Down on the Farm: The New York Yankees – October 2025

Down on the Farm: The New York Yankees – October 2025

The New York Yankees Top 30 Prospects!

Rank Name Position Level Age Peak Projections
1 Spencer Jones CF AAA 24 110.3 wRC+, 0.241/0.307/0.453, 28 HR, 22 SB, 71 Runs, 81 RBI
2 Dax Kilby SS A 18 110.7 wRC+, 0.262/0.338/0.405, 14 HR, 42 SB, 74 Runs, 59 RBI
3 Enmanuel Tejeda 3B A 20 102.7 wRC+, 0.253/0.331/0.381, 14 HR, 31 SB, 64 Runs, 57 RBI
4 Ben Hess SP AA 22 4.04 ERA, 144 IP, 69 BB, 143 K, 9.0 K/9, 1.27 WHIP
5 Brennen Davis DH AAA 25 98.0 wRC+, 0.217/0.306/0.401, 24 HR, 3 SB, 64 Runs, 73 RBI
6 George Lombard Jr. SS AA 20 97.6 wRC+, 0.228/0.333/0.354, 11 HR, 25 SB, 69 Runs, 56 RBI
7 Brendan Jones CF AA 23 93.3 wRC+, 0.224/0.323/0.351, 12 HR, 36 SB, 73 Runs, 52 RBI
8 Jace Avina CF AA 22 97.0 wRC+, 0.226/0.311/0.387, 18 HR, 1 SB, 63 Runs, 69 RBI
9 Carlos Lagrange SP AA 22 4.20 ERA, 144 IP, 83 BB, 152 K, 9.5 K/9, 1.36 WHIP
10 Juan Torres 2B DSL 17 90.6 wRC+, 0.251/0.283/0.404, 19 HR, 20 SB, 59 Runs, 69 RBI
11 Elmer Rodriguez-Cruz SP AAA 21 4.28 ERA, 151 IP, 75 BB, 131 K, 7.8 K/9, 1.41 WHIP
12 Queni Pineda C DSL 18 93.5 wRC+, 0.213/0.319/0.359, 17 HR, 4 SB, 63 Runs, 61 RBI
13 Jackson Lovich 1B A 21 92.2 wRC+, 0.239/0.309/0.369, 12 HR, 14 SB, 69 Runs, 56 RBI
14 Dillon Lewis CF A+ 22 86.3 wRC+, 0.221/0.277/0.395, 22 HR, 12 SB, 65 Runs, 71 RBI
15 Richard Matic 3B DSL 17 90.1 wRC+, 0.214/0.311/0.358, 16 HR, 10 SB, 65 Runs, 60 RBI
16 Christopher Familia LF AA 25 85.0 wRC+, 0.219/0.279/0.387, 22 HR, 3 SB, 62 Runs, 70 RBI
17 T.J. Rumfield 1B AAA 25 90.9 wRC+, 0.237/0.312/0.359, 13 HR, 2 SB, 59 Runs, 57 RBI
18 Camden Troyer OF A+ 22 89.6 wRC+, 0.236/0.309/0.357, 12 HR, 5 SB, 63 Runs, 55 RBI
19 Leni Done 3B DSL 18 82.3 wRC+, 0.214/0.287/0.361, 18 HR, 20 SB, 60 Runs, 63 RBI
20 Jorbit Vivas 2B MLB 24 88.4 wRC+, 0.231/0.326/0.324, 7 HR, 12 SB, 63 Runs, 46 RBI
21 Tyler Hardman 3B AA 26 78.1 wRC+, 0.204/0.259/0.387, 24 HR, 10 SB, 61 Runs, 72 RBI
22 Dylan Jasso 3B AA 22 85.1 wRC+, 0.231/0.291/0.367, 14 HR, 1 SB, 65 Runs, 59 RBI
23 Omar Martinez C AAA 23 84.4 wRC+, 0.211/0.297/0.353, 17 HR, 1 SB, 60 Runs, 60 RBI
24 Chase Hampton SP AA 22 4.41 ERA, 147 IP, 52 BB, 127 K, 7.8 K/9, 1.20 WHIP
25 Engelth Urena 1B A+ 20 84.4 wRC+, 0.215/0.297/0.354, 16 HR, 1 SB, 58 Runs, 63 RBI
26 Wilson Rodriguez RF A 20 82.2 wRC+, 0.221/0.290/0.356, 15 HR, 12 SB, 62 Runs, 59 RBI
27 Justin Capellan C DSL 18 79.2 wRC+, 0.208/0.281/0.357, 18 HR, 19 SB, 55 Runs, 66 RBI
28 Eric Genther OF A 23 85.4 wRC+, 0.220/0.309/0.339, 12 HR, 6 SB, 62 Runs, 53 RBI
29 J.C. Escarra C MLB 30 85.3 wRC+, 0.226/0.306/0.345, 10 HR, 4 SB, 65 Runs, 52 RBI
30 Jose Castro CF DSL 19 75.2 wRC+, 0.197/0.279/0.343, 19 HR, 33 SB, 56 Runs, 62 RBI

Projected organization depth!

Below are the top projected players at peak for each position at the MLB level.

C 1B 2B SS 3B LF CF RF
Projected Starter Austin Wells Ben Rice Jazz Chisholm Jr. Dax Kilby Enmanuel Tejeda Jasson Domínguez Aaron Judge Cody Bellinger
Projected Backup Queni Pineda Paul Goldschmidt Amed Rosario Anthony Volpe Ryan McMahon Bryan De La Cruz Spencer Jones Austin Slater
Depth J.C. Escarra Jackson Lovich Juan Torres George Lombard Jr. Oswaldo Cabrera Christopher Familia Trent Grisham Jackson Castillo

Starting Pitchers

Projected #1 Max Fried
Projected #2 Gerrit Cole
Projected #3 Carlos Rodón
Projected #4 Cam Schlittler
Projected #5 Clarke Schmidt
Depth Will Warren
Depth Ben Hess
Depth Luis Gil
Depth Carlos Lagrange
Depth Elmer Rodriguez-Cruz

Relief Pitchers

Projected Closer Devin Williams
Projected Setup #1 David Bednar
Projected Setup #2 Camilo Doval
Next Up Fernando Cruz
Next Up Jake Bird
Next Up Jake Cousins
Depth Jonathan Loáisiga
Depth Tim Hill
Depth Ian Hamilton
Depth Scott Effross

Top prospects by tool!

Hit Power Speed
Dax Kilby – 60 Spencer Jones – 60 Duke Ellis – 80
Enmanuel Tejeda – 55 Dillon Lewis – 55 Dax Kilby – 70
Ismael Munguia – 50 Carlos Villarroel – 55 Christofer Reyes – 70

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What are Scout the Statline Peak Projections?
Scout the Statline Peak Projections are based entirely on data analysis. Combining performance data with advanced statistics, translating the results to an equalized playing field (the MLB level), and scaling the results up to a player’s peak ages, allows us to instantly compare thousands of players across different ages, environments, and organizations! While the results can often be quite different than you might find from other scout-based analysis, peak projections provide a bias-free look at players with all the best data available, based on sound data science practices!

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1 Comment

  1. Thankyou for all your efforts that you have put in this. very interesting info .

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