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

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

The Toronto Blue Jays Top 30 Prospects!

Rank Name Position Level Age Projections
1 Trey Yesavage SP MLB 21 3.16 ERA, 125 IP, 54 BB, 152 K, 10.9 K/9, 1.12 WHIP
2 Ricky Tiedemann SP AAA 21 3.54 ERA, 102 IP, 44 BB, 118 K, 10.4 K/9, 1.20 WHIP
3 Arjun Nimmala SS A+ 19 106.9 wRC+, 0.240/0.311/0.432, 22 HR, 12 SB, 74 Runs, 76 RBI
4 Johnny King SP A 18 3.62 ERA, 110 IP, 59 BB, 123 K, 10.0 K/9, 1.28 WHIP
5 Adrian Pinto 2B A+ 22 102.1 wRC+, 0.243/0.317/0.401, 18 HR, 16 SB, 68 Runs, 66 RBI
6 RJ Schreck LF AAA 24 103.7 wRC+, 0.220/0.332/0.383, 18 HR, 4 SB, 70 Runs, 65 RBI
7 Victor Arias LF AA 21 95.4 wRC+, 0.242/0.316/0.373, 12 HR, 16 SB, 71 Runs, 56 RBI
8 Yohendrick Pinango LF AAA 23 95.6 wRC+, 0.240/0.310/0.384, 15 HR, 3 SB, 63 Runs, 63 RBI
9 Charles McAdoo 3B AA 23 88.8 wRC+, 0.229/0.295/0.377, 17 HR, 22 SB, 64 Runs, 63 RBI
10 Sean Keys 3B A+ 22 91.3 wRC+, 0.211/0.314/0.357, 16 HR, 4 SB, 66 Runs, 60 RBI
11 Eddie Micheletti Jr. LF A+ 23 90.9 wRC+, 0.214/0.319/0.347, 13 HR, 1 SB, 65 Runs, 57 RBI
12 Riley Tirotta 1B AAA 26 87.8 wRC+, 0.219/0.305/0.357, 15 HR, 9 SB, 65 Runs, 56 RBI
13 Cutter Coffey 3B A+ 21 87.5 wRC+, 0.225/0.299/0.365, 16 HR, 8 SB, 58 Runs, 63 RBI
14 Eddinson Paulino 3B AA 22 84.9 wRC+, 0.223/0.287/0.372, 15 HR, 11 SB, 67 Runs, 64 RBI
15 Devonte Brown 3B AAA 25 85.6 wRC+, 0.206/0.301/0.353, 17 HR, 10 SB, 62 Runs, 59 RBI
16 Dariel Ramon SS A 19 87.3 wRC+, 0.226/0.314/0.340, 10 HR, 16 SB, 64 Runs, 51 RBI
17 Austin Smith OF A 22 85.6 wRC+, 0.227/0.304/0.349, 12 HR, 19 SB, 61 Runs, 55 RBI
18 Sam Shaw 2B A+ 20 87.5 wRC+, 0.219/0.308/0.350, 14 HR, 5 SB, 65 Runs, 56 RBI
19 Jace Bohrofen RF AA 23 83.4 wRC+, 0.204/0.295/0.353, 17 HR, 7 SB, 63 Runs, 62 RBI
20 Jake Casey OF A 22 83.6 wRC+, 0.213/0.298/0.348, 15 HR, 9 SB, 66 Runs, 56 RBI
21 Rafael Flores 3B DSL 18 84.0 wRC+, 0.217/0.307/0.337, 13 HR, 14 SB, 60 Runs, 53 RBI
22 Kendry Chirinos 3B A 20 86.1 wRC+, 0.223/0.306/0.348, 13 HR, 2 SB, 64 Runs, 53 RBI
23 Damiano Palmegiani 1B AAA 25 81.7 wRC+, 0.202/0.291/0.352, 18 HR, 1 SB, 60 Runs, 63 RBI
24 Jaxson West C A 21 82.1 wRC+, 0.222/0.302/0.336, 11 HR, 7 SB, 61 Runs, 52 RBI
25 J.R. Freethy 2B AA 22 82.7 wRC+, 0.208/0.317/0.314, 9 HR, 2 SB, 65 Runs, 46 RBI
26 Alexis Hernandez 1B A+ 22 77.2 wRC+, 0.217/0.286/0.339, 13 HR, 18 SB, 55 Runs, 57 RBI
27 Franklin Rojas DH DSL 18 80.9 wRC+, 0.215/0.299/0.335, 13 HR, 3 SB, 58 Runs, 54 RBI
28 Aaron Parker C A+ 22 77.8 wRC+, 0.219/0.277/0.358, 16 HR, 5 SB, 58 Runs, 62 RBI
29 Alex De Jesus 3B AA 23 80.3 wRC+, 0.217/0.294/0.341, 12 HR, 3 SB, 63 Runs, 54 RBI
30 Pascual Archila CF DSL 18 78.9 wRC+, 0.196/0.308/0.313, 13 HR, 13 SB, 57 Runs, 52 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. Adrian Pinto Bo Bichette Addison Barger Davis Schneider Daulton Varsho George Springer
Projected Backup Jaxson West Ty France Andrés Giménez Arjun Nimmala Sean Keys RJ Schreck Pascual Archila Anthony Santander
Depth Edward Duran Riley Tirotta Michael Stefanic Leo Jiménez Charles McAdoo Nathan Lukes Brennan Orf Jace Bohrofen

Starting Pitchers

Projected #1 Trey Yesavage
Projected #2 Kevin Gausman
Projected #3 Shane Bieber
Projected #4 Ricky Tiedemann
Projected #5 Louie Varland
Depth Johnny King
Depth Chris Bassitt
Depth Max Scherzer
Depth José Berríos
Depth Bowden Francis

Relief Pitchers

Projected Closer Yimi García
Projected Setup #1 Brendon Little
Projected Setup #2 Seranthony Domínguez
Next Up Jeff Hoffman
Next Up Tommy Nance
Next Up Braydon Fisher
Depth T.J. Brock
Depth Ryan Burr
Depth Justin Bruihl
Depth Mason Fluharty

Top prospects by tool!

Hit Power Speed
Arjun Nimmala – 50 Arjun Nimmala – 55 Dasan Brown – 70
Josh Kasevich – 50 Sean Keys – 50 Maddox Latta – 55
Edward Duran – 50 Carter Cunningham – 50 Austin Smith – 55

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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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2 Comments

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