Down on the Farm: The Chicago Cubs – August 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 22 122.3 wRC+, 0.257/0.344/0.446, 22 HR, 5 SB, 74 Runs, 77 RBI
2 Moises Ballesteros C MLB 21 126.3 wRC+, 0.284/0.347/0.460, 20 HR, 1 SB, 68 Runs, 77 RBI
3 Jonathon Long 1B AAA 23 118.2 wRC+, 0.260/0.346/0.425, 20 HR, 1 SB, 71 Runs, 67 RBI
4 Kevin Alcantara RF MLB 22 110.6 wRC+, 0.254/0.322/0.431, 21 HR, 9 SB, 69 Runs, 73 RBI
5 Jefferson Rojas SS AA 20 106.7 wRC+, 0.252/0.323/0.411, 17 HR, 15 SB, 70 Runs, 67 RBI
6 Pedro Ramirez 3B AA 21 102.7 wRC+, 0.263/0.322/0.395, 12 HR, 16 SB, 71 Runs, 57 RBI
7 Ty Southisene SS A 19 101.3 wRC+, 0.244/0.344/0.355, 10 HR, 27 SB, 64 Runs, 51 RBI
8 James Triantos 2B AAA 22 94.5 wRC+, 0.262/0.310/0.379, 10 HR, 32 SB, 64 Runs, 56 RBI
9 Cristian Hernandez SS A+ 21 91.3 wRC+, 0.240/0.305/0.372, 13 HR, 32 SB, 62 Runs, 58 RBI
10 Kane Kepley N/A A 21 90.5 wRC+, 0.229/0.316/0.350, 12 HR, 23 SB, 69 Runs, 51 RBI
11 Juan Cabada SS DSL 17 86.6 wRC+, 0.219/0.299/0.361, 16 HR, 29 SB, 61 Runs, 62 RBI
12 Luis Leon CF DSL 17 84.1 wRC+, 0.221/0.282/0.377, 20 HR, 24 SB, 60 Runs, 68 RBI
13 BJ Murray Jr. 3B AAA 25 89.3 wRC+, 0.217/0.309/0.356, 16 HR, 10 SB, 63 Runs, 60 RBI
14 Carter Trice 1B AA 22 84.1 wRC+, 0.195/0.292/0.362, 21 HR, 17 SB, 64 Runs, 67 RBI
15 Derik Alcantara RF A 20 88.7 wRC+, 0.237/0.299/0.370, 13 HR, 13 SB, 70 Runs, 56 RBI
16 Cameron Sisneros 1B A+ 24 91.0 wRC+, 0.228/0.314/0.357, 14 HR, 5 SB, 61 Runs, 57 RBI
17 Jose Escobar 2B A 20 89.4 wRC+, 0.230/0.304/0.365, 15 HR, 5 SB, 62 Runs, 59 RBI
18 Felix Stevens RF AA 25 84.2 wRC+, 0.206/0.279/0.383, 23 HR, 1 SB, 62 Runs, 72 RBI
19 Edgardo De Leon 3B CPX 18 83.8 wRC+, 0.218/0.281/0.377, 19 HR, 10 SB, 69 Runs, 63 RBI
20 Angel Cepeda SS A 19 83.0 wRC+, 0.220/0.290/0.359, 15 HR, 19 SB, 66 Runs, 57 RBI
21 Freiker Betencourt OF DSL 16 84.7 wRC+, 0.224/0.303/0.345, 11 HR, 18 SB, 67 Runs, 52 RBI
22 Leonel Vivas 1B DSL 18 83.7 wRC+, 0.196/0.315/0.323, 13 HR, 19 SB, 64 Runs, 53 RBI
23 Michael Carico C A 22 83.1 wRC+, 0.211/0.302/0.340, 14 HR, 7 SB, 62 Runs, 55 RBI
24 Brett Bateman CF AA 23 86.3 wRC+, 0.235/0.331/0.307, 5 HR, 18 SB, 60 Runs, 37 RBI
25 Jaxon Wiggins SP AA 23 4.29 ERA, 122 IP, 65 BB, 118 K, 8.7 K/9, 1.31 WHIP
26 Cole Mathis 1B A 21 81.1 wRC+, 0.212/0.288/0.354, 16 HR, 4 SB, 62 Runs, 61 RBI
27 Luke Little RP MLB 24 3.77 ERA, 44 IP, 25 BB, 47 K, 9.7 K/9, 1.36 WHIP
28 Chase Strumpf 1B AAA 27 81.1 wRC+, 0.195/0.298/0.338, 16 HR, 3 SB, 63 Runs, 58 RBI
29 Kade Snell N/A A+ 22 77.4 wRC+, 0.219/0.289/0.336, 11 HR, 29 SB, 62 Runs, 52 RBI
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 Jonathon Long Nico Hoerner Jefferson Rojas Matt Shaw Ian Happ Pete Crow-Armstrong Kyle Tucker
Projected Backup Carson Kelly Michael Busch James Triantos Ty Southisene Pedro Ramirez Leonel Espinoza Brett Bateman Seiya Suzuki
Depth Miguel Amaya Cameron Sisneros Jose Escobar Willi Castro BJ Murray Jr. Andy Garriola Luis Leon 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 Michael Soroka
Depth Jordan Wicks
Depth Thomas Pannone
Depth Jameson Taillon
Depth Colin Rea

Relief Pitchers

Projected Closer Daniel Palencia
Projected Setup #1 Caleb Thielbar
Projected Setup #2 Luke Little
Next Up Andrew Kittredge
Next Up Yency Almonte
Next Up Ryan Brasier
Depth Taylor Rogers
Depth Gavin Hollowell
Depth Adbert Alzolay
Depth Porter Hodge

Top prospects by tool!

Hit Power Speed
Jonathon Long – 60 Ivan Cespedes – 55 Luis Santos – 60
Moises Ballesteros – 60 Carter Trice – 55 Juan Cabada – 60
Jefferson Rojas – 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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