Matt Moore

P Omaha Storm Chasers

Age 38 LHP 6'3" / 210 lbs AAA

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	34	MLBMIA	50	0	53	2.56	3.23	27.5%	5.5%	0.8
2024	35	MLBLAA	51	0	48	5.03	5.85	19.5%	11.4%	-0.7
2026	37	MIX2 levels	18	2	21	4.29	3.39	27.2%	10.9%	—	K 22.0% · BB 11.0% · FIP 4.10
2027PROJ	38	MLBKC	14	0	15	4.40	4.57	22.4%	10.0%	-0.0
2028PROJ	39	MLBKC	90	0	94	4.61	4.86	21.3%	10.7%	-0.5
2029PROJ	40	MLBKC	64	0	67	5.00	5.27	20.1%	11.4%	-0.7

Last 3 seasons WAR 0.1 · Next 3 projected WAR -1.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2024

xwOBA vs39

K%32

FB Velo25

CompsJuan Antonio Pena (age 37) · Jorge Castillo (age 37) · Hisanori Takahashi (age 37)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 101110vR 118

ERA4.40

FIP4.57

K%22.4%

BB%10.0%

K-BB%12.4%

Proj IP150

Value2027 · WAR

Runs saved vs league avg · 150 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2592.7

Whiff%

p5926.7%

p3219.5%

xwOBA vs

p390.324

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)
2011	22	MLB MLE	Tampa Bay Rays	3	1	9	40	15	3	1	33%	3.1	37.5%	7.5%	30.0%	2.5%	2.89	2.26	+0.3
2011	22	MLB RAW	Tampa Bay Rays	3	1	9	40	15	3	1	33%	3.1	37.5%	7.5%	30.0%	2.5%	2.89	2.24	+0.3
2012	23	MLB MLE	Tampa Bay Rays	31	31	177	759	175	76	18	100%	5.7	23.1%	10.0%	13.0%	2.4%	3.81	3.76	+3.5
2012	23	MLB RAW	Tampa Bay Rays	31	31	177	759	175	76	18	100%	5.7	23.1%	10.0%	13.0%	2.4%	3.81	3.73	+3.6
2013	24	MLB MLE	Tampa Bay Rays	27	27	150	642	143	75	14	100%	5.6	22.3%	11.7%	10.6%	2.2%	3.29	3.93	+2.6
2013	24	MLB RAW	Tampa Bay Rays	27	27	150	642	143	75	14	100%	5.6	22.3%	11.7%	10.6%	2.2%	3.29	3.90	+2.7
2014	25	MLB MLE	Tampa Bay Rays	2	2	10	44	6	5	1	100%	5.0	13.6%	11.4%	2.3%	2.3%	2.70	4.64	+0.1
2014	25	MLB RAW	Tampa Bay Rays	2	2	10	44	6	5	1	100%	5.0	13.6%	11.4%	2.3%	2.3%	2.70	4.70	+0.1
2015	26	MIX MLE	3 levels	22	22	114	490	113	38	16	100%	5.2	21.9%	7.8%	14.1%	3.5%	4.41	4.17	+1.7
2015	26	MIX RAW	3 levels	22	22	114	490	113	38	16	100%	5.2	23.1%	7.8%	15.3%	3.3%	4.41	3.94	+2.0
2016	27	MLB MLE	San Francisco Giants	33	33	198	838	178	71	25	100%	6.0	21.2%	8.5%	12.8%	3.2%	4.08	4.15	+2.9
2016	27	MLB RAW	San Francisco Giants	33	33	198	838	178	71	25	100%	6.0	21.2%	8.5%	12.8%	3.0%	4.08	4.02	+3.3
2017	28	MLB MLE	San Francisco Giants	32	31	174	790	148	64	27	97%	5.4	18.7%	8.1%	10.6%	3.7%	5.52	4.57	+1.7
2017	28	MLB RAW	San Francisco Giants	32	31	174	790	148	64	27	97%	5.4	18.7%	8.1%	10.6%	3.4%	5.52	4.52	+1.8
2018	29	MLB MLE	Texas Rangers	39	12	102	471	86	40	19	31%	2.6	18.3%	8.5%	9.8%	3.9%	6.79	4.76	-0.3
2018	29	MLB RAW	Texas Rangers	39	12	102	471	86	40	19	31%	2.6	18.3%	8.5%	9.8%	4.0%	6.79	5.01	-0.5
2019	30	MLB MLE	Detroit Tigers	2	2	10	33	9	1	0	100%	5.0	27.3%	3.0%	24.2%	0.0%	0.00	1.18	+0.6
