Down on the Farm: The Toronto Blue Jays – March 2026

Down on the Farm: The Toronto Blue Jays – March 2026

The Toronto Blue Jays Top 30 Prospects!

Rank Name Position Level Age Projections
1 Trey Yesavage SP MLB 21 3.20 ERA, 125 IP, 52 BB, 154 K, 11.1 K/9, 1.24 WHIP
2 Ricky Tiedemann SP AAA 21 3.47 ERA, 102 IP, 47 BB, 118 K, 10.4 K/9, 1.31 WHIP
3 Arjun Nimmala SS A+ 19 102.1 wRC+, 0.239/0.312/0.417, 20 HR, 12 SB, 74 Runs, 73 RBI
4 Kazuma Okamoto 3B MLB 29 108.9 wRC+, 0.233/0.319/0.437, 27 HR, 3 SB, 71 Runs, 80 RBI
5 Johnny King SP A 18 3.78 ERA, 110 IP, 56 BB, 118 K, 9.6 K/9, 1.43 WHIP
6 Adrian Pinto 2B A+ 22 99.1 wRC+, 0.242/0.317/0.396, 18 HR, 16 SB, 69 Runs, 67 RBI
7 Victor Arias CF AA 21 95.9 wRC+, 0.244/0.318/0.384, 14 HR, 15 SB, 73 Runs, 61 RBI
8 RJ Schreck RF AAA 24 102.0 wRC+, 0.223/0.331/0.380, 18 HR, 5 SB, 69 Runs, 65 RBI
9 Angel Bastardo SP AA 22 4.57 ERA, 145 IP, 68 BB, 120 K, 7.5 K/9, 1.49 WHIP
10 Dariel Ramon SS A 19 85.7 wRC+, 0.227/0.312/0.345, 12 HR, 12 SB, 65 Runs, 56 RBI
11 Sean Keys 3B A+ 22 92.0 wRC+, 0.217/0.316/0.362, 17 HR, 4 SB, 67 Runs, 62 RBI
12 Cutter Coffey 3B A+ 21 88.1 wRC+, 0.228/0.303/0.370, 17 HR, 8 SB, 61 Runs, 66 RBI
13 Carlos Mendoza 3B AAA 25 91.1 wRC+, 0.235/0.337/0.328, 8 HR, 13 SB, 68 Runs, 47 RBI
14 Gage Stanifer SP AA 21 4.51 ERA, 119 IP, 69 BB, 115 K, 8.7 K/9, 1.54 WHIP
15 Yohendrick Pinango LF AAA 23 98.9 wRC+, 0.248/0.319/0.396, 15 HR, 5 SB, 71 Runs, 66 RBI
16 Eddinson Paulino 2B AA 22 88.0 wRC+, 0.230/0.296/0.383, 16 HR, 11 SB, 70 Runs, 66 RBI
17 Adam Kloffenstein SP MLB 24 4.56 ERA, 151 IP, 60 BB, 121 K, 7.2 K/9, 1.44 WHIP
18 Charles McAdoo 3B AA 23 88.4 wRC+, 0.231/0.298/0.380, 17 HR, 23 SB, 66 Runs, 66 RBI
19 T.J. Brock RP AA 24 3.64 ERA, 37 IP, 16 BB, 42 K, 10.2 K/9, 1.31 WHIP
20 Aaron Parker C A+ 22 81.1 wRC+, 0.225/0.287/0.367, 17 HR, 6 SB, 62 Runs, 65 RBI
21 Edward Duran C A+ 21 81.5 wRC+, 0.235/0.301/0.349, 11 HR, 3 SB, 63 Runs, 56 RBI
22 Fernando Perez SP AA 21 4.89 ERA, 144 IP, 47 BB, 91 K, 5.7 K/9, 1.45 WHIP
23 Chad Dallas SP AAA 24 4.81 ERA, 150 IP, 59 BB, 110 K, 6.6 K/9, 1.46 WHIP
24 Sam Shaw 2B A+ 20 87.1 wRC+, 0.223/0.310/0.354, 14 HR, 5 SB, 65 Runs, 59 RBI
25 Jaxson West C A 21 79.4 wRC+, 0.221/0.300/0.339, 12 HR, 10 SB, 61 Runs, 57 RBI
26 Easton Lucas SP MLB 28 4.40 ERA, 93 IP, 36 BB, 83 K, 8.0 K/9, 1.38 WHIP
27 Robinson Pina SP MLB 26 4.60 ERA, 131 IP, 42 BB, 97 K, 6.7 K/9, 1.39 WHIP
28 Eddie Micheletti Jr. RF A+ 23 93.6 wRC+, 0.223/0.323/0.359, 14 HR, 1 SB, 67 Runs, 61 RBI
29 Richard Gallardo SP AA 22 4.95 ERA, 146 IP, 51 BB, 90 K, 5.6 K/9, 1.47 WHIP
30 Eric Snow SS A 21 77.7 wRC+, 0.231/0.291/0.348, 12 HR, 5 SB, 59 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. Andrés Giménez Arjun Nimmala Addison Barger Davis Schneider Daulton Varsho Nathan Lukes
Projected Backup Tyler Heineman Ty France Adrian Pinto Dariel Ramon Kazuma Okamoto Jonatan Clase Victor Arias RJ Schreck
Depth Aaron Parker Rafael Flores Leo Jiménez Eric Snow Ernie Clement Yohendrick Pinango Myles Straw Eddie Micheletti Jr.

Starting Pitchers

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

Relief Pitchers

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

Top prospects by tool!

Hit Power Speed
Kazuma Okamoto – 50 Kazuma Okamoto – 60 Maddox Latta – 55
Arjun Nimmala – 50 Sean Keys – 50 Austin Smith – 55
Charles McAdoo – 50 Carter Cunningham – 50 Charles McAdoo – 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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