Trent Thornton

P Chicago Cubs

Age 33 RHP 6'0" / 190 lbs MLB

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	30	MLBSEA	71	0	72	3.61	3.16	26.2%	6.1%	1.2
2025	31	MIX2 levels	35	0	45	4.60	4.37	18.4%	6.3%	0.1	K 17.8% · BB 6.7% · FIP 4.32
2026	32	MIX2 levels	52	1	54	3.33	5.17	16.3%	8.1%	-0.4	K 15.8% · BB 7.7% · FIP 5.06
2027PROJ	33	MLBCHC	38	0	40	4.46	4.72	18.7%	7.3%	-0.1
2028PROJ	34	MLBCHC	22	0	23	4.80	5.02	17.8%	7.8%	-0.1

Last 3 seasons WAR 0.9 · Next 2 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo58

xwOBA vs27

K%10

Form4.02 ERA (proj 4.46) · K 23.2% (proj 18.7%) · BB 8.7% (proj 7.3%) ▲ last 90d · 16 IP through Oct 27

CompsChris Jakubauskas (age 32) · Jean Machi (age 33) · Felipe Arredondo (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 123115vR 107

ERA4.46

FIP4.72

K%18.7%

BB%7.3%

K-BB%11.5%

Proj IP40

Value2027 · WAR

Runs saved vs league avg · 40 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p5895.1

