Down on the Farm: The Colorado Rockies – August 2025

Down on the Farm: The Colorado Rockies – October 2025

The Colorado Rockies Top 30 Prospects!

Rank Name Position Level Age Projections
1 Roldy Brito 2B A 18 112.3 wRC+, 0.273/0.333/0.420, 14 HR, 36 SB, 76 Runs, 62 RBI
2 Wilder Dalis 3B A 18 108.6 wRC+, 0.254/0.327/0.413, 17 HR, 14 SB, 73 Runs, 65 RBI
3 Cristian Arguelles CF DSL 18 108.6 wRC+, 0.264/0.322/0.421, 17 HR, 5 SB, 70 Runs, 66 RBI
4 Robert Calaz RF A 19 101.1 wRC+, 0.244/0.302/0.421, 22 HR, 6 SB, 71 Runs, 71 RBI
5 Roc Riggio 2B AA 23 97.1 wRC+, 0.219/0.310/0.390, 20 HR, 17 SB, 68 Runs, 69 RBI
6 Jared Thomas LF AA 21 99.2 wRC+, 0.247/0.321/0.381, 15 HR, 23 SB, 65 Runs, 58 RBI
7 Yanquiel Fernandez RF MLB 22 100.1 wRC+, 0.252/0.297/0.424, 21 HR, 1 SB, 63 Runs, 75 RBI
8 Jeff Criswell SP MLB 25 3.86 ERA, 114 IP, 42 BB, 109 K, 8.6 K/9, 1.15 WHIP
9 Luis Morejon LF DSL 17 96.1 wRC+, 0.229/0.306/0.392, 20 HR, 8 SB, 65 Runs, 68 RBI
10 Blaine Crim 1B MLB 28 97.2 wRC+, 0.237/0.303/0.402, 20 HR, 3 SB, 62 Runs, 68 RBI
11 Kyle Karros 3B MLB 22 100.6 wRC+, 0.249/0.330/0.373, 11 HR, 3 SB, 67 Runs, 57 RBI
12 Sean Sullivan SP AA 22 4.24 ERA, 160 IP, 45 BB, 130 K, 7.3 K/9, 1.12 WHIP
13 Carlos Renzullo 2B CPX 18 96.3 wRC+, 0.243/0.323/0.365, 12 HR, 16 SB, 64 Runs, 55 RBI
14 Zac Veen LF MLB 23 91.3 wRC+, 0.234/0.306/0.370, 13 HR, 28 SB, 68 Runs, 58 RBI
15 Eriel Dihigo SS DSL 18 94.0 wRC+, 0.228/0.316/0.366, 15 HR, 13 SB, 63 Runs, 59 RBI
16 Cole Carrigg CF AA 23 85.6 wRC+, 0.225/0.287/0.376, 16 HR, 36 SB, 75 Runs, 58 RBI
17 Derek Bernard LF A 19 88.9 wRC+, 0.240/0.294/0.379, 14 HR, 16 SB, 63 Runs, 62 RBI
18 Ashly Andujar SS CPX 17 93.2 wRC+, 0.261/0.304/0.382, 13 HR, 8 SB, 56 Runs, 58 RBI
19 Carson Palmquist SP AAA 23 4.33 ERA, 145 IP, 62 BB, 134 K, 8.3 K/9, 1.20 WHIP
20 Welinton Herrera RP AA 21 3.59 ERA, 40 IP, 19 BB, 43 K, 9.8 K/9, 1.23 WHIP
21 Braxton Fulford C MLB 26 84.7 wRC+, 0.216/0.293/0.362, 16 HR, 7 SB, 64 Runs, 61 RBI
22 Sterlin Thompson LF AAA 24 85.8 wRC+, 0.227/0.298/0.359, 13 HR, 8 SB, 67 Runs, 56 RBI
23 Charlie Condon LF AA 22 86.8 wRC+, 0.221/0.306/0.351, 14 HR, 2 SB, 64 Runs, 55 RBI
24 Max Belyeu N/A A+ 21 81.4 wRC+, 0.211/0.289/0.354, 17 HR, 12 SB, 62 Runs, 59 RBI
25 Bryant Betancourt C AA 21 85.0 wRC+, 0.229/0.296/0.358, 15 HR, 5 SB, 57 Runs, 58 RBI
26 Jace Kaminska SP A 22 4.51 ERA, 160 IP, 36 BB, 119 K, 6.7 K/9, 1.15 WHIP
27 Griffin Herring SP A+ 22 4.52 ERA, 154 IP, 73 BB, 136 K, 7.9 K/9, 1.33 WHIP
28 Warming Bernabel 3B MLB 23 83.5 wRC+, 0.248/0.286/0.369, 13 HR, 7 SB, 54 Runs, 61 RBI
29 Juan Mejia RP MLB 24 3.69 ERA, 39 IP, 18 BB, 42 K, 9.7 K/9, 1.23 WHIP
30 Braiden Ward CF AAA 26 76.5 wRC+, 0.220/0.311/0.296, 4 HR, 52 SB, 66 Runs, 35 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 Bryant Betancourt Blaine Crim Roldy Brito Ezequiel Tovar Wilder Dalis Jordan Beck Cristian Arguelles Hunter Goodman
Projected Backup Braxton Fulford Michael Toglia Adael Amador Ryan Ritter Kyle Karros Jared Thomas Mickey Moniak Robert Calaz
Depth Drew Romo Ronny Ugarte Keston Hiura Eriel Dihigo Roynier Hernandez Sam Hilliard Tyler Freeman Yanquiel Fernandez

Starting Pitchers

Projected #1 Tyler Dollander
Projected #2 Jeff Criswell
Projected #3 Sean Sullivan
Projected #4 Carson Palmquist
Projected #5 Jace Kaminska
Depth Griffin Herring
Depth German Marquez
Depth Brody Brecht
Depth Ben Shields
Depth Kyle Freeland

Relief Pitchers

Projected Closer Welinton Herrera
Projected Setup #1 Victor Vodnik
Projected Setup #2 Juan Mejia
Next Up Seth Halvorsen
Next Up Jaden Hill
Next Up Riley Pint
Depth Angel Chivilli
Depth Jimmy Herget
Depth Matt Carasiti
Depth Jefry Yan

Top prospects by tool!

Hit Power Speed
Roldy Brito – 60 Luis Morejon – 55 Braiden Ward – 80
Cristian Arguelles – 55 Robert Calaz – 55 Dariel Garcia – 60
Ashly Andujar – 55 Trevor Boone – 55 Roldy Brito – 60

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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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