Jack Patterson

P South Bend Cubs

Age 31 LHP 6'0" / 210 lbs A+

Overview

A+PROSPECT

MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2018	22	ROKAZL Cubs 2	10	5	30	2.70	3.40	21.6%	6.4%	—	K 14.7% · BB 8.0% · FIP 4.72
2019	23	MIX3 levels	24	9	80	1.69	2.42	25.6%	9.9%	—	K 19.4% · BB 11.5% · FIP 3.10
2024	28	MIX3 levels	20	0	29	5.65	3.48	25.4%	16.4%	—	K 17.4% · BB 20.5% · FIP 5.13
2025	29	No MLB games played
2026	30	No MLB games played
2027PROJ	31	MLBCHC	—	—	0	4.71	4.71	22.5%	11.8%	—
2028PROJ30% MLB	32	MLBCHC	5	0	5	4.80	4.89	22.2%	11.9%	-0.0

Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsRaul Valdes (age 32) · Andrew Barbosa (age 29) · Luke Westphal (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 106115vR 123

ERA4.71

FIP4.71

K%22.5%

BB%11.8%

K-BB%10.8%

Proj IP10

Value2027 · WAR

Runs saved vs league avg · 10 IP

Grades · Contract

Scouting Grades

K†5555

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)
2018	22	ROK MLE	AZL Cubs 2	10	5	30	125	27	8	3	50%	3.0	14.7%	8.0%	6.7%	3.4%	2.70	4.72	—
2018	22	ROK RAW	AZL Cubs 2	10	5	30	125	27	8	3	50%	3.0	21.6%	6.4%	15.2%	2.4%	2.70	3.40	—
2019	23	MIX MLE	3 levels	24	9	80	312	80	31	1	38%	3.3	19.4%	11.5%	7.9%	0.3%	1.69	3.10	—
2019	23	MIX RAW	3 levels	24	9	80	312	80	31	1	38%	3.3	25.6%	9.9%	15.7%	0.3%	1.69	2.42	—
2024	28	MIX MLE	3 levels	20	0	29	134	34	22	1	0%	1.4	17.4%	20.5%	-3.1%	1.6%	5.65	5.13	—
2024	28	MIX MLE	3 levels	20	0	29	134	34	22	1	0%	1.4	17.4%	20.5%	-3.1%	1.6%	5.65	5.13	—
2024	28	MIX RAW	3 levels	20	0	29	134	34	22	1	0%	1.4	25.4%	16.4%	9.0%	0.7%	5.65	3.48	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	CHC	—	—	0	—	—	—	—	—	—	22.5%	11.8%	10.8%	3.0%	4.71	4.71	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	CHC	16	0	17	73	16	9	2	0%	1.1	22.2%	11.9%	10.3%	3.2%	4.80	4.89	-0.1
2028	32	MLB CTX	CHC	5	0	5	21	5	3	1	0%	1.0	22.2%	11.9%	10.3%	3.2%	4.80	4.89	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		5	0	5	22	5	3	1	0%	1.0	22.2%	11.9%	10.3%	3.2%	4.80	4.51	+0.0
/ 200 IP	—	RAW		190	0	200	844	192	115	38	0%	1.1	22.2%	11.9%	10.3%	3.2%	4.80	4.51	+0.1
Career	—	MLE		54	14	138	571	102	73	7	26%	2.6	17.9%	12.9%	5.1%	1.3%	2.73	3.93	-0.1
/ 200 IP	—	MLE		78	20	200	826	148	106	11	26%	2.6	17.9%	12.9%	5.1%	1.3%	2.73	3.93	-0.8

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 A+South Bend Cubs · 18 IPFIP 4.11 → 5.13 (+1.02)K% 22.6% → 17.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.62%	—	×0.770	17.42%
BB% (unintentional) /BF	17.86%	—	×1.150	20.54%
HR / BF /BF	1.19%	÷0.909	×1.250	1.64%

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 82).

Result: FIP 4.11 → 5.13, 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 ROKACL Cubs · 10 IPFIP 2.59 → 3.98 (+1.40)K% 31.7% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.71%	—	×0.680	21.56%
BB% (unintentional) /BF	17.07%	—	×1.250	21.34%
HR / BF /BF	0.00%	÷0.935	×1.400	0.00%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 87).

Result: FIP 2.59 → 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).

2019 ASouth Bend Cubs · 42 IPFIP 2.03 → 2.87 (+0.84)K% 27.8% → 20.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.81%	—	×0.740	20.58%
BB% (unintentional) /BF	10.06%	—	×1.180	11.87%
HR / BF /BF	0.00%	÷0.914	×1.300	0.00%

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 83).

