JT Chargois

P Round Rock Express

Age 36 RHP 6'3" / 200 lbs AAA peak 0.1 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	32	MIX3 levels	53	5	51	3.18	3.18	22.4%	9.3%	0.8	K 20.1% · BB 9.8% · FIP 3.64
2024	33	MIX2 levels	47	2	49	1.84	3.88	22.9%	8.3%	0.4	K 20.5% · BB 8.2% · FIP 4.72
2025	34	AAATEX	5	0	5	19.29	15.96	24.1%	10.3%	—	K 24.1% · BB 10.3% · FIP 11.84
2026	35	No MLB games played
2027PROJ	36	MLBTEX	5	0	5	4.32	4.42	22.8%	9.3%	0.0
2028PROJMLE	37	MLBTEX	—	—	0	4.62	4.82	21.7%	10.0%	—

Last 3 seasons WAR 1.2 · Next 2 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2024

FB Velo50

xwOBA vs42

K%38

CompsHirokazu Sawamura (age 34) · Keiichi Yabu (age 36) · Juan Sandoval (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117109vR 101

ERA4.32

FIP4.42

K%22.8%

BB%9.3%

K-BB%13.4%

Proj IP46

Value2027 · WAR

Runs saved vs league avg · 46 IP

Grades · Statcast · Contract

Scouting Grades

STUF4545

CTRL4545

CMD4545

K†5555

Statcast Percentiles

FB Velo

p5094.5

Whiff%

p3923.7%

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)
2015	24	MIX MLE	2 levels	48	0	48	207	53	25	1	0%	1.0	20.4%	13.4%	7.0%	0.5%	2.62	3.34	—
2015	24	MIX RAW	2 levels	48	0	48	207	53	25	1	0%	1.0	25.6%	12.1%	13.5%	0.5%	2.62	2.73	—
2016	25	MIX MLE	3 levels	64	0	70	289	72	25	2	0%	1.1	22.0%	8.5%	13.6%	0.5%	2.45	2.58	+1.7
2016	25	MIX RAW	3 levels	64	0	70	289	72	25	2	0%	1.1	24.9%	8.7%	16.3%	0.7%	2.45	2.48	+1.7
2017	26	AAA MLE	Rochester Red Wings	2	0	3	9	2	1	0	0%	1.3	22.2%	11.1%	11.1%	0.0%	0.00	2.63	—
2017	26	AAA RAW	Rochester Red Wings	2	0	3	9	2	1	0	0%	1.3	22.2%	11.1%	11.1%	0.0%	0.00	2.73	—
2018	27	MIX MLE	2 levels	50	0	47	196	49	19	4	0%	0.9	24.4%	9.9%	14.6%	2.0%	2.85	3.36	+0.7
2018	27	MIX RAW	2 levels	50	0	47	196	49	19	4	0%	0.9	25.0%	9.7%	15.3%	2.0%	2.85	3.33	+0.7
2019	28	MIX MLE	2 levels	48	0	54	223	65	19	7	0%	1.1	27.2%	8.9%	18.3%	3.1%	4.17	3.63	+0.6
2019	28	MIX RAW	2 levels	48	0	54	223	65	19	7	0%	1.1	29.1%	8.5%	20.6%	3.1%	4.17	3.43	+0.7
2021	30	MLB MLE	Tampa Bay Rays	56	0	54	216	53	17	5	0%	1.0	24.5%	7.9%	16.7%	2.4%	2.52	3.33	+0.8
2021	30	MLB RAW	Tampa Bay Rays	56	0	54	216	53	17	5	0%	1.0	24.5%	7.9%	16.7%	2.3%	2.52	3.29	+0.8
2022	31	MIX MLE	3 levels	33	3	30	129	22	12	4	9%	0.9	19.8%	5.8%	14.0%	3.6%	6.00	4.14	+0.1
2022	31	MIX RAW	3 levels	33	3	30	129	22	12	4	9%	0.9	17.1%	9.3%	7.8%	3.1%	6.00	4.57	-0.0
2023	32	MIX MLE	3 levels	53	5	51	205	46	19	3	9%	1.0	20.1%	9.8%	10.3%	1.8%	3.18	3.64	+0.6
2023	32	MIX RAW	3 levels	53	5	51	205	46	19	3	9%	1.0	22.4%	9.3%	13.2%	1.5%	3.18	3.18	+0.8
2024	33	MIX MLE	2 levels	47	2	49	192	44	16	6	4%	1.0	20.5%	8.2%	12.3%	4.2%	1.84	4.72	-0.1
2024	33	MIX MLE	2 levels	47	2	49	192	44	16	6	4%	1.0	20.5%	8.2%	12.3%	4.2%	1.84	4.72	-0.1
2024	33	MIX RAW	2 levels	47	2	49	192	44	16	6	4%	1.0	22.9%	8.3%	14.6%	3.1%	1.84	3.88	+0.4
2025	34	AAA MLE	Round Rock Express	5	0	5	29	7	3	5	0%	0.9	24.1%	10.3%	13.8%	17.2%	19.29	11.84	—
2025	34	AAA MLE	Round Rock Express	5	0	5	29	7	3	5	0%	0.9	24.1%	10.3%	13.8%	17.2%	19.29	11.84	—
2025	34	AAA RAW	Round Rock Express	5	0	5	29	7	3	5	0%	0.9	24.1%	10.3%	13.8%	17.2%	19.29	15.96	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			244	8	231	945	220	78	25	3%	0.9	23.3%	8.3%	15.0%	2.6%	3.35	3.61	+2.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	TEX	5	0	5	21	5	2	1	0%	1.0	22.8%	9.3%	13.4%	3.1%	4.32	4.42	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	TEX	30	0	32	133	29	13	5	0%	1.1	21.7%	10.0%	11.7%	3.5%	4.62	4.82	-0.1
2028	37	MLB CTX	TEX	—	—	0	—	—	—	—	—	—	21.7%	10.0%	11.7%	3.5%	4.62	4.82	—
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		244	8	232	946	220	78	25	3%	0.9	23.3%	8.3%	15.0%	2.6%	3.35	3.63	+2.5
/ 200 IP	—	RAW		211	7	200	817	190	67	22	3%	0.9	23.3%	8.3%	15.0%	2.6%	3.35	3.63	+2.2
Career	—	MLE		406	10	410	1695	382	156	40	2%	1.0	22.6%	9.2%	13.4%	2.3%	3.20	3.64	+2.5
/ 200 IP	—	MLE		198	5	200	827	187	76	19	2%	1.0	22.6%	9.2%	13.4%	2.3%	3.20	3.64	+1.9

