Rosternomics

2017 MLB Draft

Every MLB amateur draft, 1985–2025. Real = career WAR so far. Expected = the model's full-career projection for this slot × position × age-at-draft (HS vs college), ± a predictive band. DVOS = Real minus what the same cohort has produced by the same number of years post-draft (apples-to-apples — so we don't punish a 2024 pick for not yet being a 10-yr veteran). Example: Bazzana (2024 #1, college IF) has Real 0.8; the typical #1 college IF has produced 1.6 WAR by year 2 post-draft, so his DVOS = −0.8 (0.8 behind cohort); long-run Expected ≈ 40 WAR. Model: empirical-Bayes hierarchical, fit on 41 years of realized outcomes. SD leaderboard →

Round:
Year Pick Rd Team Player Pos Age B/T Ht Wt Bonus Bonus% Scouting Dir GM / POBO Real DVOS Expected
2017 1 1.1 MIN Royce Lewis IF 3B 18 HS R/R 6'0" 200 $6.72M 45% Sean Johnson Derek Falvey 4.6 -18.5 49.9±4.0
2017 2 1.2 CIN Hunter Greene P P 18 HS R/R 6'5" 242 $7.23M 49% Chris Buckley 10.2 -0.9 18.9±3.8
2017 3 1.3 SDP MacKenzie Gore P P 18 HS L/L 6'2" 193 $6.7M 49% Mark Conner AJ Preller 8.9 +2.2 9.9±4.1
2017 4 1.4 TBR Brendan McKay P P 22 COLL L/L 6'2" 220 $7.0M 58% Rob Metzler Erik Neander 0.8 -6.2 9.8±3.8
2017 5 1.5 ATL Kyle Wright P P 22 COLL R/R 6'4" 215 $7.0M 61% Brian Bridges John Hart 1.9 -4.6 8.5±3.9
2017 6 1.6 OAK Austin Beck OF CF 19 HS R/R 6'1" 200 $5.3M 42% Eric Kubota David Forst 0.0 -4.4 8.0±3.9
2017 7 1.7 ARI Pavin Smith IF 1B 21 COLL L/L 6'2" 208 $5.02M 44% Deric Ladnier Mike Hazen 1.3 -8.8 14.8±4.0
2017 8 1.8 PHI Adam Haseley OF CF 21 COLL L/L 6'1" 190 $5.1M 47% Johnny Almaraz Matt Klentak 0.1 -9.0 8.5±3.9
2017 9 1.9 MIL Keston Hiura IF 1B 21 COLL R/R 5'10" 208 $4.0M 33% Tod Johnson David Stearns 2.0 -6.5 14.8±3.8
2017 10 1.10 ANA Jo Adell OF RF 18 HS R/R 6'2" 215 $4.38M 47% Matt Swanson Billy Eppler 0.2 -3.8 8.0±3.8
2017 11 1.11 CHW Jake Burger IF 1B 21 COLL R/R 6'0" 230 $3.7M 42% Kenny Williams 4.6 -3.9 10.8±3.7
2017 12 1.12 PIT Shane Baz P P 18 HS R/R 6'3" 200 $4.1M 34% Joe Dellicarri Neal Huntington 3.9 +0.6 5.5±3.7
2017 13 1.13 MIA Trevor Rogers P P 20 COLL L/L 6'5" 230 $3.4M 32% Michael Hill 10.3 +4.6 7.9±3.7
2017 14 1.14 KCR Nick Pratto IF 1B 19 HS L/L 6'1" 225 $3.45M 33% Lonnie Goldberg Dayton Moore -0.8 -4.5 7.8±3.8
2017 15 1.15 HOU J.B. Bukauskas P P 21 COLL R/R 6'0" 210 $3.6M 34% Jeff Luhnow -0.3 -5.9 7.9±3.7
2017 16 1.16 NYY Clarke Schmidt P P 21 COLL R/R 6'1" 210 $2.18M 27% Damon Oppenheimer Brian Cashman 5.4 +1.4 5.5±3.7
2017 17 1.17 SEA Evan White IF 1B 21 COLL R/L 6'1" 219 $3.12M 42% Scott Hunter Jerry Dipoto -0.8 -7.7 10.8±3.7
