Down on the Farm: The Philadelphia Phillies – July 2025

Down on the Farm: The Philadelphia Phillies – October 2025

The Philadelphia Phillies Top 30 Prospects!

Rank Name Position Level Age Projections
1 Andrew Painter SP AAA 22 3.69 ERA, 140 IP, 45 BB, 132 K, 8.5 K/9, 1.13 WHIP
2 Eduardo Tait C A 18 116.3 wRC+, 0.266/0.315/0.467, 25 HR, 1 SB, 67 Runs, 85 RBI
3 Justin Crawford CF AAA 21 110.1 wRC+, 0.286/0.339/0.401, 8 HR, 38 SB, 73 Runs, 53 RBI
4 Aroon Escobar 2B A+ 20 110.2 wRC+, 0.258/0.328/0.419, 19 HR, 7 SB, 66 Runs, 68 RBI
5 Aidan Miller SS AA 21 101.0 wRC+, 0.234/0.328/0.379, 14 HR, 34 SB, 72 Runs, 59 RBI
6 Hendry Mendez LF AA 21 107.2 wRC+, 0.265/0.341/0.385, 10 HR, 3 SB, 69 Runs, 55 RBI
7 Otto Kemp 3B MLB 25 99.6 wRC+, 0.234/0.319/0.386, 16 HR, 13 SB, 73 Runs, 61 RBI
8 Anderson Araujo N/A DSL 17 89.7 wRC+, 0.221/0.282/0.402, 24 HR, 20 SB, 63 Runs, 74 RBI
9 Devin Saltiban 2B A+ 20 90.9 wRC+, 0.223/0.290/0.395, 22 HR, 16 SB, 62 Runs, 71 RBI
10 Carson DeMartini 3B AA 22 92.6 wRC+, 0.231/0.312/0.367, 14 HR, 25 SB, 67 Runs, 58 RBI
11 Mick Abel SP MLB 23 4.33 ERA, 158 IP, 78 BB, 148 K, 8.4 K/9, 1.32 WHIP
12 Gabriel Rincones Jr. RF AAA 24 90.5 wRC+, 0.218/0.305/0.369, 17 HR, 18 SB, 64 Runs, 62 RBI
13 Luke Davis C A+ 22 95.9 wRC+, 0.210/0.338/0.340, 14 HR, 3 SB, 67 Runs, 53 RBI
14 Josueth Quinonez CF DSL 18 93.0 wRC+, 0.246/0.304/0.380, 15 HR, 8 SB, 58 Runs, 62 RBI
15 Francisco Loreto C CPX 18 88.8 wRC+, 0.228/0.287/0.390, 20 HR, 3 SB, 63 Runs, 69 RBI
16 Marcus Lee Sang CF AAA 24 87.6 wRC+, 0.229/0.292/0.376, 16 HR, 13 SB, 67 Runs, 59 RBI
17 Keaton Anthony 1B AAA 24 93.9 wRC+, 0.257/0.315/0.368, 9 HR, 1 SB, 61 Runs, 55 RBI
18 Jose Rodriguez SS AAA 24 84.8 wRC+, 0.249/0.279/0.384, 15 HR, 26 SB, 61 Runs, 62 RBI
19 Yhoan Escalona C A 19 87.5 wRC+, 0.228/0.299/0.365, 15 HR, 9 SB, 64 Runs, 59 RBI
20 Raylin Heredia RF A+ 21 84.6 wRC+, 0.237/0.278/0.386, 16 HR, 7 SB, 68 Runs, 62 RBI
21 Carson Taylor 1B AAA 26 88.7 wRC+, 0.228/0.311/0.350, 13 HR, 1 SB, 62 Runs, 53 RBI
22 Manolfi Jimenez RF A 20 81.2 wRC+, 0.240/0.261/0.397, 18 HR, 9 SB, 65 Runs, 68 RBI
23 Alirio Ferrebus C A 19 83.4 wRC+, 0.234/0.279/0.379, 17 HR, 3 SB, 59 Runs, 65 RBI
24 Paul McIntosh C AAA 27 84.2 wRC+, 0.219/0.297/0.354, 15 HR, 2 SB, 61 Runs, 59 RBI
25 Dante Nori CF A 20 83.0 wRC+, 0.234/0.303/0.338, 8 HR, 17 SB, 64 Runs, 48 RBI
26 Matt Kroon LF AAA 28 78.9 wRC+, 0.225/0.285/0.349, 12 HR, 25 SB, 62 Runs, 55 RBI
27 Dylan Campbell RF AA 22 77.4 wRC+, 0.220/0.281/0.348, 14 HR, 29 SB, 59 Runs, 58 RBI
28 Kehden Hettiger C A+ 21 83.1 wRC+, 0.205/0.306/0.334, 14 HR, 2 SB, 62 Runs, 55 RBI
29 Payton Henry C MLB 28 82.7 wRC+, 0.230/0.288/0.361, 15 HR, 2 SB, 58 Runs, 58 RBI
30 Daniel Robert RP MLB 30 3.67 ERA, 41 IP, 15 BB, 43 K, 9.5 K/9, 1.12 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 Eduardo Tait Bryce Harper Aroon Escobar Trea Turner Alec Bohm Hendry Mendez Justin Crawford Nick Castellanos
Projected Backup J.T. Realmuto Keaton Anthony Bryson Stott Aidan Miller Otto Kemp Brandon Marsh Josueth Quinonez Max Kepler
Depth Luke Davis Carson Taylor Devin Saltiban Edmundo Sosa Carson DeMartini Weston Wilson Johan Rojas Gabriel Rincones Jr.

Starting Pitchers

Projected #1 Zack Wheeler
Projected #2 Cristopher Sánchez
Projected #3 Aaron Nola
Projected #4 Jesús Luzardo
Projected #5 Ranger Suárez
Depth Andrew Painter
Depth Mick Abel
Depth Robinson Pina
Depth Alan Rangel
Depth Wen-Hui Pan

Relief Pitchers

Projected Closer José Alvarado
Projected Setup #1 Orion Kerkering
Projected Setup #2 Matt Strahm
Next Up Tanner Banks
Next Up Daniel Robert
Next Up Michael Rucker
Depth Luis F. Ortiz
Depth Jordan Romano
Depth Max Lazar
Depth Joel Kuhnel

Top prospects by tool!

Hit Power Speed
Justin Crawford – 60 Eduardo Tait – 60 Justin Crawford – 70
Hendry Mendez – 60 Anderson Araujo – 55 Carson DeMartini – 60
Keaton Anthony – 55 Devin Saltiban – 55 Samuel Estevez – 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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