Jaime Barria

P Columbus Clippers

Age 31 RHP 6'1" / 210 lbs AAA peak 0.1 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	26	MLBLAA	35	1	79	2.61	4.26	17.1%	6.0%	0.3
2023	27	MLBLAA	34	6	82	5.68	5.63	17.1%	6.6%	-1.0
2024	28	AAACLE	13	1	24	4.81	2.69	28.1%	6.2%	—	K 24.2% · BB 5.6% · FIP 2.96
2025	29	No MLB games played
2026	30	No MLB games played
2027PROJ	31	MLBCLE	4	4	23	4.31	4.36	22.7%	8.9%	0.3
2028PROJ	32	MLBCLE	21	21	126	4.49	4.62	21.9%	9.3%	1.1
2029PROJ	33	MLBCLE	17	17	102	4.73	4.90	21.0%	9.8%	0.6

Last 3 seasons WAR -0.7 · Next 3 projected WAR 2.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2023

FB Velo29

xwOBA vs16

K%15

CompsHisashi Iwakuma (age 31) · Kenshin Kawakami (age 34) · Jorge Campillo (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 122114vR 106

ERA4.31

FIP4.36

K%22.7%

BB%8.9%

K-BB%13.7%

Proj IP165

Value2027 · WAR

Runs saved vs league avg · 165 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2993.0

Whiff%

p3322.9%

p1517.1%

xwOBA vs

p160.352

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)
2015	19	ROK MLE	Orem Owlz	15	14	69	298	61	10	4	93%	4.6	13.9%	4.2%	9.7%	1.9%	4.02	3.49	—
2015	19	ROK RAW	Orem Owlz	15	14	69	298	61	10	4	93%	4.6	20.5%	3.4%	17.1%	1.3%	4.02	2.52	—
2016	20	A MLE	Burlington Bees	25	25	117	501	78	21	6	100%	4.7	11.5%	4.9%	6.6%	1.2%	3.85	3.42	—
2016	20	A RAW	Burlington Bees	25	25	117	501	78	21	6	100%	4.7	15.6%	4.2%	11.4%	1.2%	3.85	2.97	—
2017	21	MIX MLE	3 levels	26	26	142	576	117	31	14	100%	5.4	16.2%	6.0%	10.2%	3.2%	2.80	4.22	—
2017	21	MIX RAW	3 levels	26	26	142	576	117	31	14	100%	5.4	20.3%	5.4%	14.9%	2.4%	2.80	3.39	—
2018	22	MIX MLE	2 levels	31	31	147	615	117	52	19	100%	4.8	18.7%	8.5%	10.2%	3.1%	3.42	4.29	+1.9
2018	22	MIX RAW	2 levels	31	31	147	615	117	52	19	100%	4.8	19.0%	8.5%	10.6%	3.1%	3.42	4.25	+2.0
2019	23	MIX MLE	2 levels	29	23	131	592	119	37	40	79%	4.5	19.2%	6.3%	12.9%	6.8%	7.63	6.00	-0.9
2019	23	MIX RAW	2 levels	29	23	131	592	119	37	40	79%	4.5	20.1%	6.2%	13.9%	6.8%	7.63	6.10	-1.0
2020	24	MLB MLE	Los Angeles Angels	7	5	32	132	27	8	3	71%	4.6	20.5%	6.1%	14.4%	2.2%	3.62	3.36	+0.8
2020	24	MLB RAW	Los Angeles Angels	7	5	32	132	27	8	3	71%	4.6	20.5%	6.1%	14.4%	2.3%	3.62	3.38	+0.8
2021	25	MIX MLE	2 levels	23	21	106	460	69	27	18	91%	4.6	14.1%	6.0%	8.2%	4.3%	4.51	5.05	+0.4
2021	25	MIX RAW	2 levels	23	21	106	460	69	27	18	91%	4.6	15.0%	5.9%	9.1%	3.9%	4.51	4.78	+0.8
2022	26	MLB MLE	Los Angeles Angels	35	1	79	316	54	19	11	3%	2.3	17.1%	6.0%	11.1%	3.4%	2.61	4.29	+0.2
2022	26	MLB RAW	Los Angeles Angels	35	1	79	316	54	19	11	3%	2.3	17.1%	6.0%	11.1%	3.5%	2.61	4.26	+0.3
2023	27	MLB MLE	Los Angeles Angels	34	6	82	362	62	24	20	18%	2.4	17.1%	6.6%	10.5%	5.4%	5.68	5.47	-0.9
2023	27	MLB RAW	Los Angeles Angels	34	6	82	362	62	24	20	18%	2.4	17.1%	6.6%	10.5%	5.5%	5.68	5.63	-1.0
2024	28	AAA MLE	Columbus Clippers	13	1	24	96	27	6	2	8%	1.9	24.2%	5.6%	18.6%	2.2%	4.81	2.96	—
2024	28	AAA MLE	Columbus Clippers	13	1	24	96	27	6	2	8%	1.9	24.2%	5.6%	18.6%	2.2%	4.81	2.96	—
2024	28	AAA RAW	Columbus Clippers	13	1	24	96	27	6	2	8%	1.9	28.1%	6.2%	21.9%	2.1%	4.81	2.69	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			134	62	463	1962	351	144	83	46%	3.5	17.9%	7.3%	10.6%	4.2%	4.38	4.85	-1.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	CLE	4	4	23	97	22	9	3	100%	5.8	22.7%	8.9%	13.7%	3.0%	4.31	4.36	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	CLE	21	21	126	534	117	50	18	100%	6.0	21.9%	9.3%	12.5%	3.3%	4.49	4.62	+1.1
2029	33	MLB MLE	CLE	17	17	102	432	91	42	15	100%	6.0	21.0%	9.8%	11.2%	3.6%	4.73	4.90	+0.6
2028	32	MLB CTX	CLE	21	21	126	532	116	50	17	100%	6.0	21.9%	9.3%	12.5%	3.3%	4.49	4.62	+1.1
2029	33	MLB CTX	CLE	17	17	102	431	91	42	15	100%	6.0	21.0%	9.8%	11.2%	3.6%	4.73	4.90	+0.6
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		172	100	692	2928	559	236	116	58%	4.0	19.1%	8.1%	11.0%	4.0%	4.45	4.68	+5.8
/ 200 IP	—	RAW		50	29	200	847	162	68	34	58%	4.0	19.1%	8.1%	11.0%	4.0%	4.45	4.68	+1.7
Career	—	MLE		238	153	930	3948	652	245	144	64%	3.9	16.5%	6.2%	10.3%	3.7%	4.31	4.50	+4.7
/ 200 IP	—	MLE		51	33	200	849	140	53	31	64%	3.9	16.5%	6.2%	10.3%	3.7%	4.31	4.50	+1.4