Whiff%

p1720.6%

p1015.8%

xwOBA vs

p270.339

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)
2016	22	MIX MLE	2 levels	24	21	136	546	124	21	19	88%	5.7	17.7%	4.4%	13.4%	3.5%	3.52	4.07	—
2016	22	MIX RAW	2 levels	24	21	136	546	124	21	19	88%	5.7	22.7%	3.8%	18.9%	3.5%	3.52	3.56	—
2017	23	MIX MLE	2 levels	25	23	131	574	101	22	14	92%	5.3	15.3%	4.0%	11.3%	2.8%	5.21	3.85	—
2017	23	MIX RAW	2 levels	25	23	131	574	101	22	14	92%	5.3	17.6%	3.8%	13.8%	2.4%	5.21	3.45	—
2018	24	AAA MLE	Fresno Grizzlies	24	22	124	516	122	29	13	92%	5.2	20.8%	5.9%	14.9%	2.9%	4.42	3.69	—
2018	24	AAA RAW	Fresno Grizzlies	24	22	124	516	122	29	13	92%	5.2	23.6%	5.6%	18.0%	2.5%	4.42	3.20	—
2019	25	MLB MLE	Toronto Blue Jays	32	29	154	677	149	61	24	91%	4.8	22.0%	9.0%	13.0%	3.5%	4.84	4.33	+1.9
2019	25	MLB RAW	Toronto Blue Jays	32	29	154	677	149	61	24	91%	4.8	22.0%	9.0%	13.0%	3.5%	4.84	4.38	+1.8
2020	26	MLB MLE	Toronto Blue Jays	3	3	6	33	6	3	0	100%	1.9	18.2%	9.1%	9.1%	0.0%	11.12	2.72	+0.2
2020	26	MLB RAW	Toronto Blue Jays	3	3	6	33	6	3	0	100%	1.9	18.2%	9.1%	9.1%	0.0%	11.12	2.57	+0.2
2021	27	MIX MLE	2 levels	47	3	59	256	60	17	12	6%	1.3	24.1%	6.9%	17.1%	5.6%	3.94	5.00	-0.3
2021	27	MIX RAW	2 levels	47	3	59	256	60	17	12	6%	1.3	23.4%	6.6%	16.8%	4.7%	3.94	4.57	-0.0
2022	28	MIX MLE	2 levels	53	0	74	311	67	29	8	0%	1.4	20.4%	9.5%	10.9%	2.6%	3.65	4.02	+0.5
2022	28	MIX RAW	2 levels	53	0	74	311	67	29	8	0%	1.4	21.5%	9.3%	12.2%	2.6%	3.65	3.87	+0.6
2023	29	MIX MLE	2 levels	50	1	60	260	54	24	9	2%	1.2	19.3%	8.5%	10.7%	3.4%	2.98	4.44	+0.1
2023	29	MIX RAW	2 levels	50	1	60	260	54	24	9	2%	1.2	20.8%	9.2%	11.5%	3.5%	2.98	4.44	+0.1
2024	30	MLB MLE	Seattle Mariners	71	0	72	294	77	18	8	0%	1.0	26.2%	6.1%	20.1%	2.8%	3.61	3.20	+1.2
2024	30	MLB MLE	Seattle Mariners	71	0	72	294	77	18	8	0%	1.0	26.2%	6.1%	20.1%	2.8%	3.61	3.20	+1.2
2024	30	MLB RAW	Seattle Mariners	71	0	72	294	77	18	8	0%	1.0	26.2%	6.1%	20.1%	2.7%	3.61	3.16	+1.2
2025	31	MIX MLE	2 levels	35	0	45	190	35	12	7	0%	1.3	17.8%	6.7%	11.1%	3.4%	4.60	4.32	+0.1
2025	31	MIX MLE	2 levels	35	0	45	190	35	12	7	0%	1.3	17.8%	6.7%	11.1%	3.4%	4.60	4.32	+0.1
2025	31	MIX RAW	2 levels	35	0	45	190	35	12	7	0%	1.3	18.4%	6.3%	12.1%	3.7%	4.60	4.37	+0.1
2026	32	MIX MLE	2 levels	52	1	54	221	36	18	10	2%	1.0	15.8%	7.7%	8.2%	4.2%	3.33	5.06	-0.3
2026	32	MIX MLE	2 levels	52	1	54	221	36	18	10	2%	1.0	15.8%	7.7%	8.2%	4.2%	3.33	5.06	-0.3
2026	32	MIX RAW	2 levels	52	1	54	221	36	18	10	2%	1.0	16.3%	8.1%	8.1%	4.5%	3.33	5.17	-0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			283	36	449	1910	410	147	70	13%	1.6	21.5%	7.7%	13.8%	3.7%	4.27	4.28	+1.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	CHC	38	0	40	169	32	12	6	0%	1.1	18.7%	7.3%	11.5%	3.5%	4.46	4.72	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	CHC	22	0	23	98	17	8	4	0%	1.1	17.8%	7.8%	10.1%	3.8%	4.80	5.02	-0.1
2028	34	MLB CTX	CHC	22	0	23	97	17	8	4	0%	1.0	17.8%	7.8%	10.1%	3.8%	4.80	5.02	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		305	36	473	2008	427	155	74	12%	1.5	21.3%	7.7%	13.6%	3.7%	4.29	4.29	+1.4
/ 200 IP	—	RAW		129	15	200	850	181	66	31	12%	1.5	21.3%	7.7%	13.6%	3.7%	4.29	4.29	+0.6
Career	—	MLE		416	103	916	3878	768	259	129	25%	2.2	19.8%	6.7%	13.1%	3.3%	4.20	4.10	+1.3
/ 200 IP	—	MLE		91	22	200	846	168	56	28	25%	2.2	19.8%	6.7%	13.1%	3.3%	4.20	4.10	+0.5

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 AAAIowa Cubs · 6 IPFIP 7.32 → 7.85 (+0.52)K% 20.0% → 17.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.860	17.20%
BB% (unintentional) /BF	12.00%	—	×0.900	10.80%
HR / BF /BF	8.00%	÷1.000	×1.100	8.80%

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 7.32 → 7.85, 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 AAATacoma Rainiers · 29 IPFIP 4.66 → 4.66 (−0.00)K% 20.7% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.74%	—	×0.860	17.84%
BB% (unintentional) /BF	13.33%	—	×0.900	12.00%
HR / BF /BF	2.96%	÷1.157	×1.100	2.82%

Result: FIP 4.66 → 4.66, 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 · 28 IPFIP 2.72 → 3.10 (+0.38)K% 24.6% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.59%	—	×0.880	21.64%
BB% (unintentional) /BF	9.84%	—	×1.050	10.33%
HR / BF /BF	0.82%	÷0.951	×1.100	0.95%

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 2.72 → 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).

