Down on the Farm: The Chicago Cubs – October 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 MLB 22 127.7 wRC+, 0.262/0.349/0.465, 24 HR, 6 SB, 75 Runs, 80 RBI
2 Moises Ballesteros C MLB 21 128.1 wRC+, 0.285/0.352/0.459, 20 HR, 1 SB, 70 Runs, 76 RBI
3 Kane Kepley OF A 21 112.0 wRC+, 0.247/0.352/0.389, 14 HR, 26 SB, 77 Runs, 56 RBI
4 Jonathon Long 1B AAA 23 116.8 wRC+, 0.259/0.347/0.417, 18 HR, 1 SB, 70 Runs, 65 RBI
5 Kevin Alcantara RF MLB 22 107.3 wRC+, 0.253/0.318/0.422, 20 HR, 10 SB, 68 Runs, 71 RBI
6 Jefferson Rojas SS AA 20 104.1 wRC+, 0.249/0.321/0.403, 17 HR, 14 SB, 69 Runs, 65 RBI
7 Pedro Ramirez 3B AA 21 104.7 wRC+, 0.267/0.324/0.400, 12 HR, 15 SB, 71 Runs, 58 RBI
8 Ty Southisene SS A 19 101.8 wRC+, 0.245/0.344/0.356, 10 HR, 29 SB, 67 Runs, 50 RBI
9 James Triantos 2B AAA 22 93.7 wRC+, 0.260/0.308/0.378, 10 HR, 31 SB, 65 Runs, 56 RBI
10 Cristian Hernandez SS A+ 21 88.4 wRC+, 0.236/0.301/0.366, 13 HR, 33 SB, 61 Runs, 58 RBI
11 Jose Escobar 2B A 20 92.8 wRC+, 0.239/0.308/0.374, 15 HR, 4 SB, 62 Runs, 60 RBI
12 Angel Cepeda SS A 19 87.2 wRC+, 0.230/0.291/0.376, 16 HR, 18 SB, 67 Runs, 60 RBI
13 Freiker Betencourt OF DSL 16 88.6 wRC+, 0.223/0.307/0.357, 13 HR, 19 SB, 69 Runs, 56 RBI
14 BJ Murray Jr. 3B AAA 25 89.2 wRC+, 0.216/0.311/0.354, 15 HR, 11 SB, 63 Runs, 59 RBI
15 Carter Trice 1B AA 22 84.5 wRC+, 0.194/0.293/0.360, 20 HR, 15 SB, 64 Runs, 66 RBI
16 Derik Alcantara RF A 20 88.8 wRC+, 0.233/0.305/0.361, 12 HR, 12 SB, 69 Runs, 54 RBI
17 Juan Cabada SS DSL 17 84.0 wRC+, 0.218/0.297/0.352, 15 HR, 25 SB, 59 Runs, 59 RBI
18 Felix Stevens RF AA 25 84.1 wRC+, 0.206/0.279/0.382, 23 HR, 2 SB, 62 Runs, 72 RBI
19 Edgardo De Leon 3B CPX 18 83.6 wRC+, 0.218/0.281/0.376, 19 HR, 10 SB, 68 Runs, 63 RBI
20 Leonel Vivas 1B DSL 18 83.8 wRC+, 0.198/0.306/0.337, 16 HR, 20 SB, 64 Runs, 56 RBI
21 Cameron Sisneros 1B A+ 24 88.4 wRC+, 0.223/0.311/0.350, 14 HR, 4 SB, 61 Runs, 56 RBI
22 Jaxon Wiggins SP AAA 23 4.17 ERA, 117 IP, 62 BB, 115 K, 8.9 K/9, 1.30 WHIP
23 Luis Leon CF DSL 17 78.3 wRC+, 0.220/0.271/0.368, 18 HR, 23 SB, 56 Runs, 66 RBI
24 Ezequiel Pena 2B DSL 18 82.1 wRC+, 0.229/0.300/0.339, 11 HR, 21 SB, 58 Runs, 52 RBI
25 Michael Carico C A 22 82.6 wRC+, 0.211/0.301/0.340, 14 HR, 7 SB, 62 Runs, 55 RBI
26 Chase Strumpf 1B AAA 27 80.9 wRC+, 0.193/0.299/0.335, 16 HR, 5 SB, 63 Runs, 58 RBI
27 Cole Mathis 1B A 21 80.9 wRC+, 0.211/0.288/0.352, 16 HR, 4 SB, 62 Runs, 61 RBI
28 Nico Zeglin SP A+ 24 3.97 ERA, 77 IP, 28 BB, 73 K, 8.5 K/9, 1.20 WHIP
29 Jeury Ramirez 1B DSL 17 78.4 wRC+, 0.200/0.289/0.340, 15 HR, 9 SB, 67 Runs, 56 RBI
30 Brett Bateman CF AA 23 84.4 wRC+, 0.234/0.328/0.305, 5 HR, 17 SB, 59 Runs, 37 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 Moises Ballesteros Michael Busch Nico Hoerner Jefferson Rojas Matt Shaw Ian Happ Pete Crow-Armstrong Kyle Tucker
Projected Backup Carson Kelly Jonathon Long James Triantos Ty Southisene Pedro Ramirez Leonel Espinoza Brett Bateman Owen Caissie
Depth Miguel Amaya Carlos Santana Jose Escobar Dansby Swanson BJ Murray Jr. Andy Garriola Luis Leon Seiya Suzuki

Starting Pitchers

Projected #1 Justin Steele
Projected #2 Ben Brown
Projected #3 Cade Horton
Projected #4 Matthew Boyd
Projected #5 Shota Imanaga
Depth Michael Soroka
Depth Colin Rea
Depth Jameson Taillon
Depth Brad Keller
Depth Aaron Civale

Relief Pitchers

Projected Closer Daniel Palencia
Projected Setup #1 Ben Heller
Projected Setup #2 Andrew Kittredge
Next Up Drew Pomeranz
Next Up Porter Hodge
Next Up Caleb Thielbar
Depth Ryan Borucki
Depth Keegan Thompson
Depth Luke Little
Depth Taylor Rogers

Top prospects by tool!

Hit Power Speed
Moises Ballesteros – 70 Ivan Cespedes – 55 Luis Santos – 60
Jonathon Long – 60 Carter Trice – 55 Juan Cabada – 60
Pedro Ramirez – 60 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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