Alex Troop

P Tennessee Smokies

Age 31 LHP 6'5" / 210 lbs AA

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	26	MIX2 levels	29	17	113	4.78	4.04	27.1%	6.5%	—	K 22.1% · BB 6.9% · FIP 5.08
2023	27	MIX2 levels	27	20	105	5.16	4.69	21.2%	10.2%	—	K 17.8% · BB 10.9% · FIP 5.03
2024	28	AATennessee Smo…	13	5	43	4.60	3.91	21.3%	6.2%	—	K 17.3% · BB 6.8% · FIP 5.10
2025	29	No MLB games played
2026	30	No MLB games played
2027PROJ	31	MLBCHC	—	—	0	4.41	4.41	21.3%	8.3%	—
2028PROJ31% MLB	32	MLBCHC	6	0	6	4.54	4.63	21.0%	8.8%	-0.0

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

Contract

No contract data on file.

CompsLuke Westphal (age 32) · Ryan Verdugo (age 30) · Santiago Gutierrez (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 101109vR 117

ERA4.41

FIP4.41

K%21.3%

BB%8.3%

K-BB%13.0%

Proj IP24

Value2027 · WAR

Runs saved vs league avg · 24 IP

Grades · Contract

Scouting Grades

K†5050

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)
2017	21	MIX MLE	2 levels	3	1	7	24	6	0	1	33%	2.3	25.0%	0.0%	25.0%	4.2%	1.29	3.28	—
2017	21	MIX RAW	2 levels	3	1	7	24	6	0	1	33%	2.3	25.0%	0.0%	25.0%	4.2%	1.29	3.24	—
2018	22	A MLE	Hagerstown Suns	4	3	17	79	16	5	2	75%	4.2	15.0%	7.5%	7.5%	3.0%	4.76	4.41	—
2018	22	A RAW	Hagerstown Suns	4	3	17	79	16	5	2	75%	4.2	20.3%	6.3%	13.9%	2.5%	4.76	3.63	—
2019	23	A MLE	Hagerstown Suns	11	0	33	123	34	3	2	0%	3.0	20.5%	2.9%	17.6%	2.2%	2.18	2.93	—
2019	23	A RAW	Hagerstown Suns	11	0	33	123	34	3	2	0%	3.0	27.6%	2.4%	25.2%	1.6%	2.18	2.10	—
2021	25	MIX MLE	2 levels	28	8	83	345	100	15	9	29%	3.0	21.0%	5.5%	15.5%	4.0%	3.35	4.22	—
2021	25	MIX RAW	2 levels	28	8	83	345	100	15	9	29%	3.0	29.0%	4.3%	24.6%	2.6%	3.35	2.64	—
2022	26	MIX MLE	2 levels	29	17	113	479	130	31	21	59%	3.9	22.1%	6.9%	15.2%	5.4%	4.78	5.08	—
2022	26	MIX RAW	2 levels	29	17	113	479	130	31	21	59%	3.9	27.1%	6.5%	20.7%	4.4%	4.78	4.04	—
2023	27	MIX MLE	2 levels	27	20	105	463	98	47	17	74%	3.9	17.8%	10.9%	6.8%	3.7%	5.16	5.03	—
2023	27	MIX RAW	2 levels	27	20	105	463	98	47	17	74%	3.9	21.2%	10.2%	11.0%	3.7%	5.16	4.69	—
2024	28	AA MLE	Tennessee Smokies	13	5	43	178	38	11	6	38%	3.3	17.3%	6.8%	10.5%	4.7%	4.60	5.10	—
2024	28	AA MLE	Tennessee Smokies	13	5	43	178	38	11	6	38%	3.3	17.3%	6.8%	10.5%	4.7%	4.60	5.10	—
2024	28	AA RAW	Tennessee Smokies	13	5	43	178	38	11	6	38%	3.3	21.3%	6.2%	15.2%	3.4%	4.60	3.91	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	CHC	—	—	0	—	—	—	—	—	—	21.3%	8.3%	13.0%	3.1%	4.41	4.41	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	CHC	19	0	20	83	17	7	3	0%	1.0	21.0%	8.8%	12.3%	3.3%	4.54	4.63	-0.0
2028	32	MLB CTX	CHC	6	0	6	25	5	2	1	0%	1.0	21.0%	8.8%	12.3%	3.3%	4.54	4.63	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		6	0	6	26	5	2	1	0%	1.0	21.0%	8.8%	12.3%	3.3%	4.54	4.25	+0.0
/ 200 IP	—	RAW		190	0	200	844	162	65	32	0%	1.1	21.0%	8.8%	12.3%	3.3%	4.54	4.25	+0.7
Career	—	MLE		115	54	401	1691	335	124	71	47%	3.5	19.8%	7.4%	12.4%	4.2%	4.29	4.68	-0.0
/ 200 IP	—	MLE		57	27	200	843	167	62	36	47%	3.5	19.8%	7.4%	12.4%	4.2%	4.29	4.68	-0.2

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 AATennessee Smokies · 43 IPFIP 3.93 → 5.10 (+1.17)K% 21.3% → 17.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.35%	—	×0.810	17.29%
BB% (unintentional) /BF	6.18%	—	×1.100	6.80%
HR / BF /BF	3.37%	÷0.854	×1.200	4.74%

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 71).

Result: FIP 3.93 → 5.10, 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).

