Nate Pearson

P Kansas City Royals

Age 30 RHP 6'6" / 255 lbs MLB peak 0.1 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MLBCHC	60	1	66	4.48	4.17	26.0%	7.4%	0.3
2025	28	MIX2 levels	49	1	59	3.94	3.22	24.5%	12.3%	0.9	K 21.5% · BB 11.4% · FIP 3.37
2026	29	MIX3 levels	49	2	55	4.45	3.70	21.1%	15.0%	0.5	K 20.5% · BB 13.0% · FIP 3.61
2027PROJ	30	MLBKC	41	0	43	4.45	4.33	23.0%	11.6%	0.1
2028PROJ95% MLB	31	MLBKC	26	0	27	4.52	4.56	22.3%	11.9%	-0.0
2029PROJ95% MLB	32	MLBKC	17	0	18	4.70	4.82	21.5%	12.3%	-0.1

Last 3 seasons WAR 1.8 · Next 3 projected WAR 0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

xwOBA vs42

K%38

Form8.76 ERA (proj 4.45) · K 21.7% (proj 23.0%) · BB 13.3% (proj 11.6%) ▼ last 60d · 12 IP through Oct 27

CompsDustin Molleken (age 30) · Joe Nelson (age 31) · Hirokazu Sawamura (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 114106vR 98

ERA4.45

FIP4.33

K%23.0%

BB%11.6%

K-BB%11.4%

Proj IP43

Value2027 · WAR

Runs saved vs league avg · 43 IP

Grades · Statcast · Contract

Scouting Grades

STUF5050

CTRL5050

CMD5050

K†5555

Statcast Percentiles

FB Velo

p9097.6

Whiff%

p3423.0%

p3820.5%

xwOBA vs

p420.321

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	20	ROK MLE	GCL Blue Jays	1	1	1	4	2	0	0	100%	1.0	50.0%	0.0%	50.0%	0.0%	0.00	-1.12	—
2017	20	ROK RAW	GCL Blue Jays	1	1	1	4	2	0	0	100%	1.0	50.0%	0.0%	50.0%	0.0%	0.00	-0.90	—
2018	21	A+ MLE	Dunedin Blue Jays	1	1	2	10	1	0	1	100%	1.7	10.0%	0.0%	10.0%	10.0%	10.80	7.74	—
2018	21	A+ RAW	Dunedin Blue Jays	1	1	2	10	1	0	1	100%	1.7	10.0%	0.0%	10.0%	10.0%	10.80	9.70	—
2019	22	MIX MLE	3 levels	25	25	102	388	119	27	8	100%	4.1	24.8%	7.7%	17.1%	2.5%	2.30	3.38	—
2019	22	MIX RAW	3 levels	25	25	102	388	119	27	8	100%	4.1	30.7%	7.0%	23.7%	2.1%	2.30	2.58	—
2020	23	MLB MLE	Toronto Blue Jays	5	4	18	81	16	13	5	80%	3.6	19.8%	16.0%	3.7%	6.2%	6.00	6.85	-0.3
2020	23	MLB RAW	Toronto Blue Jays	5	4	18	81	16	13	5	80%	3.6	19.8%	16.0%	3.7%	6.2%	6.00	7.10	-0.3
2021	24	MIX MLE	2 levels	24	7	46	200	64	24	6	29%	1.9	29.4%	12.3%	17.0%	3.0%	4.34	3.85	+0.4
2021	24	MIX RAW	2 levels	24	7	46	200	64	24	6	29%	1.9	32.0%	12.0%	20.0%	3.0%	4.34	3.58	+0.5
2022	25	MIX MLE	2 levels	13	1	15	60	19	8	2	8%	1.2	31.7%	14.7%	17.0%	4.6%	3.52	4.83	—
2022	25	MIX RAW	2 levels	13	1	15	60	19	8	2	8%	1.2	31.7%	13.3%	18.3%	3.3%	3.52	3.88	—
2023	26	MIX MLE	2 levels	55	0	63	273	77	31	8	0%	1.2	26.5%	10.8%	15.7%	3.0%	3.84	3.88	+0.5
2023	26	MIX RAW	2 levels	55	0	63	273	77	31	8	0%	1.2	28.2%	11.4%	16.8%	2.9%	3.84	3.78	+0.6
2024	27	MLB MLE	Chicago Cubs	60	1	66	285	74	21	12	2%	1.1	26.0%	7.4%	18.6%	4.4%	4.48	4.24	+0.2
2024	27	MLB MLE	Chicago Cubs	60	1	66	285	74	21	12	2%	1.1	26.0%	7.4%	18.6%	4.4%	4.48	4.24	+0.2
2024	27	MLB RAW	Chicago Cubs	60	1	66	285	74	21	12	2%	1.1	26.0%	7.4%	18.6%	4.2%	4.48	4.17	+0.3
2025	28	MIX MLE	2 levels	49	1	59	261	64	32	3	2%	1.2	21.5%	11.4%	10.1%	1.2%	3.94	3.37	+0.8
2025	28	MIX MLE	2 levels	49	1	59	261	64	32	3	2%	1.2	21.5%	11.4%	10.1%	1.2%	3.94	3.37	+0.8
2025	28	MIX RAW	2 levels	49	1	59	261	64	32	3	2%	1.2	24.5%	12.3%	12.3%	1.1%	3.94	3.22	+0.9
2026	29	MIX MLE	3 levels	49	2	55	246	52	37	2	4%	1.1	20.5%	13.0%	7.5%	1.1%	4.45	3.61	+0.6
2026	29	MIX MLE	3 levels	49	2	55	246	52	37	2	4%	1.1	20.5%	13.0%	7.5%	1.1%	4.45	3.61	+0.6
2026	29	MIX RAW	3 levels	49	2	55	246	52	37	2	4%	1.1	21.1%	15.0%	6.1%	0.8%	4.45	3.70	+0.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			165	6	203	894	201	96	30	4%	1.2	22.5%	10.7%	11.7%	3.4%	4.93	4.46	+0.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	KC	41	0	43	182	42	21	4	0%	1.0	23.0%	11.6%	11.4%	2.4%	4.45	4.33	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	KC	28	0	29	122	27	15	3	0%	1.0	22.3%	11.9%	10.3%	2.6%	4.52	4.56	-0.0
2029	32	MLB MLE	KC	18	0	19	80	17	10	2	0%	1.1	21.5%	12.3%	9.2%	2.9%	4.70	4.82	-0.1
2030	33	MLB MLE	KC	11	0	12	51	10	6	2	0%	1.1	20.6%	12.8%	7.8%	3.2%	4.94	5.10	-0.1
2028	31	MLB CTX	KC	26	0	27	114	25	14	3	0%	1.0	22.3%	11.9%	10.3%	2.6%	4.52	4.56	-0.0
2029	32	MLB CTX	KC	17	0	18	76	16	9	2	0%	1.1	21.5%	12.3%	9.2%	2.9%	4.70	4.82	-0.1
2030	33	MLB CTX	KC	10	0	11	46	10	6	1	0%	1.1	20.6%	12.8%	7.8%	3.2%	4.94	5.10	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		219	6	260	1134	253	125	37	3%	1.2	22.3%	11.1%	11.3%	3.2%	4.87	4.40	+0.5
/ 200 IP	—	RAW		169	5	200	874	195	96	29	3%	1.2	22.3%	11.1%	11.3%	3.2%	4.87	4.40	+0.4
Career	—	MLE		282	43	427	1808	445	188	51	15%	1.5	24.6%	10.4%	14.2%	2.8%	3.81	3.89	+0.1
/ 200 IP	—	MLE		132	20	200	847	209	88	24	15%	1.5	24.6%	10.4%	14.2%	2.8%	3.81	3.89	+0.1

