John Schreiber

P New York Yankees

Age 33 RHP 6'2" / 224 lbs MLB

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	30	MIX2 levels	61	1	54	4.02	2.82	20.9%	8.1%	1.1	K 20.5% · BB 8.5% · FIP 2.71
2025	31	MLBKC	74	0	64	3.80	4.04	23.4%	6.8%	0.4
2026	32	MLBNYY	66	0	60	3.28	4.38	16.3%	8.9%	0.1
2027PROJ	33	MLBNYY	44	0	46	4.24	4.56	18.8%	8.3%	-0.0
2028PROJ95% MLB	34	MLBNYY	23	0	24	4.61	4.86	17.9%	8.8%	-0.1
2029PROJ91% MLB	35	MLBNYY	11	0	12	4.96	5.20	16.9%	9.3%	-0.1

Last 3 seasons WAR 1.6 · Next 3 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs53

FB Velo40

K%10

Form2.29 ERA (proj 4.24) · K 14.5% (proj 18.8%) · BB 11.8% (proj 8.3%) ▼▼ last 90d · 20 IP through Oct 27

CompsJean Machi (age 33) · Rafael Betancourt (age 33) · Jared Burton (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 114106vR 98

ERA4.24

FIP4.56

K%18.8%

BB%8.3%

K-BB%10.5%

Proj IP46

Value2027 · WAR

Runs saved vs league avg · 46 IP

Grades · Statcast · Contract

Scouting Grades

STUF4545

CTRL5050

CMD5050

K†4545

Statcast Percentiles

FB Velo

p4093.8

Whiff%

p1920.9%

p1016.3%

xwOBA vs

p530.309

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)
2017	23	A MLE	West Michigan Whitecaps	27	0	50	182	70	7	0	0%	1.9	28.5%	4.5%	23.9%	0.0%	0.54	1.27	—
2017	23	A RAW	West Michigan Whitecaps	27	0	50	182	70	7	0	0%	1.9	38.5%	3.8%	34.6%	0.0%	0.54	0.74	—
2018	24	AA MLE	Erie SeaWolves	49	0	58	238	59	14	2	0%	1.2	20.1%	6.5%	13.6%	0.9%	2.48	2.72	—
2018	24	AA RAW	Erie SeaWolves	49	0	58	238	59	14	2	0%	1.2	24.8%	5.9%	18.9%	0.8%	2.48	2.24	—
2019	25	MIX MLE	3 levels	66	0	79	326	101	28	8	0%	1.2	27.1%	8.8%	18.4%	2.7%	2.95	3.41	+1.1
2019	25	MIX RAW	3 levels	66	0	79	326	101	28	8	0%	1.2	31.0%	8.6%	22.4%	2.5%	2.95	2.92	+1.5
2020	26	MLB MLE	Detroit Tigers	15	0	16	70	14	3	2	0%	1.0	20.0%	4.3%	15.7%	3.0%	6.32	3.59	+0.2
2020	26	MLB RAW	Detroit Tigers	15	0	16	70	14	3	2	0%	1.0	20.0%	4.3%	15.7%	2.9%	6.32	3.55	+0.2
2021	27	MIX MLE	2 levels	34	8	69	293	70	27	3	24%	2.0	20.4%	9.8%	10.7%	0.9%	2.73	3.10	+1.2
2021	27	MIX RAW	2 levels	34	8	69	293	70	27	3	24%	2.0	23.9%	9.2%	14.7%	1.0%	2.73	2.81	+1.5
2022	28	MIX MLE	2 levels	71	0	77	306	89	22	5	0%	1.1	28.8%	7.4%	21.4%	1.2%	2.09	2.26	+2.1
2022	28	MIX RAW	2 levels	71	0	77	306	89	22	5	0%	1.1	29.1%	7.2%	21.9%	1.6%	2.09	2.49	+1.9
2023	29	MIX MLE	3 levels	52	2	52	224	59	24	7	4%	1.0	26.0%	11.3%	14.7%	3.0%	3.61	3.99	+0.3
2023	29	MIX RAW	3 levels	52	2	52	224	59	24	7	4%	1.0	26.3%	10.7%	15.6%	3.1%	3.61	3.96	+0.4
2024	30	MIX MLE	2 levels	61	1	54	234	49	19	2	2%	0.9	20.5%	8.5%	12.1%	0.5%	4.02	2.71	+1.2
2024	30	MIX MLE	2 levels	61	1	54	234	49	19	2	2%	0.9	20.5%	8.5%	12.1%	0.5%	4.02	2.71	+1.2
2024	30	MIX RAW	2 levels	61	1	54	234	49	19	2	2%	0.9	20.9%	8.1%	12.8%	0.9%	4.02	2.82	+1.1
2025	31	MLB MLE	Kansas City Royals	74	0	64	265	62	18	10	0%	0.9	23.4%	6.8%	16.6%	4.1%	3.80	4.22	+0.2
2025	31	MLB MLE	Kansas City Royals	74	0	64	265	62	18	10	0%	0.9	23.4%	6.8%	16.6%	4.1%	3.80	4.22	+0.2
2025	31	MLB RAW	Kansas City Royals	74	0	64	265	62	18	10	0%	0.9	23.4%	6.8%	16.6%	3.8%	3.80	4.04	+0.4
2026	32	MLB MLE	New York Yankees	66	0	60	246	40	22	7	0%	0.9	16.3%	8.9%	7.3%	2.7%	3.28	4.32	+0.2
2026	32	MLB MLE	New York Yankees	66	0	60	246	40	22	7	0%	0.9	16.3%	8.9%	7.3%	2.7%	3.28	4.32	+0.2
2026	32	MLB RAW	New York Yankees	66	0	60	246	40	22	7	0%	0.9	16.3%	8.9%	7.3%	2.8%	3.28	4.38	+0.1
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			338	2	319	1338	313	109	32	1%	0.9	23.4%	8.1%	15.2%	2.4%	3.58	3.47	+4.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	NYY	44	0	46	194	37	16	6	0%	1.0	18.8%	8.3%	10.5%	2.9%	4.24	4.56	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	NYY	24	0	26	108	19	9	3	0%	1.1	17.9%	8.8%	9.1%	3.2%	4.61	4.86	-0.1
2029	35	MLB MLE	NYY	13	0	13	57	10	5	2	0%	1.0	16.9%	9.3%	7.6%	3.6%	4.96	5.20	-0.1
2028	34	MLB CTX	NYY	23	0	24	101	18	9	3	0%	1.0	17.9%	8.8%	9.1%	3.2%	4.61	4.86	-0.1
2029	35	MLB CTX	NYY	11	0	12	51	9	5	2	0%	1.1	16.9%	9.3%	7.6%	3.6%	4.96	5.20	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		373	2	356	1492	340	123	37	1%	1.0	22.8%	8.2%	14.6%	2.5%	3.70	3.59	+4.1
/ 200 IP	—	RAW		210	1	200	839	191	69	21	1%	1.0	22.8%	8.2%	14.6%	2.5%	3.70	3.59	+2.3
Career	—	MLE		515	11	580	2384	558	192	45	2%	1.1	23.4%	8.0%	15.4%	1.9%	2.93	3.17	+3.9
/ 200 IP	—	MLE		177	4	200	822	192	66	15	2%	1.1	23.4%	8.0%	15.4%	1.9%	2.93	3.17	+2.2