2023 AAHarrisburg Senators · 101 IPFIP 4.41 → 5.03 (+0.62)K% 21.9% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.94%	—	×0.810	17.77%
BB% (unintentional) /BF	9.93%	—	×1.100	10.92%
HR / BF /BF	3.46%	÷1.115	×1.200	3.73%

Result: FIP 4.41 → 5.03, 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 AAARochester Red Wings · 4 IPFIP 7.60 → 7.70 (+0.10)K% 10.0% → 8.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	10.00%	—	×0.860	8.60%
BB% (unintentional) /BF	13.33%	—	×0.900	12.00%
HR / BF /BF	6.67%	÷1.058	×1.100	6.93%

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 7.60 → 7.70, 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 AAHarrisburg Senators · 112 IPFIP 4.01 → 5.08 (+1.06)K% 27.3% → 22.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.31%	—	×0.810	22.12%
BB% (unintentional) /BF	6.30%	—	×1.100	6.93%
HR / BF /BF	4.41%	÷0.979	×1.200	5.41%

Result: FIP 4.01 → 5.08, 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 A+Wilmington Blue Rocks · 73 IPFIP 2.99 → 4.22 (+1.22)K% 27.3% → 21.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.33%	—	×0.770	21.05%
BB% (unintentional) /BF	4.82%	—	×1.150	5.55%
HR / BF /BF	2.89%	÷0.906	×1.250	3.99%

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 81).

Result: FIP 2.99 → 4.22, 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 AAHarrisburg Senators · 11 IPFIP -0.63 → 0.08 (+0.71)K% 44.1% → 35.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	44.12%	—	×0.810	35.74%
BB% (unintentional) /BF	0.00%	—	×1.100	0.00%
HR / BF /BF	0.00%	÷1.037	×1.200	0.00%

Result: FIP -0.63 → 0.08, 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 AHagerstown Suns · 33 IPFIP 1.97 → 2.93 (+0.96)K% 27.6% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.64%	—	×0.740	20.46%
BB% (unintentional) /BF	2.44%	—	×1.180	2.88%
HR / BF /BF	1.63%	÷0.976	×1.300	2.17%

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 95).

Result: FIP 1.97 → 2.93, 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).

2018 AHagerstown Suns · 17 IPFIP 3.58 → 4.41 (+0.83)K% 20.3% → 15.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.25%	—	×0.740	14.99%
BB% (unintentional) /BF	6.33%	—	×1.180	7.47%
HR / BF /BF	2.53%	÷1.108	×1.300	2.97%

Result: FIP 3.58 → 4.41, 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
Alex Troop (OGILVIE)	—	—	4.41	21.3%	8.3%	3.1%	—	4.63	21.0%	12.3%	20	+0.22	—	—	—
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	72
Ryan Verdugo L 2017 · 3.36 FIP	30	SP-	3.36	22.7%	5.4%	2.7%	.333	—	—	—	—	—	3.36	153	71
Santiago Gutierrez L 2017 · 3.95 FIP	31	SP-	3.95	15.0%	8.2%	2.0%	.366	6.54	17.1%	3.7%	46	+2.59	3.90	341	66
Tyler Alexander L 2021 · 5.27 FIP	29	SW	5.27	15.7%	10.7%	3.9%	.272	—	—	—	—	—	5.27	128	65
Alejandro Soto L 2017 · 4.36 FIP	31	SW	4.36	13.8%	3.3%	3.7%	.323	4.03	17.1%	10.2%	61	-0.33	3.96	280	61
Jason Gurka L 2019 · 5.13 FIP	31	SW	5.13	15.5%	7.6%	4.3%	.382	—	—	—	—	—	2.82	183	61
Andrew Barbosa L 2017 · 4.67 FIP	29	SU	4.67	20.2%	9.6%	3.8%	.301	—	—	—	—	—	2.70	175	59
Andy Otero L 2022 · 4.36 FIP	30	SW	4.36	14.5%	8.9%	2.5%	.308	—	—	—	—	—	2.86	200	58
Anthony Vasquez L 2018 · 4.64 FIP	31	SW	4.64	17.1%	8.9%	3.4%	.322	6.00	14.6%	7.3%	85	+1.36	4.02	671	58
Sean Nolin L 2021 · 4.54 FIP	31	SP-	4.54	20.4%	8.9%	3.7%	.316	5.87	19.3%	12.9%	23	+1.33	3.79	309	58
John Bale L 2003 · 4.12 FIP	29	SP	4.12	19.0%	5.1%	3.6%	.305	—	—	—	—	—	2.49	168	56
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	54
Chris Capuano L 2010 · 4.06 FIP	31	SW	4.06	19.4%	7.2%	3.1%	.287	3.77	19.8%	14.1%	490	-0.29	3.40	1,458	54
Kwang Hyun Kim L 2020 · 3.76 FIP	32	SP	3.76	15.6%	6.5%	2.1%	.214	4.21	17.7%	9.5%	107	+0.45	3.76	146	54
Justin Marks L 2017 · 3.83 FIP	29	SW	3.83	18.7%	11.0%	1.7%	.317	—	—	—	—	—	3.83	324	52
Edgar Osuna L 2019 · 5.09 FIP	31	SW	5.09	10.0%	7.2%	3.5%	.373	—	—	—	—	—	5.05	175	52
Everett Teaford L 2015 · 4.42 FIP	31	SP-	4.42	13.9%	7.6%	2.8%	.324	—	—	—	—	—	4.42	208	52
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	49
Joey Lucchesi L 2024 · 4.73 FIP	31	SP-	4.73	14.9%	10.1%	2.9%	.293	3.65	22.6%	15.8%	102	-1.08	3.12	817	48
Vidal Nuño L 2018 · 4.00 FIP	31	SW	4.00	20.9%	3.5%	4.1%	.246	4.64	18.1%	11.3%	105	+0.64	4.00	606	47