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 AAASugar Land Space Cowboys · 7 IPFIP 4.12 → 4.10 (−0.02)K% 24.2% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.24%	—	×0.860	20.85%
BB% (unintentional) /BF	24.24%	—	×0.900	21.82%
HR / BF /BF	0.00%	÷1.000	×1.100	0.00%

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 4.12 → 4.10, 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).

2025 AAAIowa Cubs · 45 IPFIP 2.37 → 2.57 (+0.20)K% 30.6% → 26.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.65%	—	×0.860	26.35%
BB% (unintentional) /BF	12.37%	—	×0.900	11.13%
HR / BF /BF	0.54%	÷1.125	×1.100	0.53%

Result: FIP 2.37 → 2.57, 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 AAABuffalo Bisons · 21 IPFIP 2.64 → 2.99 (+0.35)K% 37.4% → 32.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.36%	—	×0.860	32.13%
BB% (unintentional) /BF	16.48%	—	×0.900	14.84%
HR / BF /BF	1.10%	÷0.916	×1.100	1.32%

Result: FIP 2.64 → 2.99, 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 AAABuffalo Bisons · 13 IPFIP 4.03 → 4.83 (+0.80)K% 36.0% → 31.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.00%	—	×0.880	31.68%
BB% (unintentional) /BF	14.00%	—	×1.050	14.70%
HR / BF /BF	4.00%	÷0.951	×1.100	4.62%

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 90).

Result: FIP 4.03 → 4.83, 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 AAABuffalo Bisons · 31 IPFIP 3.20 → 3.64 (+0.44)K% 34.1% → 30.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.11%	—	×0.880	30.02%
BB% (unintentional) /BF	10.08%	—	×1.050	10.58%
HR / BF /BF	3.10%	÷1.081	×1.100	3.16%

Result: FIP 3.20 → 3.64, 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 AANew Hampshire Fisher Cats · 63 IPFIP 2.70 → 3.62 (+0.92)K% 28.3% → 22.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.28%	—	×0.810	22.91%
BB% (unintentional) /BF	8.61%	—	×1.100	9.47%
HR / BF /BF	1.64%	÷0.860	×1.200	2.29%

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 72).