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.

2022 AAAWorcester Red Sox · 12 IPFIP 3.53 → 4.26 (+0.73)K% 30.6% → 26.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.61%	—	×0.880	26.94%
BB% (unintentional) /BF	6.12%	—	×1.050	6.43%
HR / BF /BF	4.08%	÷0.939	×1.100	4.78%

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 88).

Result: FIP 3.53 → 4.26, 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 AAAWorcester Red Sox · 66 IPFIP 2.90 → 3.10 (+0.20)K% 23.2% → 20.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.21%	—	×0.880	20.43%
BB% (unintentional) /BF	9.29%	—	×1.050	9.75%
HR / BF /BF	1.07%	÷1.318	×1.100	0.89%

Result: FIP 2.90 → 3.10, 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 AAAToledo Mud Hens · 59 IPFIP 2.66 → 3.23 (+0.57)K% 29.4% → 25.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.41%	—	×0.880	25.88%
BB% (unintentional) /BF	8.82%	—	×1.050	9.26%
HR / BF /BF	1.68%	÷0.888	×1.100	2.08%

Result: FIP 2.66 → 3.23, 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 AAErie SeaWolves · 7 IPFIP 2.81 → 3.47 (+0.66)K% 41.4% → 33.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	41.38%	—	×0.810	33.52%
BB% (unintentional) /BF	10.34%	—	×1.100	11.38%
HR / BF /BF	3.45%	÷1.294	×1.200	3.20%

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 159).

Result: FIP 2.81 → 3.47, 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).

2018 AAErie SeaWolves · 58 IPFIP 2.21 → 2.72 (+0.51)K% 24.8% → 20.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.79%	—	×0.810	20.08%
BB% (unintentional) /BF	5.88%	—	×1.100	6.47%
HR / BF /BF	0.84%	÷1.112	×1.200	0.91%

Result: FIP 2.21 → 2.72, 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 AWest Michigan Whitecaps · 50 IPFIP 0.34 → 1.27 (+0.93)K% 38.5% → 28.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.46%	—	×0.740	28.46%
BB% (unintentional) /BF	3.85%	—	×1.180	4.54%
HR / BF /BF	0.00%	÷0.849	×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 70).

