Robert Gsellman

P Rochester Red Wings

Age 34 RHP 6'4" / 237 lbs AAA peak 0.1 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2021	28	MIX3 levels	23	3	35	4.89	3.81	14.5%	6.6%	0.3	K 14.3% · BB 5.9% · FIP 4.03
2022	29	MIX2 levels	18	9	44	5.68	4.12	16.2%	7.3%	0.7	K 14.8% · BB 7.6% · FIP 4.36
2024	31	AAAWSH	17	4	36	8.75	4.24	20.1%	11.2%	—	K 17.3% · BB 10.1% · FIP 4.37
2025	32	No MLB games played
2026	33	No MLB games played
2027PROJ	34	MLBWSH	22	0	23	4.53	4.41	22.6%	9.3%	0.0
2028PROJ	35	MLBWSH	113	0	119	4.66	4.74	21.6%	9.9%	-0.3
2029PROJ	36	MLBWSH	86	0	90	4.93	5.11	20.6%	10.5%	-0.6

Last 3 seasons WAR 1.0 · Next 3 projected WAR -0.8Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2022

FB Velo38

K%10

xwOBA vs10

CompsPaolo Espino (age 34) · Tiago Da Silva (age 34) · Chris Jakubauskas (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117109vR 101

ERA4.53

FIP4.41

K%22.6%

BB%9.3%

K-BB%13.3%

Proj IP165

Value2027 · WAR

Runs saved vs league avg · 165 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p3893.7

Whiff%

p4324.3%

p1014.1%

xwOBA vs

p100.396

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	22	MIX MLE	2 levels	24	24	143	583	86	36	5	100%	6.0	11.7%	6.9%	4.8%	0.9%	2.89	3.49	—
2015	22	MIX RAW	2 levels	24	24	143	583	86	36	5	100%	6.0	14.8%	6.2%	8.6%	0.9%	2.89	3.11	—
2016	23	MIX MLE	3 levels	28	27	160	666	130	44	11	96%	5.7	17.4%	7.0%	10.5%	1.7%	3.55	3.44	+3.8
2016	23	MIX RAW	3 levels	28	27	160	666	130	44	11	96%	5.7	19.5%	6.6%	12.9%	1.7%	3.55	3.19	+4.4
2017	24	MIX MLE	3 levels	30	27	138	634	94	47	18	90%	4.6	14.8%	7.4%	7.4%	2.8%	5.09	4.33	+1.7
2017	24	MIX RAW	3 levels	30	27	138	634	94	47	18	90%	4.6	14.8%	7.4%	7.4%	2.8%	5.09	4.46	+1.5
2018	25	MLB MLE	New York Mets	68	0	80	345	70	22	8	0%	1.2	20.3%	6.4%	13.9%	2.3%	4.28	3.47	+1.0
2018	25	MLB RAW	New York Mets	68	0	80	345	70	22	8	0%	1.2	20.3%	6.4%	13.9%	2.3%	4.28	3.48	+1.0
2019	26	MLB MLE	New York Mets	52	0	64	277	60	21	7	0%	1.2	21.7%	7.6%	14.1%	2.5%	4.66	3.62	+0.7
2019	26	MLB RAW	New York Mets	52	0	64	277	60	21	7	0%	1.2	21.7%	7.6%	14.1%	2.5%	4.66	3.63	+0.7
2020	27	MLB MLE	New York Mets	6	4	14	71	9	8	4	67%	2.3	12.7%	11.3%	1.4%	5.6%	9.64	6.56	-0.2
2020	27	MLB RAW	New York Mets	6	4	14	71	9	8	4	67%	2.3	12.7%	11.3%	1.4%	5.6%	9.64	7.24	-0.3
2021	28	MIX MLE	3 levels	23	3	35	152	22	10	3	13%	1.5	14.3%	5.9%	8.4%	2.5%	4.89	4.03	+0.2
2021	28	MIX RAW	3 levels	23	3	35	152	22	10	3	13%	1.5	14.5%	6.6%	7.9%	2.0%	4.89	3.81	+0.3
2022	29	MIX MLE	2 levels	18	9	44	191	31	14	5	50%	2.5	14.8%	7.6%	7.2%	2.8%	5.68	4.36	+0.5
2022	29	MIX RAW	2 levels	18	9	44	191	31	14	5	50%	2.5	16.2%	7.3%	8.9%	2.6%	5.68	4.12	+0.7
2024	31	AAA MLE	Rochester Red Wings	17	4	36	169	34	19	4	24%	2.1	17.3%	10.1%	7.2%	2.6%	8.75	4.37	—
2024	31	AAA MLE	Rochester Red Wings	17	4	36	169	34	19	4	24%	2.1	17.3%	10.1%	7.2%	2.6%	8.75	4.37	—
2024	31	AAA RAW	Rochester Red Wings	17	4	36	169	34	19	4	24%	2.1	20.1%	11.2%	8.9%	2.4%	8.75	4.24	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			184	34	366	1610	289	113	42	18%	2.0	18.0%	7.0%	10.9%	2.6%	4.60	3.94	+2.7
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	WSH	22	0	23	97	22	9	3	0%	1.0	22.6%	9.3%	13.3%	3.0%	4.53	4.41	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	WSH	113	0	119	501	108	49	17	0%	1.1	21.6%	9.9%	11.8%	3.4%	4.66	4.74	-0.3
2029	36	MLB MLE	WSH	85	0	90	378	78	40	14	0%	1.1	20.6%	10.5%	10.1%	3.7%	4.93	5.11	-0.6
2028	35	MLB CTX	WSH	113	0	119	502	109	50	17	0%	1.1	21.6%	9.9%	11.8%	3.4%	4.66	4.74	-0.3
2029	36	MLB CTX	WSH	86	0	90	380	78	40	14	0%	1.0	20.6%	10.5%	10.1%	3.7%	4.93	5.11	-0.6
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		382	34	574	2489	475	202	73	9%	1.5	19.1%	8.1%	11.0%	2.9%	4.66	4.12	+2.9
/ 200 IP	—	RAW		133	12	200	867	165	70	25	9%	1.5	19.1%	8.1%	11.0%	2.9%	4.66	4.12	+1.0
Career	—	MLE		266	98	714	3088	496	225	67	37%	2.7	16.1%	7.3%	8.8%	2.2%	4.47	3.86	+1.8
/ 200 IP	—	MLE		75	27	200	865	139	63	19	37%	2.7	16.1%	7.3%	8.8%	2.2%	4.47	3.86	+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.

