Jose Peroza
| Year | Age | Lvl / Team | PA | AVG/OBP/SLG | HR | SB | wRC+ | WAR | MLE AVG/OBP/SLG |
|---|---|---|---|---|---|---|---|---|---|
| 2022 | 22 | A+NYM | 459 | .271/.360/.399 | 8 | 4 | 62 | — | .215/.276/.323 |
| 2023 | 23 | AAA/AA/INTL2 orgs | 466 | .246/.347/.421 | 13 | 4 | 79 | — | .216/.293/.348 |
| 2024 | 24 | AANYM | 82 | .243/.346/.371 | 2 | 0 | 78 | — | .233/.309/.356 |
Career Stats + Projections
Methodology▸ how these numbers are built
All seasons translated to park-neutral MLB-equivalent (MLE) and combined per year. 2027 = full-season projection (no games played yet). 2028+ via Advanced Marcel + aging curve.
| Year | Age | Lvl | Team | MLB% | PA | AB | H | 2B | 3B | HR | BB | SO | HBP | SB | CS | AVG | OBP | SLG | OPS | ISO | BABIP | K% | BB% | wOBA | wRC+ | BAT | RUN | FLD | POS | REP | WAR |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ▸ Career — actual stats, one row per stint (as recorded, no adjustments) | |||||||||||||||||||||||||||||||
| 2017 | 17 | ROK | GCL Mets | — | 251 | 230 | 67 | 21 | 1 | 2 | 18 | 63 | 0 | 7 | 4 | 291 | 343 | 417 | 760 | 126 | 389 | 25 | 7 | 336 | 41 | — | — | — | — | — | — |
| 2018 | 18 | ROK | GCL Mets | — | 95 | 87 | 16 | 3 | 1 | 0 | 8 | 25 | 0 | 2 | 2 | 184 | 253 | 241 | 494 | 57 | 258 | 26 | 8 | 231 | -10 | — | — | — | — | — | — |
| 2019 | 19 | ROK | GCL Mets | — | 72 | 64 | 21 | 6 | 2 | 6 | 4 | 15 | 3 | 1 | 0 | 328 | 394 | 766 | 1.160 | 438 | 341 | 21 | 6 | 483 | 116 | — | — | — | — | — | — |
| 2021 | 21 | A+ | Brooklyn Cyclones | — | 150 | 133 | 29 | 7 | 0 | 5 | 13 | 44 | 2 | 1 | 0 | 218 | 297 | 383 | 681 | 165 | 279 | 29 | 9 | 302 | 58 | — | — | — | — | — | — |
| 2021 | 21 | A | St. Lucie Mets | — | 278 | 226 | 62 | 15 | 1 | 7 | 41 | 67 | 9 | 5 | 0 | 274 | 404 | 442 | 847 | 168 | 359 | 24 | 15 | 381 | 78 | — | — | — | — | — | — |
| 2022 | 22 | A+ | Brooklyn Cyclones | — | 459 | 398 | 108 | 23 | 2 | 8 | 48 | 110 | 7 | 4 | 1 | 271 | 360 | 399 | 759 | 128 | 351 | 24 | 10 | 340 | 62 | — | — | — | — | — | — |
| 2023 | 23 | AAA | Syracuse Mets | — | 38 | 30 | 10 | 1 | 1 | 2 | 6 | 8 | 2 | 0 | 0 | 333 | 474 | 633 | 1.107 | 300 | 400 | 21 | 16 | 478 | 153 | — | — | — | — | — | — |
| 2023 | 23 | AA | Binghamton Rumble Ponies | — | 404 | 349 | 86 | 25 | 1 | 10 | 42 | 124 | 8 | 4 | 1 | 246 | 341 | 410 | 751 | 163 | 345 | 31 | 10 | 334 | 73 | — | — | — | — | — | — |
| 2023 | 23 | INTL | Cardenales de Lara | — | 24 | 20 | 2 | 1 | 0 | 1 | 4 | 8 | 0 | 0 | 2 | 100 | 250 | 300 | 550 | 200 | 91 | 33 | 17 | 256 | 60 | — | — | — | — | — | — |
| 2024 | 24 | AA | Binghamton Rumble Ponies | — | 82 | 70 | 17 | 3 | 0 | 2 | 10 | 27 | 1 | 0 | 0 | 243 | 346 | 371 | 717 | 129 | 357 | 33 | 12 | 324 | 78 | — | — | — | — | — | — |
| ▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents) | |||||||||||||||||||||||||||||||
| 2017 | 17 | ROK MLE | GCL Mets | — | 251 | 236 | 48 | 14 | 1 | 1 | 12 | 91 | 0 | 7 | 4 | 203 | 242 | 284 | 526 | 81 | 310 | 36 | 5 | 229 | 41 | — | — | — | — | — | — |
| 2018 | 18 | ROK MLE | GCL Mets | — | 95 | 90 | 12 | 2 | 1 | 0 | 5 | 36 | 0 | 2 | 2 | 133 | 179 | 178 | 357 | 44 | 210 | 38 | 5 | 154 | -10 | — | — | — | — | — | — |
| 2019 | 19 | ROK MLE | GCL Mets | — | 72 | 65 | 14 | 4 | 1 | 4 | 3 | 22 | 3 | 1 | 0 | 215 | 282 | 492 | 774 | 277 | 255 | 31 | 4 | 339 | 116 | — | — | — | — | — | — |
| 2021 | 21 | A+ MLE | Brooklyn Cyclones | — | 150 | 137 | 26 | 6 | 0 | 4 | 9 | 57 | 2 | 1 | 0 | 190 | 250 | 321 | 571 | 131 | 273 | 38 | 6 | 254 | 58 | — | — | — | — | — | — |
| 2021 | 21 | A MLE | St. Lucie Mets | — | 278 | 241 | 49 | 12 | 1 | 5 | 27 | 96 | 9 | 5 | 0 | 203 | 307 | 324 | 631 | 120 | 308 | 35 | 10 | 283 | 78 | — | — | — | — | — | — |
| 2022 | 22 | A+ MLE | Brooklyn Cyclones | — | 459 | 418 | 90 | 17 | 2 | 8 | 28 | 145 | 7 | 4 | 1 | 215 | 276 | 323 | 599 | 108 | 294 | 32 | 6 | 260 | 62 | — | — | — | — | — | — |
| 2023 | 23 | AAA MLE | Syracuse Mets | — | 38 | 32 | 9 | 1 | 1 | 1 | 4 | 10 | 2 | 0 | 0 | 281 | 395 | 469 | 863 | 188 | 375 | 26 | 11 | 394 | 153 | — | — | — | — | — | — |
| 2023 | 23 | AA MLE | Binghamton Rumble Ponies | — | 404 | 362 | 76 | 20 | 1 | 8 | 29 | 151 | 8 | 4 | 1 | 210 | 283 | 337 | 620 | 127 | 320 | 37 | 7 | 276 | 73 | — | — | — | — | — | — |
| 2024 | 24 | AA MLE | Binghamton Rumble Ponies | — | 82 | 73 | 17 | 3 | 0 | 2 | 7 | 31 | 1 | 0 | 0 | 233 | 309 | 356 | 665 | 123 | 347 | 38 | 9 | 284 | 78 | — | — | — | — | — | — |
| 2017 | 17 | ROK MLE | GCL Mets | — | 251 | 236 | 48 | 14 | 1 | 1 | 12 | 91 | 0 | 7 | 4 | 203 | 242 | 284 | 526 | 81 | 322 | 36 | 5 | 229 | 41 | — | — | — | — | — | — |
| 2017 | 17 | ROK RAW | GCL Mets | — | 251 | 230 | 67 | 21 | 1 | 2 | 18 | 63 | 0 | 7 | 4 | 291 | 343 | 417 | 760 | 126 | 389 | 25 | 7 | 336 | 41 | — | — | — | — | — | — |
| 2018 | 18 | ROK MLE | GCL Mets | — | 95 | 90 | 12 | 2 | 1 | 0 | 5 | 36 | 0 | 2 | 2 | 133 | 179 | 178 | 357 | 44 | 222 | 38 | 5 | 154 | -10 | — | — | — | — | — | — |
| 2018 | 18 | ROK RAW | GCL Mets | — | 95 | 87 | 16 | 3 | 1 | 0 | 8 | 25 | 0 | 2 | 2 | 184 | 253 | 241 | 494 | 57 | 258 | 26 | 8 | 231 | -10 | — | — | — | — | — | — |
| 2019 | 19 | ROK MLE | GCL Mets | — | 72 | 65 | 14 | 4 | 1 | 4 | 3 | 22 | 3 | 1 | 0 | 215 | 282 | 492 | 774 | 277 | 250 | 31 | 4 | 339 | 116 | — | — | — | — | — | — |
| 2019 | 19 | ROK RAW | GCL Mets | — | 72 | 64 | 21 | 6 | 2 | 6 | 4 | 15 | 3 | 1 | 0 | 328 | 394 | 766 | 1.160 | 438 | 341 | 21 | 6 | 483 | 116 | — | — | — | — | — | — |
