Down on the Farm: The Detroit Tigers – October 2025

Down on the Farm: The Detroit Tigers – October 2025

The Detroit Tigers Top 30 Prospects!

Rank Name Position Level Age Projections
1 Kevin McGonigle SS AA 20 141.2 wRC+, 0.285/0.369/0.491, 23 HR, 14 SB, 81 Runs, 83 RBI
2 Josue Briceno C AA 20 132.9 wRC+, 0.268/0.353/0.480, 26 HR, 1 SB, 79 Runs, 82 RBI
3 Max Clark CF AA 20 127.3 wRC+, 0.266/0.363/0.439, 18 HR, 10 SB, 80 Runs, 67 RBI
4 Thayron Liranzo C AA 21 115.5 wRC+, 0.234/0.331/0.441, 26 HR, 1 SB, 68 Runs, 80 RBI
5 Hao-Yu Lee 2B AAA 22 109.3 wRC+, 0.253/0.329/0.414, 16 HR, 15 SB, 77 Runs, 64 RBI
6 Bryce Rainer SS A 19 105.8 wRC+, 0.247/0.318/0.415, 21 HR, 16 SB, 67 Runs, 69 RBI
7 Jace Jung 3B MLB 24 108.2 wRC+, 0.231/0.332/0.404, 20 HR, 2 SB, 69 Runs, 69 RBI
8 Troy Melton SP MLB 24 3.88 ERA, 123 IP, 43 BB, 117 K, 8.6 K/9, 1.15 WHIP
9 Ty Madden SP MLB 24 4.06 ERA, 146 IP, 52 BB, 130 K, 8.0 K/9, 1.19 WHIP
10 Eduardo Valencia C AAA 25 94.4 wRC+, 0.240/0.295/0.402, 21 HR, 1 SB, 61 Runs, 69 RBI
11 Eddys Leonard SS AAA 24 92.7 wRC+, 0.236/0.293/0.397, 19 HR, 8 SB, 65 Runs, 69 RBI
12 Max Anderson 2B AAA 23 95.2 wRC+, 0.257/0.297/0.403, 17 HR, 1 SB, 62 Runs, 67 RBI
13 Cristian Santana SS A 21 93.5 wRC+, 0.186/0.326/0.347, 20 HR, 2 SB, 65 Runs, 62 RBI
14 Jesus Pinto CF A 18 88.1 wRC+, 0.237/0.286/0.389, 18 HR, 21 SB, 61 Runs, 67 RBI
15 Jack Goodman SS A 21 91.0 wRC+, 0.231/0.310/0.363, 14 HR, 15 SB, 66 Runs, 57 RBI
16 Izaac Pacheco 3B A+ 22 89.7 wRC+, 0.217/0.295/0.381, 19 HR, 2 SB, 65 Runs, 68 RBI
17 Cris Rodriguez CF DSL 17 82.8 wRC+, 0.230/0.254/0.416, 26 HR, 11 SB, 57 Runs, 80 RBI
18 Carlos Mendoza 2B AAA 25 93.3 wRC+, 0.239/0.341/0.322, 5 HR, 15 SB, 67 Runs, 39 RBI
19 Jude Warwick SS A 19 84.6 wRC+, 0.227/0.300/0.351, 13 HR, 26 SB, 59 Runs, 56 RBI
20 Franyerber Montilla SS A 20 83.1 wRC+, 0.218/0.298/0.346, 13 HR, 29 SB, 60 Runs, 58 RBI
21 John Peck 2B AA 22 86.3 wRC+, 0.244/0.294/0.367, 13 HR, 16 SB, 60 Runs, 57 RBI
22 Roberto Campos RF AA 22 87.5 wRC+, 0.237/0.292/0.376, 13 HR, 2 SB, 65 Runs, 61 RBI
23 Josueth Quinonez CF DSL 18 87.2 wRC+, 0.237/0.298/0.365, 15 HR, 6 SB, 56 Runs, 60 RBI
24 Javier Osorio SS CPX 20 78.6 wRC+, 0.221/0.275/0.363, 18 HR, 32 SB, 57 Runs, 63 RBI
25 Brett Callahan CF A+ 23 82.2 wRC+, 0.228/0.279/0.373, 17 HR, 13 SB, 64 Runs, 61 RBI
26 Jake Holton 1B AA 27 85.5 wRC+, 0.214/0.302/0.352, 16 HR, 1 SB, 63 Runs, 59 RBI
27 Steven Madero C DSL 18 82.6 wRC+, 0.222/0.285/0.365, 18 HR, 5 SB, 58 Runs, 62 RBI
28 Chris Meyers 1B AA 26 83.1 wRC+, 0.221/0.288/0.363, 17 HR, 1 SB, 61 Runs, 62 RBI
29 Kelvis Salcedo SP A 19 4.17 ERA, 104 IP, 49 BB, 95 K, 8.2 K/9, 1.33 WHIP
30 Nick Dumesnil OF A 21 80.0 wRC+, 0.212/0.306/0.321, 10 HR, 28 SB, 61 Runs, 50 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 Josue Briceno Spencer Torkelson Colt Keith Kevin McGonigle Jace Jung Riley Greene Max Clark Kerry Carpenter
Projected Backup Thayron Liranzo Jake Holton Gleyber Torres Bryce Rainer Izaac Pacheco Akil Baddoo Matt Vierling Wenceel Pérez
Depth Dillon Dingler Chris Meyers Hao-Yu Lee Cristian Santana Nestor Miranda Manuel Margot Parker Meadows Roberto Campos

Starting Pitchers

Projected #1 Tarik Skubal
Projected #2 Reese Olson
Projected #3 Jack Flaherty
Projected #4 Troy Melton
Projected #5 Casey Mize
Depth Ty Madden
Depth Brant Hurter
Depth Jackson Jobe
Depth Alex Cobb
Depth Charlie Morton

Relief Pitchers

Projected Closer Will Vest
Projected Setup #1 Kyle Finnegan
Projected Setup #2 Tyler Holton
Next Up Jason Foley
Next Up Brenan Hanifee
Next Up Drew Sommers
Depth Chase Lee
Depth Alex Lange
Depth Bailey Horn
Depth Kervin Castro

Top prospects by tool!

Hit Power Speed
Kevin McGonigle – 70 Cris Rodriguez – 60 Seth Stephenson – 70
Max Clark – 60 Josue Briceno – 60 Sterling Bazil – 60
Josue Briceno – 60 Thayron Liranzo – 60 Jude Warwick – 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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