2019	30	MLB RAW	Detroit Tigers	2	2	10	33	9	1	0	100%	5.0	27.3%	3.0%	24.2%	0.0%	0.00	1.60	+0.5
2021	32	MIX MLE	2 levels	29	18	92	421	85	45	20	62%	3.2	19.6%	10.8%	8.7%	4.7%	5.95	5.39	+0.0
2021	32	MIX RAW	2 levels	29	18	92	421	85	45	20	62%	3.2	20.2%	10.7%	9.5%	4.8%	5.95	5.54	-0.1
2022	33	MIX MLE	2 levels	65	0	79	321	90	37	3	0%	1.2	27.3%	11.8%	15.5%	0.9%	1.82	2.80	+1.7
2022	33	MIX RAW	2 levels	65	0	79	321	90	37	3	0%	1.2	28.0%	11.5%	16.5%	0.9%	1.82	2.72	+1.7
2023	34	MLB MLE	Miami Marlins	50	0	53	218	60	12	7	0%	1.1	27.5%	5.5%	22.0%	3.4%	2.56	3.33	+0.8
2023	34	MLB RAW	Miami Marlins	50	0	53	218	60	12	7	0%	1.1	27.5%	5.5%	22.0%	3.2%	2.56	3.23	+0.8
2024	35	MLB MLE	Los Angeles Angels	51	0	48	210	41	24	11	0%	0.9	19.5%	11.4%	8.1%	5.1%	5.03	5.72	-0.7
2024	35	MLB MLE	Los Angeles Angels	51	0	48	210	41	24	11	0%	0.9	19.5%	11.4%	8.1%	5.1%	5.03	5.72	-0.7
2024	35	MLB RAW	Los Angeles Angels	51	0	48	210	41	24	11	0%	0.9	19.5%	11.4%	8.1%	5.2%	5.03	5.85	-0.7
2026	37	MIX MLE	2 levels	18	2	21	92	25	10	2	11%	1.2	22.0%	11.0%	11.0%	2.7%	4.29	4.10	—
2026	37	MIX MLE	2 levels	18	2	21	92	25	10	2	11%	1.2	22.0%	11.0%	11.0%	2.7%	4.29	4.10	—
2026	37	MIX RAW	2 levels	18	2	21	92	25	10	2	11%	1.2	27.2%	10.9%	16.3%	2.2%	4.29	3.39	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			369	164	1143	4961	1053	463	150	44%	3.1	21.2%	9.3%	11.9%	3.0%	4.39	4.18	+5.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	38	MLB	KC	14	0	15	63	14	6	2	0%	1.1	22.4%	10.0%	12.4%	3.1%	4.40	4.57	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	39	MLB MLE	KC	90	0	94	399	85	43	14	0%	1.1	21.3%	10.7%	10.6%	3.6%	4.76	5.01	-0.5
2029	40	MLB MLE	KC	64	0	67	282	57	32	11	0%	1.0	20.1%	11.4%	8.7%	4.0%	5.17	5.44	-0.7
2028	39	MLB CTX	KC	90	0	94	397	84	42	13	0%	1.0	21.3%	10.7%	10.6%	3.3%	4.61	4.86	-0.5
2029	40	MLB CTX	KC	64	0	67	283	57	32	11	0%	1.0	20.1%	11.4%	8.7%	3.7%	5.00	5.27	-0.7
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		523	164	1304	5642	1195	538	174	31%	2.5	21.2%	9.5%	11.6%	3.1%	4.44	4.21	+5.2
/ 200 IP	—	RAW		80	25	200	865	183	83	27	31%	2.5	21.2%	9.5%	11.6%	3.1%	4.44	4.21	+0.8
Career	—	MLE		404	181	1239	5369	1158	503	170	45%	3.1	21.6%	9.4%	12.2%	3.2%	4.32	4.20	+3.8
/ 200 IP	—	MLE		65	29	200	866	187	81	27	45%	3.1	21.6%	9.4%	12.2%	3.2%	4.32	4.20	+0.6

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 AAAOmaha Storm Chasers · 18 IPFIP 3.82 → 4.10 (+0.28)K% 25.6% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.61%	—	×0.860	22.02%
BB% (unintentional) /BF	12.20%	—	×0.900	10.98%
HR / BF /BF	2.44%	÷1.000	×1.100	2.68%

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 3.82 → 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).