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.

2024 AAAColumbus Clippers · 24 IPFIP 2.66 → 2.96 (+0.30)K% 28.1% → 24.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.12%	—	×0.860	24.19%
BB% (unintentional) /BF	6.25%	—	×0.900	5.62%
HR / BF /BF	2.08%	÷1.060	×1.100	2.16%

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 112).

Result: FIP 2.66 → 2.96, 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 AAASalt Lake Bees · 49 IPFIP 4.83 → 5.57 (+0.75)K% 16.2% → 14.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.19%	—	×0.880	14.25%
BB% (unintentional) /BF	3.81%	—	×1.050	4.00%
HR / BF /BF	4.76%	÷0.907	×1.100	5.78%

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 81).

Result: FIP 4.83 → 5.57, 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).

2019 AAASalt Lake Bees · 48 IPFIP 5.89 → 6.27 (+0.38)K% 19.4% → 17.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.38%	—	×0.880	17.06%
BB% (unintentional) /BF	4.41%	—	×1.050	4.63%
HR / BF /BF	7.05%	÷1.059	×1.100	7.32%

Result: FIP 5.89 → 6.27, 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).

2018 AAASalt Lake Bees · 18 IPFIP 3.26 → 3.76 (+0.50)K% 24.4% → 21.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.36%	—	×0.880	21.44%
BB% (unintentional) /BF	6.41%	—	×1.050	6.73%
HR / BF /BF	2.56%	÷0.958	×1.100	2.95%

Result: FIP 3.26 → 3.76, 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 AAMobile BayBears · 62 IPFIP 3.99 → 4.93 (+0.94)K% 18.1% → 14.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.08%	—	×0.810	14.64%
BB% (unintentional) /BF	5.77%	—	×1.100	6.35%
HR / BF /BF	3.08%	÷0.896	×1.200	4.12%

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 79).

Result: FIP 3.99 → 4.93, 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 A+Inland Empire 66ers · 65 IPFIP 3.15 → 3.98 (+0.83)K% 22.2% → 17.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.18%	—	×0.770	17.08%
BB% (unintentional) /BF	5.06%	—	×1.150	5.82%
HR / BF /BF	2.33%	÷1.008	×1.250	2.89%

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 102).

Result: FIP 3.15 → 3.98, 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 AAASalt Lake Bees · 15 IPFIP 1.88 → 2.14 (+0.26)K% 22.0% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.03%	—	×0.880	19.39%
BB% (unintentional) /BF	5.08%	—	×1.050	5.34%
HR / BF /BF	0.00%	÷0.989	×1.100	0.00%

Result: FIP 1.88 → 2.14, 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 ABurlington Bees · 117 IPFIP 2.97 → 3.42 (+0.44)K% 15.6% → 11.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.57%	—	×0.740	11.52%
BB% (unintentional) /BF	4.19%	—	×1.180	4.95%
HR / BF /BF	1.20%	÷1.285	×1.300	1.21%

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 157).

