Down on the Farm: The Kansas City Royals – June 2025

Down on the Farm: The Kansas City Royals – June 2025

The Kansas City Royals Top 30 Prospects!

Rank Name Position Level Age Projections
1 Jac Caglianone N/A MLB 22 125.1 wRC+, 0.270/0.328/0.485, 28 HR, 2 SB, 72 Runs, 85 RBI
2 Blake Mitchell C A+ 20 105.3 wRC+, 0.219/0.330/0.394, 21 HR, 13 SB, 71 Runs, 67 RBI
3 Asbel Gonzalez CF AA 19 98.4 wRC+, 0.252/0.327/0.368, 11 HR, 55 SB, 66 Runs, 52 RBI
4 Carter Jensen C AA 21 104.5 wRC+, 0.236/0.330/0.389, 15 HR, 6 SB, 75 Runs, 62 RBI
5 Ramon Ramirez C A 20 98.8 wRC+, 0.227/0.304/0.406, 23 HR, 2 SB, 63 Runs, 74 RBI
6 Noah Cameron SP MLB 25 4.21 ERA, 159 IP, 51 BB, 136 K, 7.7 K/9, 1.18 WHIP
7 Callan Moss 1B A+ 21 97.3 wRC+, 0.232/0.325/0.366, 14 HR, 10 SB, 67 Runs, 57 RBI
8 John Rave CF MLB 27 89.1 wRC+, 0.227/0.294/0.381, 17 HR, 17 SB, 68 Runs, 60 RBI
9 Erick Torres CF A+ 20 91.1 wRC+, 0.248/0.310/0.363, 11 HR, 15 SB, 61 Runs, 53 RBI
10 Steven Cruz RP MLB 26 3.54 ERA, 35 IP, 15 BB, 38 K, 9.7 K/9, 1.18 WHIP
11 Derlin Figueroa 1B A 21 87.2 wRC+, 0.232/0.290/0.378, 16 HR, 5 SB, 61 Runs, 64 RBI
12 Yandel Ricardo 3B CPX 18 81.6 wRC+, 0.214/0.298/0.340, 13 HR, 29 SB, 68 Runs, 52 RBI
13 Peyton Wilson LF AAA 25 83.9 wRC+, 0.222/0.304/0.341, 10 HR, 17 SB, 68 Runs, 51 RBI
14 Spencer Nivens LF AA 23 83.2 wRC+, 0.211/0.292/0.357, 17 HR, 5 SB, 63 Runs, 61 RBI
15 Rudy Martin OF AAA 29 79.3 wRC+, 0.225/0.301/0.325, 9 HR, 43 SB, 60 Runs, 46 RBI
16 Darryl Collins CF A+ 22 85.6 wRC+, 0.229/0.316/0.328, 7 HR, 10 SB, 64 Runs, 46 RBI
17 Javier Vaz 2B AA 24 85.1 wRC+, 0.235/0.315/0.329, 7 HR, 14 SB, 62 Runs, 45 RBI
18 Chris Brito 1B AAA 25 86.7 wRC+, 0.219/0.324/0.321, 9 HR, 2 SB, 62 Runs, 47 RBI
19 Tyler Gentry RF MLB 26 81.6 wRC+, 0.217/0.294/0.347, 14 HR, 6 SB, 61 Runs, 57 RBI
20 River Town RF AA 25 84.0 wRC+, 0.218/0.320/0.316, 8 HR, 11 SB, 63 Runs, 45 RBI
21 David Shields SP A 18 4.23 ERA, 110 IP, 39 BB, 93 K, 7.6 K/9, 1.26 WHIP
22 Darison Garcia SS CPX 18 79.0 wRC+, 0.208/0.303/0.321, 11 HR, 25 SB, 59 Runs, 50 RBI
23 Eric Kennedy N/A A+ 24 81.6 wRC+, 0.209/0.307/0.326, 12 HR, 5 SB, 63 Runs, 50 RBI
24 Gavin Cross RF AA 24 74.4 wRC+, 0.208/0.266/0.360, 19 HR, 17 SB, 58 Runs, 64 RBI
25 Roni Cabrera RF A 19 72.3 wRC+, 0.201/0.267/0.349, 18 HR, 27 SB, 57 Runs, 64 RBI
26 Lizandro Rodriguez 2B A+ 22 77.9 wRC+, 0.220/0.288/0.340, 11 HR, 17 SB, 63 Runs, 53 RBI
27 Jose Cerice 1B CPX 20 80.4 wRC+, 0.237/0.285/0.356, 12 HR, 6 SB, 57 Runs, 57 RBI
28 Luca Tresh C AAA 25 78.2 wRC+, 0.218/0.277/0.358, 16 HR, 2 SB, 59 Runs, 62 RBI
29 Andrew Hoffmann SP AAA 24 4.42 ERA, 128 IP, 45 BB, 94 K, 6.6 K/9, 1.24 WHIP
30 Brian O’Keefe C MLB 31 74.3 wRC+, 0.205/0.266/0.359, 20 HR, 3 SB, 56 Runs, 66 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 Blake Mitchell Vinnie Pasquantino Jonathan India Bobby Witt Jr. Maikel Garcia Franmil Reyes Asbel Gonzalez Hunter Renfroe
Projected Backup Carter Jensen Callan Moss Nick Loftin Darison Garcia Yandel Ricardo MJ Melendez Erick Torres Drew Waters
Depth Salvador Perez Mark Canha Michael Massey Cam Devanney Jorge Hernandez Peyton Wilson John Rave River Town

Starting Pitchers

Projected #1 Cole Ragans
Projected #2 Kris Bubic
Projected #3 Seth Lugo
Projected #4 Michael Wacha
Projected #5 Alec Marsh
Depth Noah Cameron
Depth Michael Lorenzen
Depth Kyle Wright
Depth David Shields
Depth Andrew Hoffmann

Relief Pitchers

Projected Closer Hunter Harvey
Projected Setup #1 Lucas Erceg
Projected Setup #2 Angel Zerpa
Next Up Steven Cruz
Next Up John Schreiber
Next Up James McArthur
Depth Evan Sisk
Depth Carlos Estévez
Depth Eric Cerantola
Depth Ryan Hendrix

Top prospects by tool!

Hit Power Speed
Jac Caglianone – 60 Salvador Perez – 60 Asbel Gonzalez – 80
Asbel Gonzalez – 55 Jac Caglianone – 60 Rudy Martin – 70
Callan Moss – 50 Bobby Witt Jr. – 60 Colton Becker – 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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