2021 AAALehigh Valley IronPigs · 19 IPFIP 5.72 → 6.53 (+0.81)K% 25.3% → 22.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.29%	—	×0.880	22.25%
BB% (unintentional) /BF	12.64%	—	×1.050	13.28%
HR / BF /BF	5.75%	÷0.957	×1.100	6.60%

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 91).

Result: FIP 5.72 → 6.53, 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).

2015 AAADurham Bulls · 40 IPFIP 3.05 → 3.57 (+0.52)K% 34.9% → 30.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.94%	—	×0.880	30.75%
BB% (unintentional) /BF	7.23%	—	×1.050	7.59%
HR / BF /BF	3.61%	÷1.039	×1.100	3.83%

Result: FIP 3.05 → 3.57, 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).

2015 A+Charlotte Stone Crabs · 11 IPFIP 3.74 → 4.91 (+1.17)K% 19.6% → 15.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.57%	—	×0.770	15.07%
BB% (unintentional) /BF	8.70%	—	×1.150	10.00%
HR / BF /BF	2.17%	÷0.822	×1.250	3.31%

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 64).

Result: FIP 3.74 → 4.91, 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
Matt Moore (OGILVIE)	—	—	—	—	—	3.1%	—	5.19	20.8%	9.8%	161	+0.62	—	—	—
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	54
Jorge Castillo L 2019 · 4.91 FIP	37	SP	4.91	19.6%	8.1%	4.5%	.316	—	—	—	—	—	4.50	408	46
Hisanori Takahashi L 2012 · 4.04 FIP	37	SU	4.04	24.5%	6.1%	4.1%	.295	—	—	—	—	—	3.50	240	34
Mauricio Lara L 2017 · 3.70 FIP	38	SP	3.70	12.0%	6.3%	1.5%	.301	4.96	12.3%	5.5%	111	+1.26	3.62	444	27
Jonathan Castellanos R 2019 · 5.24 FIP	37	SW	5.24	11.0%	9.0%	3.5%	.348	—	—	—	—	—	3.40	394	22
Jason Lane L 2015 · 5.56 FIP	38	SP	5.56	8.4%	6.2%	4.3%	.307	—	—	—	—	—	5.56	164	21
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	18
Keiichi Yabu R 2008 · 3.48 FIP	39	SU	3.48	15.9%	9.3%	1.0%	.269	—	—	—	—	—	3.48	126	18
Juan Sandoval R 2019 · 5.41 FIP	38	SU	5.41	15.9%	7.2%	5.0%	.273	—	—	—	—	—	3.24	225	16
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	15
Jose Oyervides R 2019 · 5.66 FIP	37	SP	5.66	16.3%	11.2%	4.6%	.320	—	—	—	—	—	3.01	407	15
Yasutomo Kubo R 2019 · 3.98 FIP	38	SP	3.98	20.4%	4.7%	3.6%	.327	—	—	—	—	—	3.98	152	13
Walter Silva R 2015 · 4.15 FIP	38	SP	4.15	14.8%	7.4%	2.5%	.311	3.91	10.4%	3.7%	209	-0.24	3.67	453	13
Hector Navarro R 2017 · 3.53 FIP	37	SU	3.53	11.5%	5.6%	1.3%	.283	—	—	—	—	—	2.30	126	11
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	11
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	10
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	7
Pablo Ortega R 2015 · 4.52 FIP	38	SP	4.52	8.6%	7.3%	2.2%	.291	4.24	11.5%	6.0%	236	-0.28	4.01	375	6
Billy Taylor R 2000 · 4.53 FIP	38	SU	4.53	21.0%	11.3%	3.2%	.275	—	—	—	—	—	2.94	323	5
Erubiel Gonzalez R 2017 · 3.51 FIP	38	SU	3.51	13.3%	2.5%	2.2%	.388	—	—	—	—	—	2.99	119	5

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	7.3	112	21.3%	9.9%	1.21	4.45
Bud Norris ('18)	6.1	122	21.7%	9.1%	1.15	4.45
Dan Spillner ('74-'85, 5 WAR*)	7.5	124	20.6%*	9.1%*	1.11*	4.64*
Diego Segui ('62-'77, 10 WAR*)	9.0	121	22.2%*	9.9%*	1.19*	4.40*
Mike Bielecki ('84-'97, 9 WAR*)	6.2	95	18.4%*	9.1%*	1.15*	4.47*
Jorge De La Rosa ('18)	7.6	109	19.1%	10.3%	1.05	4.58
Héctor Santiago ('21)	4.7	105	20.7%	10.6%	1.38	4.12