Down on the Farm: The Philadelphia Phillies – June 2025

Down on the Farm: The Philadelphia Phillies – June 2025

The Philadelphia Phillies Top 30 Prospects!

Rank Name Position Level Age Projections
1 Eduardo Tait C A 18 117.4 wRC+, 0.269/0.317/0.469, 25 HR, 1 SB, 68 Runs, 85 RBI
2 Justin Crawford CF AAA 21 112.0 wRC+, 0.291/0.338/0.411, 9 HR, 39 SB, 74 Runs, 54 RBI
3 Aroon Escobar 2B A 20 115.5 wRC+, 0.262/0.338/0.427, 21 HR, 3 SB, 66 Runs, 70 RBI
4 Otto Kemp 3B MLB 25 103.5 wRC+, 0.238/0.323/0.398, 17 HR, 14 SB, 74 Runs, 62 RBI
5 Aidan Miller SS AA 21 98.6 wRC+, 0.236/0.321/0.378, 14 HR, 30 SB, 69 Runs, 60 RBI
6 Carson DeMartini 3B AA 22 97.9 wRC+, 0.237/0.318/0.380, 16 HR, 26 SB, 68 Runs, 60 RBI
7 Andrew Painter SP AAA 22 3.90 ERA, 125 IP, 43 BB, 119 K, 8.6 K/9, 1.16 WHIP
8 Devin Saltiban 2B A+ 20 95.0 wRC+, 0.229/0.299/0.398, 21 HR, 20 SB, 64 Runs, 70 RBI
9 Hendry Mendez LF AA 21 105.3 wRC+, 0.262/0.340/0.378, 9 HR, 2 SB, 67 Runs, 54 RBI
10 Alirio Ferrebus C A 19 96.5 wRC+, 0.241/0.298/0.406, 20 HR, 5 SB, 65 Runs, 70 RBI
11 Keaton Anthony 1B AAA 24 96.5 wRC+, 0.260/0.317/0.375, 10 HR, 1 SB, 62 Runs, 56 RBI
12 Jose Rodriguez SS AAA 24 86.0 wRC+, 0.250/0.281/0.387, 15 HR, 26 SB, 62 Runs, 62 RBI
13 Marcus Lee Sang CF AAA 24 87.4 wRC+, 0.229/0.292/0.375, 16 HR, 14 SB, 67 Runs, 59 RBI
14 Buddy Kennedy 2B MLB 26 91.6 wRC+, 0.231/0.321/0.346, 10 HR, 4 SB, 68 Runs, 51 RBI
15 Mick Abel SP AAA 22 4.43 ERA, 156 IP, 75 BB, 133 K, 7.7 K/9, 1.34 WHIP
16 Luke Davis C A+ 22 90.2 wRC+, 0.210/0.331/0.325, 11 HR, 3 SB, 65 Runs, 49 RBI
17 Gabriel Rincones Jr. RF AAA 24 83.7 wRC+, 0.212/0.297/0.351, 15 HR, 17 SB, 62 Runs, 60 RBI
18 Nikau Pouaka-Grego 3B A 20 87.8 wRC+, 0.223/0.309/0.350, 12 HR, 3 SB, 63 Runs, 56 RBI
19 Raylin Heredia RF A+ 21 81.2 wRC+, 0.227/0.273/0.379, 17 HR, 9 SB, 69 Runs, 62 RBI
20 Carson Taylor 1B AAA 26 86.9 wRC+, 0.227/0.310/0.345, 13 HR, 1 SB, 62 Runs, 52 RBI
21 Kehden Hettiger C A+ 21 84.8 wRC+, 0.203/0.309/0.337, 15 HR, 2 SB, 64 Runs, 56 RBI
22 Francisco Loreto C CPX 18 82.9 wRC+, 0.225/0.284/0.369, 16 HR, 4 SB, 62 Runs, 63 RBI
23 Nolan Beltran SS A 20 83.8 wRC+, 0.226/0.290/0.362, 16 HR, 4 SB, 59 Runs, 61 RBI
24 Paul McIntosh C AAA 27 82.2 wRC+, 0.214/0.293/0.350, 16 HR, 3 SB, 61 Runs, 59 RBI
25 Dante Nori CF A 20 82.5 wRC+, 0.232/0.304/0.335, 8 HR, 17 SB, 65 Runs, 47 RBI
26 Trent Farquhar 2B AA 24 82.8 wRC+, 0.220/0.325/0.302, 5 HR, 25 SB, 61 Runs, 40 RBI
27 Manolfi Jimenez RF CPX 20 78.4 wRC+, 0.233/0.261/0.385, 18 HR, 5 SB, 63 Runs, 66 RBI
28 Griffin Burkholder RF A 19 79.0 wRC+, 0.212/0.282/0.354, 15 HR, 5 SB, 64 Runs, 60 RBI
29 Dylan Campbell RF A+ 22 75.8 wRC+, 0.218/0.278/0.347, 14 HR, 26 SB, 59 Runs, 58 RBI
30 Bryan Rincon SS A+ 21 76.6 wRC+, 0.199/0.296/0.321, 12 HR, 27 SB, 58 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 Eduardo Tait Bryce Harper Aroon Escobar Trea Turner Alec Bohm Brandon Marsh Justin Crawford Nick Castellanos
Projected Backup J.T. Realmuto Keaton Anthony Bryson Stott Aidan Miller Otto Kemp Hendry Mendez Marcus Lee Sang Max Kepler
Depth Alirio Ferrebus 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 Jesús Luzardo
Projected #4 Aaron Nola
Projected #5 Ranger Suárez
Depth Andrew Painter
Depth Mick Abel
Depth Taijuan Walker
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 Joel Kuhnel
Depth Yunior Marte

Top prospects by tool!

Hit Power Speed
Justin Crawford – 70 Eduardo Tait – 60 Justin Crawford – 70
Eduardo Tait – 60 Devin Saltiban – 55 Carson DeMartini – 60
Aroon Escobar – 60 Alirio Ferrebus – 55 Dylan Campbell – 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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