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

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

The Chicago White Sox Top 30 Prospects!

Rank Name Position Level Age Projections
1 Caleb Bonemer SS A 19 111.2 wRC+, 0.240/0.343/0.399, 16 HR, 21 SB, 76 Runs, 63 RBI
2 Javier Mogollon SS A 19 106.6 wRC+, 0.223/0.322/0.413, 23 HR, 25 SB, 73 Runs, 73 RBI
3 Jeral Perez 2B A+ 20 107.8 wRC+, 0.236/0.314/0.431, 24 HR, 2 SB, 70 Runs, 79 RBI
4 Edgar Quero C MLB 22 113.5 wRC+, 0.255/0.353/0.393, 14 HR, 2 SB, 67 Runs, 62 RBI
5 Kyle Teel C AAA 23 110.2 wRC+, 0.252/0.342/0.397, 16 HR, 8 SB, 68 Runs, 61 RBI
6 Wilfred Veras RF AA 22 103.0 wRC+, 0.251/0.302/0.429, 22 HR, 15 SB, 64 Runs, 76 RBI
7 William Bergolla 2B AA 20 106.3 wRC+, 0.285/0.333/0.393, 7 HR, 25 SB, 68 Runs, 51 RBI
8 Samuel Zavala CF A+ 20 103.6 wRC+, 0.226/0.332/0.383, 17 HR, 9 SB, 69 Runs, 64 RBI
9 Sam Antonacci 2B A+ 22 104.4 wRC+, 0.247/0.345/0.365, 9 HR, 16 SB, 73 Runs, 51 RBI
10 Alexander Albertus 3B A 20 103.4 wRC+, 0.244/0.335/0.377, 14 HR, 6 SB, 63 Runs, 59 RBI
11 Bryan Ramos 3B MLB 23 96.2 wRC+, 0.235/0.306/0.393, 19 HR, 8 SB, 63 Runs, 67 RBI
12 Tim Elko 1B MLB 26 94.2 wRC+, 0.243/0.283/0.419, 24 HR, 1 SB, 62 Runs, 73 RBI
13 Grant Taylor SP AA 23 3.70 ERA, 73 IP, 29 BB, 76 K, 9.3 K/9, 1.18 WHIP
14 Drew Thorpe SP MLB 23 4.38 ERA, 166 IP, 55 BB, 128 K, 6.9 K/9, 1.20 WHIP
15 George Wolkow RF A 19 88.9 wRC+, 0.220/0.291/0.384, 20 HR, 10 SB, 65 Runs, 66 RBI
16 Ryan Galanie 1B AA 25 90.9 wRC+, 0.237/0.294/0.388, 17 HR, 2 SB, 69 Runs, 63 RBI
17 Ronny Hernandez C A 20 95.0 wRC+, 0.248/0.321/0.362, 11 HR, 1 SB, 60 Runs, 55 RBI
18 Gage Workman N/A MLB 25 85.0 wRC+, 0.219/0.286/0.375, 17 HR, 21 SB, 70 Runs, 64 RBI
19 Adrian Gil 1B CPX 19 87.3 wRC+, 0.207/0.309/0.348, 15 HR, 14 SB, 68 Runs, 57 RBI
20 Hagen Smith SP AA 21 4.04 ERA, 108 IP, 57 BB, 117 K, 9.8 K/9, 1.33 WHIP
21 Braden Montgomery RF A+ 22 87.5 wRC+, 0.226/0.293/0.374, 17 HR, 7 SB, 60 Runs, 65 RBI
22 Colson Montgomery SS AAA 23 88.2 wRC+, 0.214/0.309/0.352, 15 HR, 4 SB, 64 Runs, 58 RBI
23 Noah Schultz SP AA 21 4.26 ERA, 126 IP, 57 BB, 110 K, 7.9 K/9, 1.34 WHIP
24 Miguel Santos 2B A+ 24 83.7 wRC+, 0.229/0.292/0.359, 15 HR, 6 SB, 60 Runs, 58 RBI
25 Andre Lipcius 1B MLB 27 83.6 wRC+, 0.227/0.295/0.353, 14 HR, 4 SB, 60 Runs, 56 RBI
26 Rikuu Nishida 2B AA 24 85.5 wRC+, 0.243/0.326/0.312, 3 HR, 24 SB, 64 Runs, 34 RBI
27 Tristan Gray 2B MLB 29 77.7 wRC+, 0.210/0.258/0.386, 23 HR, 2 SB, 61 Runs, 72 RBI
28 DJ Gladney RF AA 23 77.4 wRC+, 0.214/0.264/0.376, 21 HR, 6 SB, 61 Runs, 68 RBI
29 Abraham Nunez CF A 19 78.8 wRC+, 0.230/0.286/0.346, 11 HR, 24 SB, 63 Runs, 51 RBI
30 Matt Archer 2B CPX 23 81.6 wRC+, 0.231/0.293/0.348, 11 HR, 8 SB, 60 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 Edgar Quero Andrew Vaughn Jeral Perez Chase Meidroth Miguel Vargas Andrew Benintendi Luis Robert Jr. Wilfred Veras
Projected Backup Kyle Teel Tim Elko William Bergolla Caleb Bonemer Alexander Albertus Mark Payton Samuel Zavala Austin Slater
Depth Ronny Hernandez Joey Gallo Sam Antonacci Javier Mogollon Bryan Ramos Canaan Smith-Njigba Abraham Nunez Joshua Palacios

Starting Pitchers

Projected #1 Grant Taylor
Projected #2 Shane Smith
Projected #3 Drew Thorpe
Projected #4 Hagen Smith
Projected #5 Davis Martin
Depth Noah Schultz
Depth Jonathan Cannon
Depth Mike Vasil
Depth Adrian Houser
Depth Mason Adams

Relief Pitchers

Projected Closer Prelander Berroa
Projected Setup #1 Cam Booser
Projected Setup #2 Jordan Leasure
Next Up Fraser Ellard
Next Up Justin Anderson
Next Up Miguel Castro
Depth Tyler Gilbert
Depth Gus Varland
Depth Keone Kela
Depth Dominic Leone

Top prospects by tool!

Hit Power Speed
William Bergolla – 60 George Wolkow – 55 Greg Jones – 70
Sam Antonacci – 55 Javier Mogollon – 55 Jordan Sprinkle – 60
Kyle Teel – 55 Jeral Perez – 55 Terrell Tatum – 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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