Down on the Farm: The Los Angeles Dodgers – June 2025

Down on the Farm: The Los Angeles Dodgers – June 2025

The Los Angeles Dodgers Top 30 Prospects!

Rank Name Position Level Age Projections
1 Eduardo Quintero CF A 19 121.0 wRC+, 0.256/0.351/0.430, 19 HR, 38 SB, 80 Runs, 67 RBI
2 Josue De Paula RF A+ 20 118.5 wRC+, 0.253/0.361/0.402, 16 HR, 23 SB, 71 Runs, 61 RBI
3 Zyhir Hope LF A+ 20 115.9 wRC+, 0.250/0.337/0.430, 21 HR, 11 SB, 69 Runs, 72 RBI
4 Mike Sirota CF A+ 22 113.2 wRC+, 0.244/0.324/0.438, 24 HR, 4 SB, 75 Runs, 76 RBI
5 Dalton Rushing C MLB 24 117.7 wRC+, 0.237/0.346/0.423, 23 HR, 1 SB, 73 Runs, 72 RBI
6 Francisco Espinoza C CPX 18 113.7 wRC+, 0.267/0.340/0.416, 15 HR, 4 SB, 75 Runs, 62 RBI
7 Kyle Hurt SP MLB 26 3.67 ERA, 93 IP, 46 BB, 106 K, 10.3 K/9, 1.26 WHIP
8 Ching-Hsien Ko DH CPX 18 104.2 wRC+, 0.242/0.329/0.390, 16 HR, 3 SB, 67 Runs, 64 RBI
9 Alex Freeland SS AAA 23 99.9 wRC+, 0.228/0.329/0.372, 15 HR, 20 SB, 66 Runs, 61 RBI
10 River Ryan SP MLB 25 3.93 ERA, 119 IP, 47 BB, 111 K, 8.4 K/9, 1.24 WHIP
11 Joendry Vargas SS A 19 90.5 wRC+, 0.233/0.294/0.386, 18 HR, 34 SB, 65 Runs, 64 RBI
12 Ryan Ward RF AAA 27 90.9 wRC+, 0.222/0.279/0.413, 26 HR, 9 SB, 68 Runs, 76 RBI
13 Kendall George CF A+ 20 92.7 wRC+, 0.245/0.328/0.341, 8 HR, 42 SB, 65 Runs, 44 RBI
14 Hyeseong Kim 2B MLB 26 88.6 wRC+, 0.238/0.293/0.380, 15 HR, 34 SB, 66 Runs, 62 RBI
15 Chase Harlan 3B CPX 18 92.9 wRC+, 0.235/0.294/0.398, 20 HR, 4 SB, 61 Runs, 71 RBI
16 Sean Linan SP AAA 20 4.09 ERA, 124 IP, 54 BB, 116 K, 8.4 K/9, 1.25 WHIP
17 Yeiner Fernandez 2B AA 22 96.3 wRC+, 0.256/0.325/0.362, 9 HR, 2 SB, 60 Runs, 53 RBI
18 Victor Rodrigues C A 20 94.4 wRC+, 0.236/0.326/0.352, 12 HR, 2 SB, 61 Runs, 53 RBI
19 Jose Rodriguez SP AAA 23 3.85 ERA, 85 IP, 34 BB, 90 K, 9.5 K/9, 1.18 WHIP
20 Edgardo Henriquez RP MLB 23 3.53 ERA, 39 IP, 19 BB, 44 K, 10.1 K/9, 1.19 WHIP
21 Jaron Elkins LF A 20 80.0 wRC+, 0.215/0.285/0.353, 15 HR, 41 SB, 66 Runs, 58 RBI
22 Emil Morales SS CPX 18 85.1 wRC+, 0.221/0.284/0.378, 20 HR, 5 SB, 64 Runs, 65 RBI
23 Brendan Tunink RF CPX 19 83.1 wRC+, 0.211/0.293/0.354, 15 HR, 20 SB, 70 Runs, 58 RBI
24 Logan Wagner 1B A+ 21 86.5 wRC+, 0.208/0.301/0.357, 18 HR, 3 SB, 64 Runs, 61 RBI
25 Kole Myers CF AA 24 85.9 wRC+, 0.207/0.334/0.302, 8 HR, 26 SB, 67 Runs, 41 RBI
26 Nicolas Perez SS A 20 78.5 wRC+, 0.208/0.297/0.328, 12 HR, 44 SB, 62 Runs, 52 RBI
27 Chris Newell LF AA 24 79.5 wRC+, 0.187/0.285/0.352, 22 HR, 13 SB, 61 Runs, 65 RBI
28 Kellon Lindsey SS A 19 81.0 wRC+, 0.213/0.299/0.337, 13 HR, 18 SB, 60 Runs, 54 RBI
29 Hunter Feduccia C MLB 28 85.6 wRC+, 0.223/0.309/0.340, 11 HR, 1 SB, 65 Runs, 51 RBI
30 Elijah Hainline SS AAA 22 81.3 wRC+, 0.209/0.306/0.325, 12 HR, 19 SB, 61 Runs, 51 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 Will Smith Freddie Freeman Yeiner Fernandez Mookie Betts Max Muncy Teoscar Hernández Andy Pages Josue De Paula
Projected Backup Dalton Rushing Logan Wagner Hyeseong Kim Alex Freeland Chase Harlan Zyhir Hope Eduardo Quintero Ryan Ward
Depth Francisco Espinoza Griffin Lockwood-Powell Enrique Hernández Joendry Vargas Oswaldo Osorio Michael Conforto Mike Sirota Brendan Tunink

Starting Pitchers

Projected #1 Tyler Glasnow
Projected #2 Yoshinobu Yamamoto
Projected #3 Blake Snell
Projected #4 Gavin Stone
Projected #5 Dustin May
Depth Kyle Hurt
Depth Michael Grove
Depth River Ryan
Depth Clayton Kershaw
Depth Tony Gonsolin

Relief Pitchers

Projected Closer Tanner Scott
Projected Setup #1 Evan Phillips
Projected Setup #2 Brusdar Graterol
Next Up Alex Vesia
Next Up Blake Treinen
Next Up Edgardo Henriquez
Depth Joe Kelly
Depth Luis García
Depth Kirby Yates
Depth Daniel Hudson

Top prospects by tool!

Hit Power Speed
Francisco Espinoza – 60 Ryan Ward – 60 Nicolas Perez – 70
Josue De Paula – 60 Chase Harlan – 55 Jaron Elkins – 70
Zyhir Hope – 55 Mike Sirota – 55 Kendall George – 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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