46
Jose Oyervides R 2015 · 3.01 FIP	33	SP	3.01	19.1%	6.2%	1.4%	.333	4.09	21.9%	14.5%	184	+1.08	3.01	407	46
Jarrett Grube R 2015 · 3.75 FIP	33	SP	3.75	18.7%	4.2%	3.1%	.285	4.57	17.3%	9.6%	253	+0.82	3.75	393	45
Jeremy Hefner R 2016 · 4.64 FIP	30	SP	4.64	16.8%	9.1%	3.3%	.305	—	—	—	—	—	3.56	322	45
Tom Koehler R 2017 · 4.66 FIP	31	SP-	4.66	22.4%	9.0%	4.2%	.298	—	—	—	—	—	3.76	822	45

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	7.2	102	27.1%	8.7%	1.29	3.95
Frankie Montas ('25)	3.9	98	23.6%	8.2%	1.20	4.20
Kenta Maeda ('25)	4.9	123	25.8%	7.0%	1.30	4.20
Jose DeLeon ('83-'95, 22 WAR*)	9.5	146	25.8%*	10.4%*	1.01*	4.17*
Bob Johnson ('69-'77, 6 WAR*)	3.5	115	23.4%*	8.9%*	1.49*	4.12*
Danny Duffy ('21)	5.9	107	21.1%	8.7%	1.11	3.95
Bruce Kison ('71-'85, 16 WAR*)	9.0	121	20.4%*	8.5%*	1.10*	4.16*