Result: FIP 2.97 → 3.42, 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).

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
Jaime Barria (OGILVIE)	—	—	—	—	—	3.0%	—	4.75	21.5%	11.9%	229	+0.39	—	—	—
Hisashi Iwakuma R 2012 · 4.28 FIP	31	SP-	4.28	19.5%	7.7%	3.4%	.277	3.45	21.5%	17.8%	528	-0.83	3.19	884	75
Kenshin Kawakami R 2009 · 3.97 FIP	34	SP-	3.97	15.7%	7.6%	2.2%	.277	4.06	15.1%	9.5%	87	+0.09	3.97	244	71
Jorge Campillo R 2008 · 3.93 FIP	29	SP-	3.93	16.3%	5.5%	2.8%	.283	—	—	—	—	—	3.93	172	68
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	62
Yohan Pino R 2015 · 4.13 FIP	31	SP-	4.13	17.8%	5.8%	3.3%	.312	5.33	15.2%	11.6%	35	+1.20	3.84	193	61
Josh A. Smith R 2019 · 4.65 FIP	31	SW	4.65	20.8%	6.4%	4.5%	.341	3.67	17.4%	8.6%	59	-0.98	2.91	598	60
Elian Leyva R 2019 · 4.30 FIP	30	SP-	4.30	17.0%	8.5%	2.9%	.323	—	—	—	—	—	3.10	158	60
Scott Richmond R 2009 · 5.11 FIP	29	SP	5.11	19.2%	9.5%	4.4%	.294	—	—	—	—	—	2.80	166	60
Mike Wright Jr. R 2021 · 3.89 FIP	31	SP-	3.89	19.1%	8.8%	2.4%	.237	4.79	14.4%	1.1%	56	+0.90	3.76	725	60
Casey Lawrence R 2018 · 3.77 FIP	30	SP-	3.77	18.1%	4.4%	3.0%	.305	4.08	20.0%	14.8%	86	+0.31	3.57	1,374	60
Kohei Arihara R 2022 · 4.58 FIP	29	SP-	4.58	16.4%	7.2%	3.5%	.333	—	—	—	—	—	4.58	135	59
Nate Long R 2015 · 4.67 FIP	29	SP	4.67	14.8%	10.6%	2.7%	.285	—	—	—	—	—	4.67	151	59
Scott McGregor R 2015 · 4.37 FIP	28	SP-	4.37	11.8%	7.6%	2.4%	.293	—	—	—	—	—	4.37	107	59
Pete Walker R 2002 · 4.59 FIP	33	SP-	4.59	13.4%	7.7%	3.0%	.279	5.16	12.0%	2.8%	139	+0.57	4.45	327	58
Daniel McCutchen R 2015 · 4.30 FIP	32	SP-	4.30	13.9%	4.4%	3.3%	.268	4.74	10.8%	4.9%	155	+0.44	4.13	476	58
Shawn Haviland R 2017 · 5.00 FIP	31	SP-	5.00	16.3%	8.1%	4.1%	.261	—	—	—	—	—	3.69	283	58
Luke Hudson R 2006 · 3.81 FIP	29	SP-	3.81	14.5%	8.4%	1.6%	.307	—	—	—	—	—	3.81	235	58
Jose Samayoa R 2019 · 4.05 FIP	29	SP	4.05	19.4%	7.0%	3.1%	.314	—	—	—	—	—	3.44	262	58
Mitch Lively R 2017 · 3.47 FIP	31	SP-	3.47	17.0%	6.4%	1.8%	.304	—	—	—	—	—	2.39	158	57
Cory Riordan R 2016 · 4.24 FIP	30	SP	4.24	14.3%	5.0%	3.1%	.341	—	—	—	—	—	4.24	154	57

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	4.3	95	19.8%	8.4%	1.46	4.42
Paul Moskau ('77-'83, 3 WAR*)	3.2	91	19.8%*	8.9%*	1.50*	4.84*
Rubby De La Rosa ('17)	2.1	105	19.6%	8.6%	1.32	4.49
Chase Anderson ('24)	5.5	101	19.7%	8.1%	1.48	4.41
Drew Hutchison ('22)	2.9	84	19.2%	8.2%	1.29	4.89
Bob Stoddard ('81-'87, 3 WAR*)	2.2	72	17.9%*	8.7%*	1.46*	4.11*
Rich Nye ('66-'70, 3 WAR*)	2.4	119	16.7%*	7.5%*	1.52*	4.40*