2017 18 1.18 DET Alex Faedo P P 22 COLL R/R 6'6" 225 $3.5M 42% Scott Pleis Al Avila 0.4 -3.6 5.5±3.7
2017 19 1.19 SFG Heliot Ramos OF LF 18 HS R/R 5'11" 235 $3.1M 39% Bobby Evans 3.3 -0.7 8.0±3.7
2017 20 1.20 NYM David Peterson P P 22 COLL L/L 6'6" 240 $2.99M 38% Marc Tramuta Sandy Alderson 9.4 +5.4 5.5±3.7
2017 21 1.21 BAL DL Hall P P 19 HS L/L 6'1" 209 $3.0M 40% Gary Rajsich Dan Duquette 1.5 -1.3 5.5±3.7
2017 22 1.22 TOR Logan Warmoth IF SS 22 COLL R/R 5'11" 195 $2.82M 29% Steve Sanders Ross Atkins 0.0 -6.9 10.8±3.7
2017 23 1.23 LAD Jeren Kendall OF CF 21 COLL L/R 5'11" 190 $2.9M 35% Billy Gasparino Andrew Friedman 0.0 -3.4 4.4±3.7
2017 24 1.24 BOS Tanner Houck P P 21 COLL R/R 6'5" 226 $2.61M 35% Mike Rikard Dave Dombrowski 8.1 +4.2 5.5±3.7
2017 25 1.25 WSN Seth Romero P P 21 COLL L/L 6'3" 213 $2.8M 42% Eddie Longosz Michael Rizzo -0.1 -4.1 5.5±3.7
2017 26 1.26 TEX Bubba Thompson OF OF 19 HS R/R 6'2" 197 $2.1M 23% Kip Fagg Jon Daniels -0.3 -1.7 2.4±3.7
2017 27 1.27 CHC Brendon Little P P 21 COLL L/L 6'2" 195 $2.2M 25% Matt Dorey Jed Hoyer 0.2 -1.5 3.0±3.7
2017 28 1.28 TOR Nate Pearson P P 21 COLL R/R 6'6" 255 $2.45M 25% Steve Sanders Ross Atkins -0.3 -2.1 3.0±3.7
2017 29 1.29 TEX Chris Seise IF SS 18 HS R/R 6'1" 196 $2.0M 22% Kip Fagg Jon Daniels 0.0 -1.7 2.8±3.7
2017 30 1.30 CHC Alex Lange P P 22 COLL R/R 6'3" 202 $1.93M 22% Matt Dorey Jed Hoyer 0.8 -0.9 3.0±3.7

Model: empirical-Bayes hierarchical — smooth monotone slot curve × position group (P/IF/OF/C/DH) × age bin (HS <20 vs COLL 20+) × maturity. The slot curve is forced monotone-decreasing within each position+age cell, so pick N can't have lower expected than pick N+1 by construction. Predictive ± band = √(σ²·(1 + 1/n_cell)) — pooled within-cell residual SD plus the cell-mean uncertainty for a new pick at that cell. Fallback chain when a (slot, pos, age) cell is too sparse: → (slot, pos) → (slot). Position × age effects: pitchers debut earlier (high realized at 2 yrs, lower career due to attrition); college catchers ~2× HS catchers career WAR at top slots; HS slot-1 IF/OF carry premium upside (Harper/Correa/A-Rod pattern). Coverage: position 100% (1985–2025), bio (born/ht/wt) ~97%, age 97%, signing bonus only ~11% (sparse pre-2008 — MLB Stats API hasn't backfilled). SD attribution follows SABR (single unambiguous name per team-year), GM uses our hire/POBO records. Team/GM development effects intentionally NOT in the model — that signal belongs on the DVOS-attribution side (per-SD on /sds, per-GM in this table), or it would collapse to zero by construction.