Result: FIP 2.03 → 2.87, 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 A+Myrtle Beach Pelicans · 24 IPFIP 1.96 → 2.66 (+0.69)K% 27.0% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.97%	—	×0.770	20.76%
BB% (unintentional) /BF	8.99%	—	×1.150	10.34%
HR / BF /BF	0.00%	÷0.956	×1.250	0.00%

Result: FIP 1.96 → 2.66, 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).

2019 AATennessee Smokies · 14 IPFIP 4.12 → 4.58 (+0.46)K% 16.7% → 13.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.67%	—	×0.810	13.50%
BB% (unintentional) /BF	11.11%	—	×1.100	12.22%
HR / BF /BF	1.85%	÷1.142	×1.200	1.95%

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 128).

Result: FIP 4.12 → 4.58, 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).

2018 ROKAZL Cubs 2 · 30 IPFIP 3.40 → 4.72 (+1.31)K% 21.6% → 14.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.60%	—	×0.680	14.69%
BB% (unintentional) /BF	6.40%	—	×1.250	8.00%
HR / BF /BF	2.40%	÷1.000	×1.400	3.36%

Result: FIP 3.40 → 4.72, 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
Jack Patterson (OGILVIE)	—	—	4.71	22.5%	11.8%	3.0%	—	4.89	22.2%	10.3%	17	+0.18	—	—	—
Raul Valdes L 2010 · 4.02 FIP	32	SU	4.02	21.4%	9.9%	2.7%	.301	3.14	27.4%	22.5%	78	-0.88	2.29	160	73
Andrew Barbosa L 2017 · 4.67 FIP	29	SU	4.67	20.2%	9.6%	3.8%	.301	—	—	—	—	—	2.70	175	71
Luke Westphal L 2021 · 4.70 FIP	32	SP-	4.70	22.7%	13.1%	3.4%	.321	4.58	21.9%	7.6%	95	-0.12	2.97	294	69
Edward Paredes L 2018 · 3.81 FIP	31	SU	3.81	22.7%	11.9%	2.0%	.351	5.10	21.5%	14.2%	18	+1.29	2.36	130	68
Jason Gurka L 2019 · 5.13 FIP	31	SW	5.13	15.5%	7.6%	4.3%	.382	—	—	—	—	—	2.82	183	67
Nick Hagadone L 2017 · 3.74 FIP	31	SU	3.74	22.6%	9.3%	2.5%	.267	—	—	—	—	—	2.97	141	65
Ryan O'Rourke L 2019 · 4.08 FIP	31	SU	4.08	22.1%	14.3%	1.9%	.290	—	—	—	—	—	3.05	145	64
Tyler Alexander L 2021 · 5.27 FIP	29	SW	5.27	15.7%	10.7%	3.9%	.272	—	—	—	—	—	5.27	128	64
Jeremy Bleich L 2018 · 3.69 FIP	31	SU	3.69	20.3%	6.6%	2.7%	.296	4.41	20.1%	13.1%	33	+0.72	3.20	241	63
David Purcey L 2013 · 3.79 FIP	31	SU	3.79	20.4%	11.5%	1.7%	.227	—	—	—	—	—	3.60	206	63
Justin Marks L 2017 · 3.83 FIP	29	SW	3.83	18.7%	11.0%	1.7%	.317	—	—	—	—	—	3.83	324	62
George Sherrill L 2008 · 3.86 FIP	31	SU	3.86	24.3%	11.3%	2.5%	.277	3.38	20.6%	12.4%	141	-0.48	2.74	323	62
James Jones L 2019 · 4.28 FIP	30	SU	4.28	21.9%	13.0%	2.5%	.230	4.65	23.9%	8.8%	35	+0.37	3.58	127	62
Cory Luebke L 2016 · 3.88 FIP	31	SU	3.88	26.4%	9.1%	3.4%	.354	—	—	—	—	—	2.80	216	62
Adam Liberatore L 2018 · 4.28 FIP	31	SU	4.28	18.6%	9.1%	2.9%	.354	—	—	—	—	—	2.38	124	62
Randy Flores L 2006 · 4.00 FIP	31	SU	4.00	20.4%	9.7%	2.5%	.333	3.30	18.1%	10.5%	93	-0.70	2.51	250	62
Juan Pablo Perez L 2007 · 5.32 FIP	28	SU	5.32	17.5%	14.0%	3.5%	.324	—	—	—	—	—	1.75	104	61
Buddy Baumann L 2018 · 4.87 FIP	30	SU	4.87	20.5%	11.9%	3.6%	.258	—	—	—	—	—	2.92	155	60
Brian Shouse L 2002 · 5.54 FIP	33	SU	5.54	15.5%	11.3%	4.2%	.245	3.31	16.3%	10.7%	159	-2.23	2.32	339	59
Buddy Boshers L 2019 · 3.73 FIP	31	SU	3.73	25.3%	10.4%	2.6%	.294	—	—	—	—	—	2.76	239	59