Result: FIP 0.34 → 1.27, 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
John Schreiber (OGILVIE)	—	—	4.56	18.8%	8.3%	2.9%	—	4.98	17.6%	8.6%	39	+0.42	—	—	—
Jean Machi R 2015 · 4.56 FIP	33	SU	4.56	16.3%	8.6%	3.2%	.276	4.24	18.5%	11.9%	128	-0.32	2.16	306	84
Rafael Betancourt R 2008 · 4.12 FIP	33	SU	4.12	20.7%	6.5%	3.6%	.304	2.37	31.4%	27.3%	181	-1.75	1.79	686	78
Jared Burton R 2014 · 3.90 FIP	33	SU	3.90	16.9%	8.1%	2.2%	.263	3.99	25.1%	15.7%	14	+0.09	3.09	367	78
Tony Barnette R 2017 · 3.56 FIP	33	SU	3.56	22.6%	7.1%	2.7%	.335	2.61	25.0%	20.2%	26	-0.95	2.61	144	77
Ben Weber R 2003 · 3.66 FIP	33	SU	3.66	13.9%	4.5%	2.1%	.292	5.03	10.4%	-2.2%	35	+1.37	2.57	284	77
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	76
Juan Sandoval R 2015 · 3.82 FIP	34	SU	3.82	15.4%	6.0%	2.3%	.294	3.33	17.4%	8.8%	116	-0.49	3.24	225	75
Jose Manuel Lopez R 2017 · 3.61 FIP	32	SU	3.61	20.4%	10.0%	1.8%	.331	6.14	17.9%	3.7%	41	+2.53	3.50	170	75
Ryan Brasier R 2021 · 4.85 FIP	33	SU	4.85	18.0%	8.0%	4.1%	.286	3.16	23.7%	17.9%	150	-1.69	2.31	426	74
Mike James R 2001 · 4.52 FIP	33	SU	4.52	15.0%	8.7%	2.9%	.299	4.84	19.6%	9.8%	11	+0.32	2.87	314	74
D.J. Carrasco R 2010 · 3.45 FIP	33	SU	3.45	19.7%	9.4%	1.5%	.275	4.47	12.0%	6.7%	49	+1.02	3.23	490	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	73
Mike Buddie R 2002 · 4.26 FIP	31	SU	4.26	15.1%	7.6%	2.7%	.297	—	—	—	—	—	3.76	123	73
Tyler Walker R 2009 · 3.72 FIP	33	SU	3.72	18.0%	5.3%	2.7%	.243	3.77	20.7%	16.6%	35	+0.05	2.38	289	73
Felipe Arredondo R 2019 · 4.65 FIP	32	SU	4.65	16.2%	7.1%	3.7%	.272	—	—	—	—	—	3.92	337	73
Tyler Sturdevant R 2017 · 4.48 FIP	31	SU	4.48	13.5%	6.7%	3.0%	.300	—	—	—	—	—	3.44	149	73
Shigetoshi Hasegawa R 2002 · 3.69 FIP	33	SU	3.69	13.5%	7.6%	1.4%	.251	3.77	12.4%	5.9%	208	+0.08	3.60	720	72
Jumbo Diaz R 2017 · 4.17 FIP	33	SU	4.17	20.0%	9.7%	2.8%	.302	2.90	24.2%	15.4%	98	-1.27	2.80	319	72
Shawn Camp R 2009 · 3.73 FIP	33	SU	3.73	17.4%	7.5%	2.1%	.272	3.57	14.4%	9.7%	216	-0.16	2.79	589	72
Arcenio Leon R 2019 · 3.61 FIP	32	SU	3.61	21.4%	8.1%	2.4%	.314	—	—	—	—	—	3.00	115	72

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	2.0	51	22.3%	8.8%	0.99	3.77
Neftalí Feliz ('21)	2.0	49	22.9%	9.7%	1.05	3.55
Scott Oberg ('19)	1.3	51	21.5%	9.3%	0.91	3.85
Ryan Tepera ('23)	1.8	52	24.3%	8.8%	1.13	3.62
Dave Baldwin ('66-'73, 2 WAR*)	1.1	56	22.2%*	9.2%*	0.96*	3.97*
Mike Perez ('90-'97, 3 WAR*)	1.7	58	18.5%*	8.2%*	0.88*	3.86*
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*