In early April, The Dynasty Guru released a tool for translating minor league statistics to MLB equivalent statistics, also demonstrating their predictive validity for projecting 2019 performance. In March, we released a four–part aging curves series leveraging a new twist on the delta method. Now, we’ve combined those two projects to create The Dynasty Guru’s Sortable MLB Equivalency Leaderboard (a big thanks to Ross Jensen for all his hard work putting the leaderboards together!). For now, the leaderboards only display 2018 translations, but we will update them weekly with 2019 minor league data. As this is our first time rolling them out, we ask that you please let us know in the comments if anything seems off and we’ll double check it.

As part of these leaderboards, we’ve presented a peak wOBA measure to rank prospects, and get a better idea of their future potential. Like our validated 2019 translations, these values combine aging curves with 2018 translated minor league performance, our own spin on Clay Davenport’s excellent peak translations (you should check them out at claydavenport.com if you haven’t already). Our aging curves and translations have been validated up to one year into the future. Our peak wOBA values are much less accurate, as projecting the distant future is very hard, and their accuracy suffers from using the same general growth rate for all players. This means they miss the tendency for good players to improve less, percentage-wise, than bad players, and for bad players to improve more. This isn’t a big deal for the one year into the future translations–it doesn’t hurt predictive validity much–but it becomes more of an issue as we project further into the future. For this reason, the peak wOBA values are better for comparing minor leaguers to one another than for comparing minor leaguers to major leaguers. Nonetheless, we think the measure reasonably approximates future MLB wOBA even if it’s slightly overstated and understated for various players. We also think it’ll mesh well with many readers’ intuitions on the quality of age-relative-to-league prospect performance–indeed, the top three players on the list below are not at all surprising.

A note on peak wOBA age: I’ve found the delta method is better at accurately approximating peak growth rates than peak age, and peak wOBA age likely lies anywhere between 25 and 30, with most significant wOBA growth happening by 25.

Keep in mind peak wOBA doesn’t regress BABIP at all and only considers one year of performance. Readers should not expect Vlad Guerrero Jr. to post anywhere close to a .400+ BABIP in the major leagues (like he did in Double-A)!

Performance is adjusted for league difficulty, but not for park.

Please exercise caution with translations below Single-A. Anecdotally, numbers below Single-A seem to be less meaningful than numbers at more advanced levels. For example, very few minor leaguers carry over Dominican Summer League success stateside. While Malcolm Nunez’s debut was promising, there’s a reason he hasn’t cracked most top 100 or even top 200 lists yet (for context, the last two 17-year olds to come close to Nunez’s DSL success were Andres Gimenez and Domingo Leyba). Further, the aging curves are built using MLB data. They likely understate gains for players younger than 19 because there is little data on how these players age, and growth tends to increase exponentially at younger and younger ages.

Without further ado, here are the top 50 offensive prospects according to peak wOBA (based on aging curves and aggregate 2018 minor league translated performance across all levels, min. 300 plate appearances, must have Single-A experience or higher, players with pre-2019 MLB experience excluded, old-for-level players excluded, i.e., max 2018 in-season age of 23 for Triple-A prospects, 22 for Double-A prospects, etc. )

Top 50 Stats-Only Offensive Prospects:

1 Vladimir Guerrero Jr., 3B, TOR (incorporating MLB and MILB 2018 data, Juan Soto would have ranked a very close 2nd, and 1st when regressing BABIP for both. Ronald Acuna Jr. would have ranked a distant third, right above Eloy Jimenez).
2 Eloy Jimenez, OF, CWS
3 Fernando Tatis Jr., SS, SD
4 Alex Kirilloff, OF, MIN
5 Nathaniel Lowe, 1B, TB
6 Yordan Alvarez, OF, HOU
7 Peter Alonso, 1B, NYM
8 Jo Adell, OF, LAA
9 Gavin Lux, SS, LAD
10 Elehuris Montero, 3B, STL
11 Austin Riley, 3B, ATL
12 Isaac Paredes, SS/3B, DET
13 Ryan McKenna, OF, BAL
14 Nolan Jones, 3B, CLE
15 Yusniel Diaz, OF, BAL
16 Luis Rengifo, 2B, LAA
17 Vidal Brujan, 2B, TB
18 Josh Naylor, 1B, SD
19 Colton Welker, 3B, COL
20 Carter Kieboom, SS, WAS
21 Moises Gomez, OF, TB
22 Royce Lewis, SS, MIN
23 Khalil Lee, OF, KC
24 Keibert Ruiz, C, LAD
25 Bo Bichette, SS, TOR
26 Tyler Nevin, 1B/3B, COL
27 Ke’Bryan Hayes, SS, PIT
28 Micker Adolfo, OF, CWS
29 Seuly Matias, OF, KC
30 Keston Hiura, 2B, MIL
31 Andres Gimenez, SS, NYM
32 Drew Waters, OF, ATL
33 Lazaro Armenteros, OF, OAK
34 Carlos Rincon, OF, LAD
35 Hudson Potts, 3B, SD
36 Jeisson Rosario, OF, SD
37 Taylor Trammell, OF, CIN
38 Oneil Cruz, 3B/SS, PIT
39 Jazz Chisholm, SS, ARI
40 Tirso Ornelas, OF, SD
41 Ronaldo Hernandez, C, TB
42 Luis Alexander Basabe, OF, CWS
43 Akil Baddoo, OF, MIN
44 Ryan Mountcastle, 3B/SS, BAL
45 Estevan Florial, OF, NYY
46 Josh Ockimey, 1B, BOS
47 Anderson Tejeda, 2B, TEX
48 Brendan Rodgers 2B/SS, COL
49 Dylan Carlson, OF, STL
50 Randy Arozarena, OF, STL