Result: FIP 2.70 → 3.62, 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 A+Dunedin Blue Jays · 21 IPFIP 1.13 → 2.18 (+1.05)K% 46.7% → 35.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	46.67%	—	×0.770	35.93%
BB% (unintentional) /BF	4.00%	—	×1.150	4.60%
HR / BF /BF	2.67%	÷1.194	×1.250	2.79%

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 139).

Result: FIP 1.13 → 2.18, 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 AAABuffalo Bisons · 18 IPFIP 3.41 → 3.81 (+0.41)K% 21.7% → 19.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.74%	—	×0.880	19.13%
BB% (unintentional) /BF	4.35%	—	×1.050	4.57%
HR / BF /BF	2.90%	÷1.000	×1.100	3.19%

Result: FIP 3.41 → 3.81, 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).

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
Nate Pearson (OGILVIE)	—	—	4.33	23.0%	11.6%	2.4%	—	4.75	21.7%	9.5%	60	+0.42	—	—	—
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	75
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	74
Hirokazu Sawamura R 2021 · 4.48 FIP	33	SU	4.48	26.2%	11.2%	4.0%	.263	4.02	18.1%	6.8%	51	-0.46	4.02	104	74
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	74
Brian Schlitter R 2016 · 3.28 FIP	30	SU	3.28	20.6%	10.8%	1.0%	.333	3.81	14.4%	6.6%	110	+0.53	3.27	300	73
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	71
Erik Davis R 2017 · 4.25 FIP	30	SU	4.25	22.2%	11.1%	3.0%	.349	3.52	17.4%	8.5%	65	-0.73	3.05	227	71
Jhan Mariñez R 2018 · 4.09 FIP	29	SU	4.09	21.2%	7.9%	3.2%	.331	—	—	—	—	—	3.16	253	71
Mike Broadway R 2017 · 4.73 FIP	30	SU	4.73	18.3%	10.2%	3.4%	.245	4.65	23.0%	12.4%	49	-0.08	1.74	195	71
Arquimedes Caminero R 2016 · 4.45 FIP	29	SU	4.45	17.9%	11.4%	2.6%	.314	4.62	18.6%	7.5%	38	+0.17	3.54	186	71
Kerry Ligtenberg R 2001 · 3.20 FIP	30	SU	3.20	22.0%	8.7%	1.6%	.267	3.95	18.5%	11.1%	181	+0.75	2.94	381	70
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	70
Bryan Mitchell R 2021 · 4.29 FIP	30	SU	4.29	20.6%	12.0%	2.5%	.305	5.07	14.4%	3.9%	39	+0.78	2.72	481	70
Fernando Rodriguez Jr. R 2015 · 3.34 FIP	31	SU	3.34	26.5%	9.6%	2.3%	.241	3.89	23.3%	13.2%	105	+0.55	3.34	335	69
Tyler Yates R 2008 · 3.90 FIP	30	SU	3.90	19.0%	11.2%	1.8%	.293	5.06	16.1%	5.4%	12	+1.16	3.23	248	69
Tom Wilhelmsen R 2014 · 3.45 FIP	30	SU	3.45	22.7%	9.5%	1.9%	.196	4.64	17.5%	7.8%	148	+1.19	2.72	398	69
Danny Farquhar R 2017 · 3.62 FIP	30	SU	3.62	22.3%	11.0%	1.8%	.247	—	—	—	—	—	1.67	351	69
Kiko Calero R 2005 · 3.51 FIP	30	SU	3.51	22.7%	7.0%	2.6%	.252	3.23	23.3%	13.9%	99	-0.28	2.27	298	69
Troy Percival R 2000 · 4.46 FIP	30	SU	4.46	22.2%	11.8%	3.2%	.252	3.15	28.2%	18.8%	163	-1.31	2.15	709	69
Manny Acosta R 2011 · 3.75 FIP	30	SU	3.75	22.5%	7.4%	2.9%	.321	4.49	21.3%	10.2%	47	+0.74	3.08	350	68

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	1.5	44	22.4%	11.4%	1.21	4.81
Buck Farmer ('24)	2.6	51	21.6%	10.5%	1.24	4.71
Fred Lasher ('63-'71, -1 WAR*)	1.0	50	21.5%*	11.9%*	1.05*	4.68*
Scott Service ('04)	2.1	46	22.2%	9.9%	1.23	5.04
Gene Harris ('89-'95, -1 WAR*)	1.2	40	20.0%*	12.5%*	0.98*	4.96*
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*
Sam Long ('25)	1.1	42	19.5%	9.7%	1.28	4.65