Down on the Farm: The Tampa Bay Rays – July 2025

Down on the Farm: The Tampa Bay Rays – July 2025

The Tampa Bay Rays Top 30 Prospects!

Rank Name Position Level Age Projections
1 Carson Williams SS AAA 22 112.9 wRC+, 0.235/0.318/0.448, 25 HR, 21 SB, 82 Runs, 77 RBI
2 Xavier Isaac 1B AA 21 118.8 wRC+, 0.238/0.338/0.440, 25 HR, 5 SB, 76 Runs, 78 RBI
3 Theo Gillen CF A 19 114.2 wRC+, 0.235/0.357/0.390, 17 HR, 27 SB, 73 Runs, 60 RBI
4 Tre’ Morgan 1B AAA 22 112.2 wRC+, 0.266/0.351/0.390, 11 HR, 8 SB, 70 Runs, 56 RBI
5 Aidan Smith CF A+ 20 102.9 wRC+, 0.233/0.321/0.398, 19 HR, 28 SB, 70 Runs, 67 RBI
6 Alberth Palma N/A CPX 19 104.1 wRC+, 0.251/0.338/0.375, 11 HR, 28 SB, 75 Runs, 51 RBI
7 Bob Seymour 1B AAA 26 100.6 wRC+, 0.239/0.294/0.430, 26 HR, 1 SB, 66 Runs, 78 RBI
8 Dominic Keegan 1B AAA 24 103.0 wRC+, 0.243/0.322/0.397, 16 HR, 1 SB, 69 Runs, 64 RBI
9 Mason Montgomery SP MLB 25 3.70 ERA, 94 IP, 39 BB, 100 K, 9.6 K/9, 1.17 WHIP
10 Adrian Santana SS A+ 19 95.6 wRC+, 0.262/0.311/0.381, 10 HR, 42 SB, 63 Runs, 57 RBI
11 Joe Rock SP AAA 23 4.27 ERA, 168 IP, 43 BB, 123 K, 6.6 K/9, 1.20 WHIP
12 Brailer Guerrero RF A 19 96.1 wRC+, 0.231/0.311/0.384, 18 HR, 11 SB, 70 Runs, 62 RBI
13 Cooper Kinney 2B AA 22 94.7 wRC+, 0.248/0.292/0.408, 19 HR, 1 SB, 61 Runs, 72 RBI
14 Matthew Etzel LF AA 23 90.1 wRC+, 0.228/0.312/0.355, 12 HR, 32 SB, 69 Runs, 54 RBI
15 Nathan Flewelling N/A A 18 96.2 wRC+, 0.220/0.330/0.354, 14 HR, 2 SB, 67 Runs, 56 RBI
16 Colton Ledbetter RF AA 23 88.2 wRC+, 0.233/0.294/0.376, 15 HR, 26 SB, 68 Runs, 61 RBI
17 Angel Mateo RF A 20 90.0 wRC+, 0.240/0.293/0.385, 17 HR, 15 SB, 58 Runs, 67 RBI
18 Mac Horvath CF A+ 23 85.8 wRC+, 0.215/0.292/0.368, 19 HR, 28 SB, 61 Runs, 65 RBI
19 Yirer Garcia C A 19 92.1 wRC+, 0.234/0.321/0.350, 11 HR, 2 SB, 61 Runs, 53 RBI
20 Brayden Taylor 3B AA 23 83.6 wRC+, 0.202/0.290/0.361, 17 HR, 13 SB, 71 Runs, 62 RBI
21 Derek Datil N/A CPX 19 89.3 wRC+, 0.232/0.312/0.352, 12 HR, 8 SB, 61 Runs, 54 RBI
22 Tristan Peters RF AAA 25 87.1 wRC+, 0.233/0.308/0.349, 9 HR, 11 SB, 70 Runs, 51 RBI
23 Narciso Polanco 2B A 20 83.7 wRC+, 0.222/0.301/0.344, 12 HR, 24 SB, 62 Runs, 54 RBI
24 Ty Johnson SP AA 23 4.08 ERA, 106 IP, 48 BB, 112 K, 9.5 K/9, 1.23 WHIP
25 Will Simpson 1B AA 23 85.0 wRC+, 0.225/0.289/0.370, 16 HR, 1 SB, 61 Runs, 64 RBI
26 Ian Seymour SP AAA 25 4.45 ERA, 152 IP, 53 BB, 128 K, 7.6 K/9, 1.19 WHIP
27 Homer Bush Jr. CF AA 23 81.8 wRC+, 0.240/0.307/0.326, 6 HR, 38 SB, 59 Runs, 43 RBI
28 Ian Seymour SP MLB 26 4.50 ERA, 157 IP, 51 BB, 137 K, 7.8 K/9, 1.17 WHIP
29 Yoniel Curet SP AA 22 4.36 ERA, 131 IP, 77 BB, 127 K, 8.7 K/9, 1.35 WHIP
30 Gregory Barrios SS AA 21 81.2 wRC+, 0.252/0.293/0.346, 6 HR, 31 SB, 56 Runs, 50 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 Danny Jansen Yandy Díaz Brandon Lowe Carson Williams Junior Caminero Matthew Etzel Theo Gillen Josh Lowe
Projected Backup Yirer Garcia Jonathan Aranda Curtis Mead Ha-Seong Kim Christopher Morel Kameron Misner Aidan Smith Brailer Guerrero
Depth Matt Thaiss Xavier Isaac Cooper Kinney Adrian Santana Brayden Taylor Ryan Cermak Mac Horvath Jonny DeLuca

Starting Pitchers

Projected #1 Drew Rasmussen
Projected #2 Ryan Pepiot
Projected #3 Shane Baz
Projected #4 Taj Bradley
Projected #5 Joe Boyle
Depth Mason Montgomery
Depth Joe Rock
Depth Ty Johnson
Depth Kyle Gibson
Depth Zack Littell

Relief Pitchers

Projected Closer Pete Fairbanks
Projected Setup #1 Kevin Kelly
Projected Setup #2 Manuel Rodríguez
Next Up Garrett Cleavinger
Next Up Forrest Whitley
Next Up Eric Orze
Depth Mason Englert
Depth Jacob Waguespack
Depth Edwin Uceta
Depth Paul Gervase

Top prospects by tool!

Hit Power Speed
Tre’ Morgan – 60 Bob Seymour – 60 Adrian Santana – 70
Alberth Palma – 55 Carson Williams – 60 Homer Bush Jr. – 70
Adrian Santana – 55 Xavier Isaac – 55 Theo Gillen – 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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