2021 AAABuffalo Bisons · 10 IPFIP 2.04 → 2.28 (+0.23)K% 20.0% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.880	17.60%
BB% (unintentional) /BF	5.00%	—	×1.050	5.25%
HR / BF /BF	0.00%	÷1.081	×1.100	0.00%

Result: FIP 2.04 → 2.28, 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 AAAFresno Grizzlies · 124 IPFIP 3.20 → 3.69 (+0.49)K% 23.6% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.64%	—	×0.880	20.81%
BB% (unintentional) /BF	5.62%	—	×1.050	5.90%
HR / BF /BF	2.52%	÷0.951	×1.100	2.91%

Result: FIP 3.20 → 3.69, 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 AAAFresno Grizzlies · 115 IPFIP 3.49 → 3.88 (+0.39)K% 17.7% → 15.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.67%	—	×0.880	15.55%
BB% (unintentional) /BF	4.42%	—	×1.050	4.64%
HR / BF /BF	2.41%	÷0.963	×1.100	2.75%

Result: FIP 3.49 → 3.88, 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).

2017 AACorpus Christi Hooks · 16 IPFIP 3.10 → 3.61 (+0.51)K% 17.1% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.11%	—	×0.810	13.86%
BB% (unintentional) /BF	0.00%	—	×1.100	0.00%
HR / BF /BF	2.63%	÷1.035	×1.200	3.05%

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 107).

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).

2016 A+Lancaster JetHawks · 90 IPFIP 3.70 → 4.25 (+0.55)K% 24.3% → 18.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.25%	—	×0.770	18.67%
BB% (unintentional) /BF	4.36%	—	×1.150	5.01%
HR / BF /BF	3.81%	÷1.252	×1.250	3.81%

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 150).

Result: FIP 3.70 → 4.25, 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
Trent Thornton (OGILVIE)	—	—	—	—	—	3.5%	—	5.02	17.8%	10.1%	23	+0.30	—	—	—
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	69
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	65
Felipe Arredondo R 2019 · 4.65 FIP	32	SU	4.65	16.2%	7.1%	3.7%	.272	—	—	—	—	—	3.92	337	65
Mike Buddie R 2002 · 4.26 FIP	31	SU	4.26	15.1%	7.6%	2.7%	.297	—	—	—	—	—	3.76	123	65
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	64
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	64
Ivan Maldonado R 2015 · 4.60 FIP	35	SW	4.60	12.8%	8.7%	2.7%	.269	5.41	6.4%	-3.3%	104	+0.81	4.60	172	64
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	64
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	64
Rudy Gonzalez R 2015 · 4.52 FIP	34	SW	4.52	13.5%	9.6%	2.4%	.347	3.84	14.0%	6.5%	79	-0.68	3.31	119	63
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	63
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	62
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	62
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	62
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	62
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	62
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	61
Tyler Sturdevant R 2017 · 4.48 FIP	31	SU	4.48	13.5%	6.7%	3.0%	.300	—	—	—	—	—	3.44	149	61
Alan Busenitz R 2024 · 3.57 FIP	33	SU	3.57	18.6%	6.8%	2.1%	.304	7.93	16.1%	9.7%	25	+4.36	2.92	436	61
Angel Sánchez R 2023 · 4.90 FIP	33	SW	4.90	19.0%	10.5%	3.8%	.333	3.77	11.8%	6.8%	27	-1.13	3.77	284	61

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	2.6	57	21.1%	7.9%	1.41	4.31
Tyler Duffey ('24)	2.4	61	23.1%	7.0%	1.24	4.57
Bob Lacey ('77-'84, 2 WAR*)	2.3	75	20.5%*	7.3%*	1.35*	4.11*
Shane Greene ('23)	2.4	59	21.7%	8.6%	1.12	4.50
Justin Speier ('09)	3.4	56	20.4%	8.0%	1.44	4.11
Keith Atherton ('83-'89, 2 WAR*)	2.8	81	19.9%*	8.7%*	1.39*	4.24*
Randy St. Claire ('84-'94, -0 WAR*)	1.3	50	19.0%*	8.4%*	1.39*	4.61*