2024 AAARochester Red Wings · 36 IPFIP 4.12 → 4.37 (+0.25)K% 20.1% → 17.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.12%	—	×0.860	17.30%
BB% (unintentional) /BF	11.24%	—	×0.900	10.12%
HR / BF /BF	2.37%	÷0.986	×1.100	2.64%

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 97).

Result: FIP 4.12 → 4.37, 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 AAAIowa Cubs · 29 IPFIP 4.03 → 4.40 (+0.37)K% 17.3% → 15.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.32%	—	×0.880	15.24%
BB% (unintentional) /BF	8.66%	—	×1.050	9.09%
HR / BF /BF	2.36%	÷0.996	×1.100	2.61%

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 99).

Result: FIP 4.03 → 4.40, 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 AAASyracuse Mets · 5 IPFIP 3.10 → 3.29 (+0.19)K% 13.0% → 11.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.04%	—	×0.880	11.48%
BB% (unintentional) /BF	8.70%	—	×1.050	9.13%
HR / BF /BF	0.00%	÷1.039	×1.100	0.00%

Result: FIP 3.10 → 3.29, 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 AABinghamton Rumble Ponies · 12 IPFIP 2.87 → 3.25 (+0.38)K% 16.4% → 13.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.36%	—	×0.810	13.25%
BB% (unintentional) /BF	9.09%	—	×1.100	10.00%
HR / BF /BF	0.00%	÷1.365	×1.200	0.00%

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 173).

Result: FIP 2.87 → 3.25, 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).

