Down on the Farm: The Pittsburgh Pirates – June 2025

Down on the Farm: The Pittsburgh Pirates – June 2025

The Pittsburgh Pirates Top 30 Prospects!

Rank Name Position Level Age Projections
1 Konnor Griffin SS A+ 19 115.4 wRC+, 0.273/0.325/0.447, 21 HR, 38 SB, 71 Runs, 73 RBI
2 Edward Florentino 1B A 18 115.9 wRC+, 0.254/0.323/0.453, 25 HR, 8 SB, 75 Runs, 78 RBI
3 Wyatt Sanford SS A 19 111.6 wRC+, 0.242/0.345/0.398, 17 HR, 41 SB, 72 Runs, 62 RBI
4 Termarr Johnson 2B AA 21 112.1 wRC+, 0.242/0.348/0.396, 17 HR, 12 SB, 69 Runs, 63 RBI
5 Esmerlyn Valdez 1B A+ 21 109.0 wRC+, 0.239/0.312/0.440, 26 HR, 1 SB, 68 Runs, 81 RBI
6 Nick Yorke 2B MLB 23 104.2 wRC+, 0.258/0.324/0.400, 15 HR, 15 SB, 67 Runs, 62 RBI
7 Bubba Chandler SP AAA 22 3.98 ERA, 136 IP, 60 BB, 133 K, 8.8 K/9, 1.26 WHIP
8 Axiel Plaz C A 19 98.7 wRC+, 0.228/0.303/0.409, 23 HR, 1 SB, 63 Runs, 75 RBI
9 Thomas Harrington SP AAA 22 4.24 ERA, 162 IP, 36 BB, 124 K, 6.9 K/9, 1.08 WHIP
10 Matt Gorski CF MLB 27 86.3 wRC+, 0.220/0.265/0.413, 25 HR, 17 SB, 65 Runs, 77 RBI
11 Keiner Delgado SS A+ 21 90.7 wRC+, 0.231/0.303/0.372, 16 HR, 20 SB, 60 Runs, 62 RBI
12 Omar Alfonzo C A+ 21 95.0 wRC+, 0.228/0.318/0.367, 16 HR, 1 SB, 61 Runs, 60 RBI
13 Duce Gourson 2B A+ 22 88.1 wRC+, 0.226/0.311/0.348, 11 HR, 33 SB, 63 Runs, 55 RBI
14 Braxton Ashcraft SP AAA 24 4.14 ERA, 128 IP, 31 BB, 106 K, 7.4 K/9, 1.14 WHIP
15 Hunter Barco SP AAA 24 4.06 ERA, 110 IP, 49 BB, 109 K, 8.9 K/9, 1.26 WHIP
16 Ronny Simon 2B MLB 25 85.5 wRC+, 0.233/0.293/0.366, 13 HR, 20 SB, 65 Runs, 57 RBI
17 Jack Brannigan SS AA 24 84.6 wRC+, 0.211/0.294/0.359, 18 HR, 11 SB, 64 Runs, 61 RBI
18 Will Taylor CF A+ 22 83.7 wRC+, 0.223/0.294/0.356, 14 HR, 18 SB, 61 Runs, 59 RBI
19 Tsung-Che Cheng SS MLB 23 84.3 wRC+, 0.226/0.305/0.341, 9 HR, 20 SB, 67 Runs, 50 RBI
20 Mike Burrows SP MLB 25 4.25 ERA, 128 IP, 46 BB, 119 K, 8.4 K/9, 1.18 WHIP
21 Yordany De Los Santos SS A 20 79.2 wRC+, 0.227/0.280/0.359, 14 HR, 32 SB, 60 Runs, 59 RBI
22 Kervin Pichardo 2B AAA 23 83.8 wRC+, 0.221/0.291/0.361, 16 HR, 4 SB, 62 Runs, 59 RBI
23 Jhonny Severino 3B A 20 76.5 wRC+, 0.215/0.251/0.393, 22 HR, 11 SB, 63 Runs, 74 RBI
24 Billy Cook RF MLB 26 79.4 wRC+, 0.219/0.282/0.356, 15 HR, 18 SB, 63 Runs, 58 RBI
25 Malcom Nunez 3B AAA 24 83.7 wRC+, 0.236/0.294/0.355, 13 HR, 2 SB, 56 Runs, 57 RBI
26 Kendrick Herrera 3B CPX 18 83.1 wRC+, 0.227/0.301/0.343, 12 HR, 3 SB, 61 Runs, 52 RBI
27 Mitch Jebb 2B AA 23 81.6 wRC+, 0.242/0.311/0.319, 4 HR, 26 SB, 64 Runs, 37 RBI
28 Nick Cimillo 1B AA 25 79.7 wRC+, 0.210/0.287/0.350, 16 HR, 1 SB, 59 Runs, 61 RBI
29 Michell Ojeda 3B CPX 18 76.6 wRC+, 0.212/0.281/0.345, 15 HR, 17 SB, 59 Runs, 57 RBI
30 Shalin Polanco CF A+ 21 74.3 wRC+, 0.208/0.265/0.360, 18 HR, 12 SB, 60 Runs, 65 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 Henry Davis Edward Florentino Termarr Johnson Oneil Cruz Jared Triolo Bryan Reynolds Jack Suwinski DJ Stewart
Projected Backup Joey Bart Esmerlyn Valdez Nick Yorke Konnor Griffin Ke’Bryan Hayes Alexander Canario Matt Gorski Tommy Pham
Depth Axiel Plaz Spencer Horwitz Nick Gonzales Wyatt Sanford Malcom Nunez Nick Solak Will Taylor Billy Cook

Starting Pitchers

Projected #1 Paul Skenes
Projected #2 Jared Jones
Projected #3 Mitch Keller
Projected #4 Bubba Chandler
Projected #5 Thomas Harrington
Depth Braxton Ashcraft
Depth Hunter Barco
Depth Andrew Heaney
Depth Mike Burrows
Depth Domingo German

Relief Pitchers

Projected Closer David Bednar
Projected Setup #1 Ben Heller
Projected Setup #2 Caleb Ferguson
Next Up Colin Holderman
Next Up Ryan Borucki
Next Up Yohan Ramírez
Depth Justin Lawrence
Depth Dennis Santana
Depth Tim Mayza
Depth Hunter Stratton

Top prospects by tool!

Hit Power Speed
Konnor Griffin – 60 Edward Florentino – 60 Wyatt Sanford – 70
Edward Florentino – 55 Esmerlyn Valdez – 60 Konnor Griffin – 70
Nick Yorke – 55 Matt Gorski – 60 Duce Gourson – 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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