Down on the Farm: The New York Mets – June 2025

Down on the Farm: The New York Mets – June 2025

The New York Mets Top 30 Prospects!

Rank Name Position Level Age Projections
1 Jett Williams SS AAA 21 122.8 wRC+, 0.250/0.362/0.420, 16 HR, 31 SB, 86 Runs, 64 RBI
2 Ryan Clifford 1B AA 21 122.2 wRC+, 0.237/0.348/0.440, 25 HR, 1 SB, 71 Runs, 79 RBI
3 A.J. Ewing CF A+ 20 111.2 wRC+, 0.251/0.345/0.397, 14 HR, 39 SB, 80 Runs, 57 RBI
4 Jonah Tong SP AA 22 3.73 ERA, 138 IP, 72 BB, 156 K, 10.2 K/9, 1.28 WHIP
5 Christian Scott SP MLB 25 3.76 ERA, 146 IP, 38 BB, 134 K, 8.3 K/9, 1.07 WHIP
6 Jacob Reimer 3B A+ 21 109.8 wRC+, 0.248/0.339/0.399, 16 HR, 7 SB, 71 Runs, 63 RBI
7 Christopher Suero C A+ 21 101.0 wRC+, 0.226/0.328/0.379, 18 HR, 24 SB, 68 Runs, 62 RBI
8 Jared Young LF MLB 29 102.2 wRC+, 0.234/0.314/0.406, 20 HR, 7 SB, 71 Runs, 68 RBI
9 Daiverson Gutierrez C A 19 106.6 wRC+, 0.245/0.337/0.389, 16 HR, 2 SB, 66 Runs, 62 RBI
10 Jesus Baez SS A+ 20 102.0 wRC+, 0.249/0.308/0.415, 20 HR, 6 SB, 63 Runs, 73 RBI
11 Carson Benge CF A+ 22 99.2 wRC+, 0.238/0.327/0.371, 12 HR, 9 SB, 72 Runs, 57 RBI
12 Jeremy Rodriguez SS A 18 96.4 wRC+, 0.250/0.317/0.375, 13 HR, 23 SB, 63 Runs, 56 RBI
13 Trey Snyder SS A 19 93.1 wRC+, 0.229/0.316/0.363, 14 HR, 27 SB, 64 Runs, 58 RBI
14 Marco Vargas 2B A+ 20 96.5 wRC+, 0.241/0.338/0.341, 8 HR, 27 SB, 63 Runs, 48 RBI
15 Nick Morabito CF AA 22 92.9 wRC+, 0.255/0.320/0.354, 7 HR, 40 SB, 65 Runs, 47 RBI
16 Eli Serrano III CF A+ 22 94.6 wRC+, 0.226/0.308/0.382, 17 HR, 4 SB, 69 Runs, 64 RBI
17 Drew Gilbert CF AAA 24 91.7 wRC+, 0.225/0.309/0.367, 16 HR, 5 SB, 64 Runs, 61 RBI
18 Julio Zayas C CPX 19 91.7 wRC+, 0.237/0.303/0.378, 16 HR, 3 SB, 62 Runs, 62 RBI
19 Calvin Ziegler SP A+ 21 3.88 ERA, 90 IP, 43 BB, 98 K, 9.8 K/9, 1.20 WHIP
20 Edward Lantigua CF CPX 18 86.8 wRC+, 0.232/0.304/0.354, 12 HR, 17 SB, 63 Runs, 54 RBI
21 Blade Tidwell SP MLB 24 4.41 ERA, 152 IP, 71 BB, 136 K, 8.1 K/9, 1.26 WHIP
22 Nolan McLean SP AAA 23 4.40 ERA, 148 IP, 68 BB, 121 K, 7.4 K/9, 1.36 WHIP
23 Boston Baro 3B A+ 20 87.2 wRC+, 0.243/0.300/0.362, 10 HR, 4 SB, 66 Runs, 54 RBI
24 Kevin Parada C AA 23 83.6 wRC+, 0.215/0.285/0.369, 19 HR, 1 SB, 61 Runs, 65 RBI
25 Ronald Hernandez C A+ 21 85.9 wRC+, 0.231/0.304/0.350, 13 HR, 8 SB, 58 Runs, 55 RBI
26 Blade Tidwell SP AAA 23 4.48 ERA, 149 IP, 69 BB, 123 K, 7.4 K/9, 1.29 WHIP
27 Alex Ramirez CF AA 22 78.5 wRC+, 0.228/0.292/0.335, 9 HR, 32 SB, 59 Runs, 52 RBI
28 Corey Collins 1B A 23 83.8 wRC+, 0.196/0.326/0.304, 11 HR, 4 SB, 63 Runs, 47 RBI
29 Estarling Mercado 1B A+ 22 80.1 wRC+, 0.207/0.289/0.348, 16 HR, 3 SB, 63 Runs, 59 RBI
30 Mike Vasil SP AAA 24 4.53 ERA, 152 IP, 46 BB, 103 K, 6.1 K/9, 1.24 WHIP

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 Francisco Alvarez Pete Alonso Ronny Mauricio Jett Williams Brett Baty Brandon Nimmo A.J. Ewing Juan Soto
Projected Backup Daiverson Gutierrez Ryan Clifford Jeff McNeil Francisco Lindor Mark Vientos Jesse Winker Carson Benge Starling Marte
Depth Christopher Suero Corey Collins Marco Vargas Jesus Baez Jacob Reimer Jared Young Eli Serrano III Tyrone Taylor

Starting Pitchers

Projected #1 Kodai Senga
Projected #2 Tylor Megill
Projected #3 Jonah Tong
Projected #4 Clay Holmes
Projected #5 Christian Scott
Depth David Peterson
Depth Sean Manaea
Depth Frankie Montas
Depth Calvin Ziegler
Depth Griffin Canning

Relief Pitchers

Projected Closer Edwin Díaz
Projected Setup #1 Danny Young
Projected Setup #2 Reed Garrett
Next Up A.J. Minter
Next Up Huascar Brazobán
Next Up Dylan Ross
Depth Nate Lavender
Depth Dedniel Núñez
Depth Austin Warren
Depth Anthony Nunez

Top prospects by tool!

Hit Power Speed
A.J. Ewing – 55 Ryan Clifford – 60 A.J. Ewing – 70
Nick Morabito – 55 Jared Young – 55 Nick Morabito – 70
Jacob Reimer – 55 Trace Willhoite – 50 Omar De Los Santos – 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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