Posted inChicago White Sox

Down on the Farm: The Chicago White Sox – September 2025

Down on the Farm: The Chicago White Sox – September 2025

The Chicago White Sox Top 30 Prospects!

Rank Name Position Level Age Projections
1 Caleb Bonemer SS A+ 19 118.4 wRC+, 0.249/0.341/0.435, 21 HR, 16 SB, 76 Runs, 74 RBI
2 Jeral Perez 2B A+ 20 109.6 wRC+, 0.245/0.313/0.441, 24 HR, 2 SB, 69 Runs, 80 RBI
3 Sam Antonacci 2B AA 22 111.7 wRC+, 0.255/0.362/0.371, 8 HR, 27 SB, 77 Runs, 49 RBI
4 Grant Taylor RP MLB 23 3.23 ERA, 51 IP, 21 BB, 60 K, 10.4 K/9, 1.18 WHIP
5 Samuel Zavala CF A+ 20 108.0 wRC+, 0.237/0.339/0.392, 17 HR, 9 SB, 68 Runs, 64 RBI
6 William Bergolla 2B AA 20 106.2 wRC+, 0.289/0.332/0.394, 7 HR, 26 SB, 66 Runs, 50 RBI
7 Javier Mogollon SS A 19 97.1 wRC+, 0.217/0.307/0.394, 21 HR, 22 SB, 69 Runs, 70 RBI
8 Wilfred Veras RF AA 22 96.3 wRC+, 0.243/0.292/0.415, 21 HR, 14 SB, 63 Runs, 73 RBI
9 Alexander Albertus 3B A 20 100.6 wRC+, 0.239/0.334/0.367, 13 HR, 10 SB, 63 Runs, 57 RBI
10 Kyle Lodise SS A+ 21 95.3 wRC+, 0.230/0.308/0.385, 18 HR, 15 SB, 68 Runs, 64 RBI
11 Tim Elko 1B MLB 26 92.8 wRC+, 0.238/0.281/0.416, 24 HR, 1 SB, 62 Runs, 74 RBI
12 George Wolkow RF A 19 91.4 wRC+, 0.222/0.295/0.389, 21 HR, 13 SB, 64 Runs, 68 RBI
13 Christian Gonzalez CF DSL 18 95.1 wRC+, 0.242/0.319/0.366, 12 HR, 12 SB, 67 Runs, 54 RBI
14 Ely Brown OF A 20 94.1 wRC+, 0.245/0.318/0.363, 12 HR, 18 SB, 63 Runs, 54 RBI
15 Bryan Ramos 3B MLB 23 91.5 wRC+, 0.228/0.299/0.382, 18 HR, 10 SB, 62 Runs, 66 RBI
16 Drew Thorpe SP MLB 23 4.38 ERA, 166 IP, 55 BB, 128 K, 6.9 K/9, 1.20 WHIP
17 Braden Montgomery RF AA 22 89.8 wRC+, 0.229/0.297/0.377, 16 HR, 5 SB, 66 Runs, 64 RBI
18 Yordani Soto SS DSL 16 82.9 wRC+, 0.218/0.310/0.326, 10 HR, 41 SB, 60 Runs, 50 RBI
19 Ronny Hernandez C A 20 91.7 wRC+, 0.242/0.316/0.355, 11 HR, 1 SB, 58 Runs, 55 RBI
20 Gage Workman 3B MLB 25 81.2 wRC+, 0.209/0.282/0.365, 16 HR, 18 SB, 70 Runs, 63 RBI
21 Rikuu Nishida 2B AA 24 89.5 wRC+, 0.246/0.338/0.311, 3 HR, 25 SB, 66 Runs, 32 RBI
22 Ryan Galanie 1B AA 25 84.2 wRC+, 0.232/0.284/0.374, 16 HR, 4 SB, 65 Runs, 60 RBI
23 Noah Schultz SP AAA 21 4.35 ERA, 130 IP, 63 BB, 114 K, 7.9 K/9, 1.38 WHIP
24 Hagen Smith SP AA 21 4.20 ERA, 112 IP, 66 BB, 121 K, 9.7 K/9, 1.38 WHIP
25 Frank Mieses CF DSL 17 80.7 wRC+, 0.218/0.291/0.348, 14 HR, 8 SB, 63 Runs, 55 RBI
26 Adrian Gil 1B A 19 75.5 wRC+, 0.190/0.285/0.334, 16 HR, 22 SB, 64 Runs, 58 RBI
27 James Taussig OF A 22 79.3 wRC+, 0.226/0.287/0.347, 12 HR, 9 SB, 62 Runs, 54 RBI
28 Abraham Nunez CF A 19 75.5 wRC+, 0.225/0.280/0.342, 11 HR, 25 SB, 61 Runs, 52 RBI
29 DJ Gladney RF AA 23 74.3 wRC+, 0.215/0.258/0.373, 20 HR, 6 SB, 60 Runs, 67 RBI
30 Jefrank Silva 1B DSL 17 79.0 wRC+, 0.220/0.290/0.341, 13 HR, 5 SB, 58 Runs, 55 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 Edgar Quero Bobby Dalbec Sam Antonacci Caleb Bonemer Miguel Vargas Andrew Benintendi Luis Robert Jr. Wilfred Veras
Projected Backup Kyle Teel Tim Elko Jeral Perez Colson Montgomery Alexander Albertus Arnold Prado Samuel Zavala Will Robertson
Depth Ronny Hernandez Ryan Galanie Curtis Mead Chase Meidroth Lenyn Sosa #N/A Christian Gonzalez Mike Tauchman

Starting Pitchers

Projected #1 Drew Thorpe
Projected #2 Shane Smith
Projected #3 Aaron Civale
Projected #4 Davis Martin
Projected #5 Noah Schultz
Depth Hagen Smith
Depth Sean Burke
Depth Mike Vasil
Depth Jonathan Cannon
Depth Mason Adams

Relief Pitchers

Projected Closer Grant Taylor
Projected Setup #1 Prelander Berroa
Projected Setup #2 Bryan Hudson
Next Up Jordan Leasure
Next Up Fraser Ellard
Next Up Keone Kela
Depth Cam Booser
Depth Elvis Peguero
Depth Zach Franklin
Depth Miguel Castro

Top prospects by tool!

Hit Power Speed
Sam Antonacci – 60 Caleb Bonemer – 55 Yordani Soto – 70
William Bergolla – 60 George Wolkow – 55 Jordan Sprinkle – 70
Caleb Bonemer – 55 Javier Mogollon – 55 Greg Jones – 70

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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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