Down on the Farm: The Miami Marlins – June 2025

Down on the Farm: The Miami Marlins – June 2025

The Miami Marlins Top 30 Prospects!

Rank Name Position Level Age Projections
1 Thomas White SP A+ 20 3.85 ERA, 135 IP, 63 BB, 133 K, 8.9 K/9, 1.28 WHIP
2 Deyvison De Los Santos 1B AAA 22 104.3 wRC+, 0.255/0.297/0.442, 25 HR, 2 SB, 65 Runs, 79 RBI
3 Starlyn Caba SS A 19 102.8 wRC+, 0.236/0.348/0.355, 12 HR, 44 SB, 66 Runs, 51 RBI
4 Andrew Salas SS A 17 100.1 wRC+, 0.240/0.335/0.364, 12 HR, 29 SB, 67 Runs, 54 RBI
5 Heriberto Hernandez N/A MLB 25 98.8 wRC+, 0.217/0.308/0.400, 23 HR, 6 SB, 68 Runs, 72 RBI
6 Kemp Alderman RF AA 22 98.5 wRC+, 0.248/0.297/0.417, 20 HR, 13 SB, 67 Runs, 69 RBI
7 Wilfredo Lara SS A+ 21 95.5 wRC+, 0.240/0.308/0.386, 15 HR, 16 SB, 68 Runs, 61 RBI
8 Echedry Vargas SS A+ 20 90.1 wRC+, 0.240/0.273/0.418, 23 HR, 18 SB, 60 Runs, 78 RBI
9 Jakob Marsee CF AAA 24 90.5 wRC+, 0.210/0.330/0.329, 11 HR, 44 SB, 71 Runs, 47 RBI
10 Joe Mack C AAA 22 94.9 wRC+, 0.230/0.309/0.382, 18 HR, 1 SB, 62 Runs, 65 RBI
11 Graham Pauley 3B MLB 24 90.2 wRC+, 0.225/0.298/0.378, 17 HR, 7 SB, 64 Runs, 67 RBI
12 Dub Gleed 3B AAA 22 92.8 wRC+, 0.228/0.321/0.353, 12 HR, 5 SB, 66 Runs, 55 RBI
13 Jancory De La Cruz N/A CPX 19 90.2 wRC+, 0.218/0.315/0.351, 15 HR, 7 SB, 64 Runs, 56 RBI
14 Andres Valor CF A 19 83.1 wRC+, 0.223/0.294/0.353, 13 HR, 39 SB, 64 Runs, 56 RBI
15 Fenwick Trimble CF AA 22 85.8 wRC+, 0.234/0.303/0.351, 10 HR, 34 SB, 66 Runs, 52 RBI
16 Robby Snelling SP AAA 21 4.39 ERA, 153 IP, 62 BB, 121 K, 7.1 K/9, 1.31 WHIP
17 Maximo Acosta SS AAA 22 86.1 wRC+, 0.243/0.301/0.356, 10 HR, 29 SB, 58 Runs, 55 RBI
18 Victor Mesa Jr. N/A MLB 23 86.4 wRC+, 0.240/0.289/0.376, 14 HR, 7 SB, 64 Runs, 60 RBI
19 Abrahan Ramirez 3B A 20 89.2 wRC+, 0.235/0.317/0.343, 9 HR, 6 SB, 64 Runs, 51 RBI
20 Jared Serna SS AAA 23 84.9 wRC+, 0.232/0.294/0.362, 14 HR, 13 SB, 58 Runs, 61 RBI
21 Emaarion Boyd CF A+ 21 81.2 wRC+, 0.235/0.303/0.330, 7 HR, 46 SB, 64 Runs, 44 RBI
22 Liam Hicks C MLB 26 91.3 wRC+, 0.234/0.334/0.325, 7 HR, 1 SB, 63 Runs, 45 RBI
23 Troy Johnston 1B AAA 28 81.9 wRC+, 0.232/0.291/0.352, 11 HR, 18 SB, 62 Runs, 55 RBI
24 Cody Schrier SS A 22 75.2 wRC+, 0.218/0.280/0.341, 13 HR, 41 SB, 61 Runs, 54 RBI
25 Nathan Martorella 1B AA 24 82.5 wRC+, 0.217/0.290/0.357, 16 HR, 2 SB, 58 Runs, 62 RBI
26 Dillon Head CF A 20 78.8 wRC+, 0.224/0.285/0.348, 11 HR, 22 SB, 71 Runs, 50 RBI
27 Adam Mazur SP MLB 24 4.47 ERA, 148 IP, 42 BB, 112 K, 6.8 K/9, 1.19 WHIP
28 Ian Lewis SS A 22 76.7 wRC+, 0.222/0.279/0.349, 12 HR, 28 SB, 64 Runs, 55 RBI
29 John Cruz RF A 19 79.0 wRC+, 0.218/0.280/0.358, 16 HR, 12 SB, 58 Runs, 62 RBI
30 Jesus Hernandez SS AA 21 79.6 wRC+, 0.219/0.290/0.343, 12 HR, 16 SB, 65 Runs, 54 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 Agustin Ramirez Deyvison De Los Santos Otto Lopez Starlyn Caba Connor Norby Kyle Stowers Javier Sanoja Jesús Sánchez
Projected Backup Joe Mack Nathan Martorella Gage Miller Andrew Salas Dub Gleed Jose Monserrate Jakob Marsee Kemp Alderman
Depth Liam Hicks Troy Johnston Cody Morissette Xavier Edwards Graham Pauley Danny Gonzalez Fenwick Trimble Griffin Conine

Starting Pitchers

Projected #1 Braxton Garrett
Projected #2 Sandy Alcantara
Projected #3 Eury Perez
Projected #4 Edward Cabrera
Projected #5 Thomas White
Depth Max Meyer
Depth Ryan Weathers
Depth Robby Snelling
Depth Adam Mazur
Depth Janson Junk

Relief Pitchers

Projected Closer Andrew Nardi
Projected Setup #1 Anthony Bender
Projected Setup #2 Ronny Henriquez
Next Up Calvin Faucher
Next Up Emmanuel Ramirez
Next Up Jesus Tinoco
Depth Elvis Alvarado
Depth Nigel Belgrave
Depth Luarbert Arias
Depth Jesse Bergin

Top prospects by tool!

Hit Power Speed
Andrew Salas – 50 Deyvison De Los Santos – 60 Cody Schrier – 70
Dub Gleed – 50 Echedry Vargas – 55 Andres Valor – 70
Fenwick Trimble – 50 Heriberto Hernandez – 55 Starlyn Caba – 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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