Down on the Farm: The Toronto Blue Jays – June 2025

Down on the Farm: The Toronto Blue Jays – June 2025

The Toronto Blue Jays Top 30 Prospects!

Rank Name Position Level Age Projections
1 Trey Yesavage SP A+ 21 3.28 ERA, 138 IP, 57 BB, 162 K, 10.6 K/9, 1.11 WHIP
2 Arjun Nimmala SS A+ 19 125.1 wRC+, 0.256/0.329/0.484, 27 HR, 7 SB, 82 Runs, 86 RBI
3 Orelvis Martinez 2B MLB 23 110.3 wRC+, 0.234/0.304/0.459, 30 HR, 2 SB, 69 Runs, 88 RBI
4 Ricky Tiedemann SP AAA 21 3.54 ERA, 102 IP, 44 BB, 118 K, 10.4 K/9, 1.20 WHIP
5 Jonatan Clase LF MLB 23 98.9 wRC+, 0.237/0.317/0.387, 15 HR, 47 SB, 74 Runs, 60 RBI
6 Adrian Pinto 2B A+ 22 102.7 wRC+, 0.245/0.318/0.402, 18 HR, 18 SB, 69 Runs, 66 RBI
7 RJ Schreck LF AA 24 99.7 wRC+, 0.219/0.323/0.380, 18 HR, 7 SB, 68 Runs, 66 RBI
8 Alan Roden RF MLB 25 103.3 wRC+, 0.251/0.338/0.372, 11 HR, 8 SB, 69 Runs, 55 RBI
9 Victor Arias LF AA 21 97.6 wRC+, 0.239/0.323/0.372, 13 HR, 18 SB, 69 Runs, 57 RBI
10 Sam Shaw 2B A 20 98.3 wRC+, 0.230/0.327/0.368, 15 HR, 7 SB, 66 Runs, 58 RBI
11 Eddinson Paulino 3B AA 22 94.8 wRC+, 0.236/0.306/0.385, 13 HR, 14 SB, 72 Runs, 64 RBI
12 Dariel Ramon N/A A 19 91.3 wRC+, 0.231/0.312/0.361, 13 HR, 25 SB, 69 Runs, 55 RBI
13 Riley Tirotta 1B AAA 26 89.3 wRC+, 0.216/0.304/0.365, 17 HR, 11 SB, 67 Runs, 59 RBI
14 Jace Bohrofen RF AA 23 88.8 wRC+, 0.214/0.299/0.370, 19 HR, 3 SB, 64 Runs, 65 RBI
15 Yohendrick Pinango LF AAA 23 89.5 wRC+, 0.239/0.302/0.369, 13 HR, 7 SB, 61 Runs, 60 RBI
16 Charles McAdoo 3B AA 23 85.1 wRC+, 0.224/0.294/0.363, 15 HR, 14 SB, 64 Runs, 59 RBI
17 Eddie Micheletti Jr. LF A+ 23 87.7 wRC+, 0.218/0.304/0.358, 15 HR, 3 SB, 62 Runs, 61 RBI
18 Rainer Nunez 3B AAA 24 88.2 wRC+, 0.241/0.294/0.377, 16 HR, 1 SB, 58 Runs, 62 RBI
19 Damiano Palmegiani 1B AAA 25 85.5 wRC+, 0.206/0.296/0.361, 19 HR, 1 SB, 60 Runs, 65 RBI
20 Alex De Jesus 3B AA 23 86.3 wRC+, 0.225/0.300/0.359, 13 HR, 3 SB, 66 Runs, 58 RBI
21 Maykel Minoso C A 19 84.3 wRC+, 0.231/0.289/0.367, 15 HR, 10 SB, 62 Runs, 60 RBI
22 Aaron Parker C A+ 22 83.4 wRC+, 0.220/0.287/0.366, 16 HR, 5 SB, 62 Runs, 64 RBI
23 Sean Keys 3B A+ 22 84.7 wRC+, 0.214/0.303/0.345, 14 HR, 6 SB, 65 Runs, 56 RBI
24 Andres Arias RF CPX 18 80.1 wRC+, 0.212/0.281/0.362, 19 HR, 15 SB, 60 Runs, 63 RBI
25 Peyton Williams 1B AA 24 83.9 wRC+, 0.221/0.289/0.365, 17 HR, 1 SB, 57 Runs, 62 RBI
26 Edward Duran C A 21 85.9 wRC+, 0.236/0.307/0.345, 9 HR, 6 SB, 64 Runs, 50 RBI
27 Cutter Coffey 3B A+ 21 82.2 wRC+, 0.215/0.292/0.353, 16 HR, 8 SB, 58 Runs, 61 RBI
28 Devonte Brown 3B AA 25 81.7 wRC+, 0.203/0.298/0.341, 16 HR, 9 SB, 61 Runs, 57 RBI
29 Kendry Chirinos 3B A 20 83.8 wRC+, 0.216/0.306/0.338, 13 HR, 3 SB, 65 Runs, 51 RBI
30 Gabriel Martinez LF AA 22 83.4 wRC+, 0.235/0.293/0.355, 12 HR, 2 SB, 56 Runs, 59 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 Alejandro Kirk Vladimir Guerrero Jr. Orelvis Martinez Arjun Nimmala Addison Barger RJ Schreck Daulton Varsho George Springer
Projected Backup Edward Duran Brandon Belt Adrian Pinto Bo Bichette Eddinson Paulino Jonatan Clase Jean Joseph Anthony Santander
Depth Maykel Minoso Riley Tirotta Andrés Giménez Leo Jiménez Rainer Nunez Davis Schneider Dasan Brown Alan Roden

Starting Pitchers

Projected #1 Trey Yesavage
Projected #2 Kevin Gausman
Projected #3 Ricky Tiedemann
Projected #4 Max Scherzer
Projected #5 Alek Manoah
Depth Chris Bassitt
Depth José Berríos
Depth Yariel Rodríguez
Depth Bowden Francis
Depth Juaron Watts-Brown

Relief Pitchers

Projected Closer Jeff Hoffman
Projected Setup #1 Yimi García
Projected Setup #2 Brendon Little
Next Up Tommy Nance
Next Up T.J. Brock
Next Up Mason Fluharty
Depth Ryan Burr
Depth Zach Pop
Depth Richard Lovelady
Depth Yerry Rodríguez

Top prospects by tool!

Hit Power Speed
Arjun Nimmala – 55 Arjun Nimmala – 60 Jonatan Clase – 70
Alan Roden – 55 Orelvis Martinez – 60 Dariel Ramon – 60
Sam Shaw – 50 Cristian Feliz – 55 Marcos De La Rosa – 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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