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

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

The Los Angeles Dodgers Top 30 Prospects!

Rank Name Position Level Age Projections
1 Eduardo Quintero CF A+ 19 121.7 wRC+, 0.254/0.352/0.431, 20 HR, 33 SB, 80 Runs, 67 RBI
2 Edgardo Henriquez RP MLB 23 2.97 ERA, 37 IP, 16 BB, 44 K, 10.8 K/9, 1.10 WHIP
3 Zyhir Hope LF AA 20 115.0 wRC+, 0.248/0.337/0.427, 21 HR, 15 SB, 70 Runs, 72 RBI
4 Josue De Paula RF AA 20 114.9 wRC+, 0.248/0.356/0.394, 16 HR, 23 SB, 70 Runs, 61 RBI
5 Mike Sirota CF A+ 22 112.8 wRC+, 0.240/0.333/0.423, 22 HR, 4 SB, 75 Runs, 71 RBI
6 Kendall George CF A+ 20 104.4 wRC+, 0.264/0.351/0.356, 7 HR, 69 SB, 70 Runs, 41 RBI
7 Emil Morales SS A 18 106.8 wRC+, 0.246/0.313/0.428, 23 HR, 6 SB, 68 Runs, 75 RBI
8 Ching-Hsien Ko DH A 18 110.7 wRC+, 0.248/0.347/0.391, 14 HR, 2 SB, 72 Runs, 59 RBI
9 Francisco Espinoza C CPX 18 103.6 wRC+, 0.255/0.327/0.391, 14 HR, 3 SB, 70 Runs, 58 RBI
10 Chase Harlan 3B A 18 99.7 wRC+, 0.237/0.305/0.409, 21 HR, 2 SB, 64 Runs, 72 RBI
11 Ryan Ward RF AAA 27 93.9 wRC+, 0.224/0.283/0.419, 26 HR, 9 SB, 70 Runs, 77 RBI
12 Zach Ehrhard RF AA 22 97.5 wRC+, 0.235/0.321/0.374, 14 HR, 25 SB, 65 Runs, 61 RBI
13 Alex Freeland SS MLB 23 98.6 wRC+, 0.225/0.328/0.368, 15 HR, 17 SB, 66 Runs, 61 RBI
14 Reyli Mariano 2B DSL 18 95.7 wRC+, 0.224/0.315/0.375, 16 HR, 22 SB, 71 Runs, 60 RBI
15 River Ryan SP MLB 25 3.93 ERA, 119 IP, 47 BB, 111 K, 8.4 K/9, 1.24 WHIP
16 Edwin Sanchez 1B DSL 20 97.7 wRC+, 0.197/0.336/0.350, 18 HR, 5 SB, 63 Runs, 62 RBI
17 James Tibbs III RF AA 22 96.2 wRC+, 0.223/0.319/0.371, 17 HR, 4 SB, 68 Runs, 60 RBI
18 Charles Davalan OF A 21 92.4 wRC+, 0.242/0.303/0.380, 15 HR, 13 SB, 66 Runs, 60 RBI
19 Victor Rodrigues C A 20 97.2 wRC+, 0.239/0.339/0.344, 10 HR, 4 SB, 62 Runs, 49 RBI
20 Joendry Vargas SS A 19 84.4 wRC+, 0.223/0.282/0.379, 19 HR, 31 SB, 63 Runs, 65 RBI
21 Kyle Hurt SP MLB 27 3.82 ERA, 85 IP, 36 BB, 90 K, 9.5 K/9, 1.21 WHIP
22 Nicolas Perez SS A 20 83.9 wRC+, 0.223/0.300/0.347, 13 HR, 43 SB, 61 Runs, 55 RBI
23 Elijah Hainline SS AAA 22 89.4 wRC+, 0.219/0.320/0.339, 12 HR, 19 SB, 63 Runs, 52 RBI
24 Javier Herrera 2B AAA 20 89.3 wRC+, 0.238/0.307/0.360, 12 HR, 15 SB, 63 Runs, 55 RBI
25 Logan Wagner 1B A+ 21 87.2 wRC+, 0.204/0.305/0.353, 17 HR, 7 SB, 64 Runs, 61 RBI
26 Chris Newell LF AA 24 82.5 wRC+, 0.197/0.285/0.364, 21 HR, 15 SB, 62 Runs, 67 RBI
27 Kole Myers CF AA 24 86.8 wRC+, 0.220/0.328/0.315, 8 HR, 27 SB, 67 Runs, 42 RBI
28 Jose D. Hernandez 1B A 22 84.4 wRC+, 0.226/0.288/0.369, 17 HR, 12 SB, 59 Runs, 63 RBI
29 Jhon GIl C DSL 17 86.2 wRC+, 0.229/0.304/0.351, 13 HR, 14 SB, 61 Runs, 55 RBI
30 Eduardo Rojas C DSL 18 88.0 wRC+, 0.217/0.321/0.332, 12 HR, 9 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 Reyli Mariano Mookie Betts Max Muncy Zyhir Hope Eduardo Quintero Josue De Paula
Projected Backup Dalton Rushing Edwin Sanchez Yeiner Fernandez Emil Morales Chase Harlan Teoscar Hernández Andy Pages Zach Ehrhard
Depth Francisco Espinoza Logan Wagner Javier Herrera Alex Freeland Enrique Hernández Michael Conforto Mike Sirota James Tibbs III

Starting Pitchers

Projected #1 Yoshinobu Yamamoto
Projected #2 Shohei Ohtani
Projected #3 Tyler Glasnow
Projected #4 Emmet Sheehan
Projected #5 Blake Snell
Depth Gavin Stone
Depth Justin Wrobleski
Depth Michael Grove
Depth River Ryan
Depth Clayton Kershaw

Relief Pitchers

Projected Closer Edgardo Henriquez
Projected Setup #1 Tanner Scott
Projected Setup #2 Brusdar Graterol
Next Up Evan Phillips
Next Up Alex Vesia
Next Up Brock Stewart
Depth Blake Treinen
Depth Ronan Kopp
Depth Will Klein
Depth Jack Dreyer

Top prospects by tool!

Hit Power Speed
Kendall George – 60 Ryan Ward – 60 Kendall George – 80
Ching-Hsien Ko – 55 Chase Harlan – 55 Nicolas Perez – 70
Francisco Espinoza – 55 Mike Sirota – 55 Jaron Elkins – 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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