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.

2025 AAARound Rock Express · 5 IPFIP 11.84 → 13.92 (+2.09)K% 24.1% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.14%	—	×0.860	20.76%
BB% (unintentional) /BF	10.34%	—	×0.900	9.31%
HR / BF /BF	17.24%	÷0.913	×1.100	20.76%

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 83).

Result: FIP 11.84 → 13.92, 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).

2024 AAAJacksonville Jumbo Shrimp · 13 IPFIP 1.63 → 1.88 (+0.25)K% 30.4% → 26.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.43%	—	×0.860	26.17%
BB% (unintentional) /BF	8.70%	—	×0.900	7.83%
HR / BF /BF	0.00%	÷0.925	×1.100	0.00%

Result: FIP 1.63 → 1.88, 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 AAADurham Bulls · 6 IPFIP 6.24 → 6.58 (+0.34)K% 11.4% → 10.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	11.43%	—	×0.880	10.06%
BB% (unintentional) /BF	20.00%	—	×1.050	21.00%
HR / BF /BF	2.86%	÷1.034	×1.100	3.04%

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 107).

Result: FIP 6.24 → 6.58, 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 AAAOklahoma City Dodgers · 33 IPFIP 3.51 → 3.89 (+0.38)K% 27.4% → 24.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.41%	—	×0.880	24.12%
BB% (unintentional) /BF	11.85%	—	×1.050	12.44%
HR / BF /BF	2.22%	÷1.073	×1.100	2.28%

Result: FIP 3.51 → 3.89, 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 AAAOklahoma City Dodgers · 15 IPFIP 3.31 → 3.53 (+0.22)K% 14.8% → 13.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.75%	—	×0.880	12.98%
BB% (unintentional) /BF	11.48%	—	×1.050	12.05%
HR / BF /BF	0.00%	÷1.020	×1.100	0.00%

Result: FIP 3.31 → 3.53, 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).

2016 AAARochester Red Wings · 35 IPFIP 1.75 → 2.14 (+0.39)K% 29.1% → 25.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.08%	—	×0.880	25.59%
BB% (unintentional) /BF	5.67%	—	×1.050	5.96%
HR / BF /BF	0.71%	÷0.951	×1.100	0.82%

Result: FIP 1.75 → 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 AAChattanooga Lookouts · 12 IPFIP 3.10 → 3.61 (+0.51)K% 29.2% → 23.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.17%	—	×0.810	23.63%
BB% (unintentional) /BF	10.42%	—	×1.100	11.46%
HR / BF /BF	2.08%	÷1.301	×1.200	1.92%

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 160).

Result: FIP 3.10 → 3.61, 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 AAChattanooga Lookouts · 33 IPFIP 3.25 → 3.81 (+0.56)K% 24.1% → 19.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.11%	—	×0.810	19.53%
BB% (unintentional) /BF	14.18%	—	×1.100	15.60%
HR / BF /BF	0.71%	÷1.232	×1.200	0.69%

Result: FIP 3.25 → 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
JT Chargois (OGILVIE)	—	—	4.42	22.8%	9.3%	3.1%	—	4.82	21.7%	11.7%	32	+0.40	—	—	—
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	79
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	74
Juan Sandoval R 2017 · 3.42 FIP	36	SU	3.42	16.7%	7.2%	1.5%	.298	5.41	15.9%	8.7%	57	+1.99	3.24	225	73
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	68
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	67
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	66
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	66
Kazuhiro Sasaki R 2003 · 3.30 FIP	35	SU	3.30	19.3%	8.7%	1.3%	.274	—	—	—	—	—	2.76	223	65
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	63
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	62
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	62
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	62
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	61
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	61
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	59
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	58
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	55
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	54
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	54
Jay Jackson R 2024 · 5.31 FIP	36	SU	5.31	25.4%	7.9%	6.1%	.304	—	—	—	—	—	2.36	261	52

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	34	23.0%	9.4%	1.08	3.62
Neftalí Feliz ('21)	2.0	49	22.9%	9.7%	1.05	3.55
Tony Fossas ('88-'99, 2 WAR*)	2.1	42	21.9%*	9.3%*	0.99*	3.88*
Ryan Tepera ('23)	1.8	52	24.3%	8.8%	1.13	3.62
Jerry Blevins ('19)	2.5	45	24.2%	9.3%	0.91	3.54
Ray Searage ('81-'90, 4 WAR*)	1.4	41	21.0%*	10.8%*	0.84*	3.70*
Alberto Reyes ('95-'08, 5 WAR*)	2.1	54	27.1%*	10.2%*	1.18*	3.66*