2017 AAALas Vegas 51s · 6 IPFIP 5.35 → 5.35 (+0.00)K% 10.0% → 8.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	10.00%	—	×0.880	8.80%
BB% (unintentional) /BF	10.00%	—	×1.050	10.50%
HR / BF /BF	3.33%	÷1.209	×1.100	3.03%

Result: FIP 5.35 → 5.35, 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 AABinghamton Mets · 66 IPFIP 2.70 → 3.11 (+0.40)K% 18.0% → 14.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.05%	—	×0.810	14.62%
BB% (unintentional) /BF	5.64%	—	×1.100	6.20%
HR / BF /BF	0.75%	÷1.085	×1.200	0.83%

Result: FIP 2.70 → 3.11, 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 AAALas Vegas 51s · 49 IPFIP 4.51 → 4.77 (+0.26)K% 18.6% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.60%	—	×0.880	16.37%
BB% (unintentional) /BF	7.44%	—	×1.050	7.81%
HR / BF /BF	3.72%	÷1.088	×1.100	3.76%

Result: FIP 4.51 → 4.77, 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 AABinghamton Mets · 92 IPFIP 3.42 → 3.76 (+0.34)K% 12.7% → 10.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	12.66%	—	×0.810	10.26%
BB% (unintentional) /BF	6.46%	—	×1.100	7.11%
HR / BF /BF	1.03%	÷1.088	×1.200	1.14%

Result: FIP 3.42 → 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).

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
Robert Gsellman (OGILVIE)	—	—	—	—	—	3.0%	—	4.90	21.2%	11.1%	208	+0.49	—	—	—
Paolo Espino R 2021 · 4.32 FIP	34	SP-	4.32	20.2%	5.1%	4.2%	.277	4.81	16.6%	10.9%	260	+0.49	3.42	933	59
Tiago Da Silva R 2019 · 3.95 FIP	34	SW	3.95	15.0%	7.2%	2.2%	.365	—	—	—	—	—	2.21	287	58
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	55
Fernando Rodriguez Jr. R 2018 · 4.07 FIP	34	SW	4.07	23.7%	9.9%	3.1%	.253	5.11	25.0%	13.8%	32	+1.04	3.34	335	55
Jarrett Grube R 2016 · 4.74 FIP	34	SP-	4.74	18.9%	7.2%	4.2%	.280	4.41	15.8%	7.7%	134	-0.33	3.75	393	52
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	52
Albert Suárez R 2024 · 4.00 FIP	34	SP-	4.00	19.5%	6.8%	3.1%	.293	4.14	17.8%	10.6%	80	+0.14	3.58	616	51
Giovanni Carrara R 2002 · 4.78 FIP	34	SU	4.78	14.5%	7.2%	3.6%	.239	3.83	17.1%	7.9%	158	-0.95	2.50	462	50
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	49
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	47
Josh Lindblom R 2021 · 3.79 FIP	34	SP-	3.79	22.7%	6.9%	3.2%	.319	5.07	20.8%	12.0%	133	+1.28	1.83	480	47
Nelson Figueroa R 2008 · 3.94 FIP	34	SW	3.94	17.1%	11.8%	1.4%	.306	3.96	17.5%	9.9%	192	+0.02	3.71	499	46
Jean Machi R 2016 · 4.36 FIP	34	SU	4.36	18.3%	6.6%	3.6%	.282	4.12	18.6%	12.0%	67	-0.24	2.16	306	45
Scott McGough R 2024 · 4.56 FIP	34	SU	4.56	18.6%	12.2%	2.7%	.289	3.82	20.3%	13.1%	57	-0.74	3.55	429	45
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	44
Tim Dillard R 2017 · 3.62 FIP	34	SU	3.62	15.1%	8.8%	1.2%	.310	4.32	13.2%	7.1%	188	+0.70	2.92	432	44
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	43
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	43
Rafael Martín R 2017 · 3.40 FIP	33	SU	3.40	20.4%	5.1%	2.5%	.305	4.71	22.5%	16.5%	47	+1.31	2.94	227	43
Marco Antonio Duarte R 2019 · 5.03 FIP	32	SW	5.03	12.2%	9.1%	3.3%	.420	—	—	—	—	—	4.07	203	42

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	3.7	92	19.9%	8.8%	1.14	4.63
Odell Jones ('75-'88, 2 WAR*)	2.7	78	20.8%*	8.8%*	1.18*	4.62*
Scott Sanders ('99)	3.4	97	21.2%	9.3%	1.23	4.86
Mike Paul ('68-'74, 2 WAR*)	3.1	105	22.4%*	8.7%*	1.22*	4.77*
Marvin Freeman ('86-'96, 5 WAR*)	3.0	74	17.7%*	9.6%*	1.19*	4.33*
Dustin McGowan ('17)	3.3	65	19.2%	9.9%	1.08	4.50
Adam Conley ('21)	2.2	87	19.8%	9.4%	1.16	4.71