Down on the Farm: The Chicago Cubs – July 2025

Down on the Farm: The Chicago Cubs – October 2025

The Chicago Cubs Top 30 Prospects!

Rank Name Position Level Age Projections
1 Owen Caissie RF AAA 22 124.4 wRC+, 0.257/0.346/0.454, 23 HR, 6 SB, 75 Runs, 78 RBI
2 Moises Ballesteros C MLB 21 126.2 wRC+, 0.284/0.348/0.459, 20 HR, 1 SB, 68 Runs, 77 RBI
3 Jonathon Long 1B AAA 23 116.1 wRC+, 0.259/0.340/0.425, 20 HR, 1 SB, 70 Runs, 69 RBI
4 Kevin Alcantara RF MLB 22 110.1 wRC+, 0.257/0.321/0.431, 20 HR, 11 SB, 69 Runs, 72 RBI
5 Jefferson Rojas SS AA 20 109.4 wRC+, 0.259/0.323/0.423, 19 HR, 16 SB, 71 Runs, 68 RBI
6 Pedro Ramirez 3B AA 21 106.7 wRC+, 0.269/0.326/0.407, 13 HR, 16 SB, 73 Runs, 59 RBI
7 Christian Franklin RF AAA 25 100.1 wRC+, 0.232/0.338/0.359, 12 HR, 16 SB, 72 Runs, 51 RBI
8 James Triantos 2B AAA 22 94.8 wRC+, 0.262/0.309/0.382, 10 HR, 32 SB, 65 Runs, 56 RBI
9 Cameron Sisneros 1B A+ 24 97.5 wRC+, 0.240/0.317/0.380, 16 HR, 4 SB, 63 Runs, 62 RBI
10 Jose Escobar 2B A 20 95.9 wRC+, 0.234/0.315/0.376, 16 HR, 5 SB, 66 Runs, 60 RBI
11 Cristian Hernandez SS A+ 21 89.8 wRC+, 0.240/0.301/0.371, 13 HR, 32 SB, 62 Runs, 58 RBI
12 Juan Cabada N/A DSL 17 86.6 wRC+, 0.219/0.299/0.361, 16 HR, 29 SB, 61 Runs, 62 RBI
13 Ty Southisene SS A 19 89.4 wRC+, 0.224/0.323/0.334, 11 HR, 27 SB, 62 Runs, 50 RBI
14 Luis Leon N/A DSL 17 84.1 wRC+, 0.221/0.282/0.377, 20 HR, 24 SB, 60 Runs, 68 RBI
15 Carter Trice 1B A+ 22 83.5 wRC+, 0.199/0.290/0.362, 20 HR, 18 SB, 64 Runs, 66 RBI
16 Felix Stevens RF AA 25 83.7 wRC+, 0.204/0.277/0.384, 24 HR, 1 SB, 62 Runs, 73 RBI
17 Edgardo De Leon 3B CPX 18 82.2 wRC+, 0.215/0.278/0.375, 19 HR, 10 SB, 67 Runs, 64 RBI
18 Ronny Cruz SS CPX 18 80.7 wRC+, 0.230/0.274/0.375, 15 HR, 23 SB, 68 Runs, 62 RBI
19 Freiker Betencourt N/A DSL 16 84.7 wRC+, 0.224/0.303/0.345, 11 HR, 18 SB, 67 Runs, 52 RBI
20 BJ Murray Jr. 3B AAA 25 85.8 wRC+, 0.215/0.306/0.346, 14 HR, 9 SB, 62 Runs, 57 RBI
21 Derik Alcantara RF A 20 85.1 wRC+, 0.236/0.296/0.359, 11 HR, 14 SB, 67 Runs, 53 RBI
22 Leonel Vivas N/A DSL 18 83.7 wRC+, 0.196/0.315/0.323, 13 HR, 19 SB, 64 Runs, 53 RBI
23 Angel Cepeda SS A 19 80.4 wRC+, 0.215/0.289/0.350, 15 HR, 20 SB, 65 Runs, 56 RBI
24 Miguel Useche N/A A+ 24 81.2 wRC+, 0.222/0.294/0.344, 13 HR, 22 SB, 61 Runs, 54 RBI
25 Chase Strumpf 1B AAA 27 82.8 wRC+, 0.197/0.300/0.343, 17 HR, 2 SB, 63 Runs, 60 RBI
26 Michael Carico C A 22 83.3 wRC+, 0.211/0.302/0.340, 14 HR, 6 SB, 62 Runs, 55 RBI
27 Cole Mathis 1B A 21 80.8 wRC+, 0.212/0.288/0.353, 16 HR, 4 SB, 62 Runs, 61 RBI
28 Jaxon Wiggins SP AA 23 4.31 ERA, 121 IP, 66 BB, 117 K, 8.7 K/9, 1.32 WHIP
29 Ryan Gallagher SP A+ 22 4.59 ERA, 164 IP, 51 BB, 138 K, 7.6 K/9, 1.14 WHIP
30 Nico Zeglin SP A+ 24 3.97 ERA, 77 IP, 28 BB, 73 K, 8.5 K/9, 1.20 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 Moises Ballesteros Michael Busch Nico Hoerner Jefferson Rojas Matt Shaw Ian Happ Pete Crow-Armstrong Kyle Tucker
Projected Backup Carson Kelly Jonathon Long Jose Escobar Dansby Swanson Pedro Ramirez Leonel Espinoza Brett Bateman Seiya Suzuki
Depth Miguel Amaya Cameron Sisneros James Triantos Cristian Hernandez BJ Murray Jr. Andy Garriola Greg Allen Owen Caissie

Starting Pitchers

Projected #1 Justin Steele
Projected #2 Matthew Boyd
Projected #3 Ben Brown
Projected #4 Cade Horton
Projected #5 Shota Imanaga
Depth Thomas Pannone
Depth Jordan Wicks
Depth Jameson Taillon
Depth Colin Rea
Depth Javier Assad

Relief Pitchers

Projected Closer Daniel Palencia
Projected Setup #1 Yency Almonte
Projected Setup #2 Ryan Brasier
Next Up Caleb Thielbar
Next Up Gavin Hollowell
Next Up Luke Little
Depth Adbert Alzolay
Depth Ryan Pressly
Depth Keegan Thompson
Depth Jack Neely

Top prospects by tool!

Hit Power Speed
Moises Ballesteros – 70 Ivan Cespedes – 55 Luis Santos – 60
Pedro Ramirez – 60 Jonathon Long – 55 Juan Cabada – 60
Jonathon Long – 55 Andy Garriola – 55 Jan Luis Reyes – 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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