| 2021 | 21 | A MLE | 2 teams | — | 428 | 378 | 76 | 18 | 1 | 10 | 36 | 153 | 11 | 6 | 0 | 201 | 289 | 333 | 623 | 132 | 303 | 36 | 8 | 273 | 71 | — | — | — | — | — | — |
| 2021 | 21 | A RAW | 2 teams | — | 428 | 359 | 91 | 22 | 1 | 12 | 54 | 111 | 11 | 6 | 0 | 253 | 367 | 421 | 788 | 167 | 331 | 26 | 13 | 353 | 71 | — | — | — | — | — | — |
| 2022 | 22 | A+ MLE | Brooklyn Cyclones | — | 459 | 418 | 90 | 17 | 2 | 8 | 28 | 145 | 7 | 4 | 1 | 215 | 276 | 323 | 599 | 108 | 304 | 32 | 6 | 260 | 62 | — | — | — | — | — | — |
| 2022 | 22 | A+ RAW | Brooklyn Cyclones | — | 459 | 398 | 108 | 23 | 2 | 8 | 48 | 110 | 7 | 4 | 1 | 271 | 360 | 399 | 759 | 128 | 351 | 24 | 10 | 340 | 62 | — | — | — | — | — | — |
| 2023 | 23 | AA MLE | 3 teams | — | 466 | 416 | 90 | 22 | 2 | 10 | 35 | 170 | 10 | 4 | 3 | 216 | 293 | 351 | 644 | 135 | 332 | 36 | 8 | 286 | 80 | — | — | — | — | — | — |
| 2023 | 23 | AA RAW | 3 teams | — | 466 | 399 | 98 | 27 | 2 | 13 | 52 | 140 | 10 | 4 | 3 | 246 | 347 | 421 | 768 | 175 | 339 | 30 | 11 | 342 | 80 | — | — | — | — | — | — |
| 2024 | 24 | AA MLE | Binghamton Rumble Ponies | — | 82 | 73 | 17 | 3 | 0 | 2 | 7 | 31 | 1 | 0 | 0 | 233 | 309 | 356 | 665 | 123 | 366 | 38 | 9 | 284 | 78 | — | — | — | — | — | — |
| 2024 | 24 | AA RAW | Binghamton Rumble Ponies | — | 82 | 70 | 17 | 3 | 0 | 2 | 10 | 27 | 1 | 0 | 0 | 243 | 346 | 371 | 717 | 129 | 357 | 33 | 12 | 324 | 78 | — | — | — | — | — | — |
| 2017 | 17 | ROK MLE | GCL Mets | — | 251 | 237 | 48 | 14 | 1 | 1 | 12 | 91 | 0 | 7 | 4 | 203 | 242 | 283 | 525 | 80 | 310 | 36 | 5 | 229 | 41 | — | — | — | — | — | — |
| 2018 | 18 | ROK MLE | GCL Mets | — | 95 | 90 | 12 | 2 | 1 | 0 | 5 | 36 | 0 | 2 | 2 | 133 | 179 | 178 | 357 | 44 | 210 | 38 | 5 | 154 | -10 | — | — | — | — | — | — |
| 2019 | 19 | ROK MLE | GCL Mets | — | 72 | 66 | 14 | 4 | 1 | 4 | 3 | 22 | 3 | 1 | 0 | 212 | 282 | 485 | 767 | 273 | 255 | 31 | 4 | 339 | 116 | — | — | — | — | — | — |
| 2021 | 21 | A MLE | 2 teams | — | 428 | 378 | 76 | 18 | 1 | 10 | 36 | 153 | 11 | 6 | 0 | 201 | 289 | 333 | 623 | 132 | 296 | 36 | 8 | 273 | 71 | — | — | — | — | — | — |
| 2022 | 22 | A+ MLE | Brooklyn Cyclones | — | 459 | 421 | 90 | 17 | 2 | 8 | 28 | 145 | 7 | 4 | 1 | 214 | 276 | 321 | 597 | 107 | 294 | 32 | 6 | 260 | 62 | — | — | — | — | — | — |
| 2023 | 23 | AA MLE | 3 teams | — | 466 | 418 | 90 | 22 | 2 | 10 | 35 | 170 | 10 | 4 | 3 | 215 | 293 | 349 | 642 | 134 | 325 | 36 | 8 | 286 | 80 | — | — | — | — | — | — |
| 2024 | 24 | AA MLE | Binghamton Rumble Ponies | — | 82 | 74 | 17 | 3 | 0 | 2 | 7 | 31 | 1 | 0 | 0 | 230 | 309 | 351 | 660 | 122 | 347 | 38 | 9 | 284 | 78 | — | — | — | — | — | — |
| ▸ Model inputs — neutral MLE (past 3 seasons) | |||||||||||||||||||||||||||||||
| 2024 | 24 | AA MLE | Binghamton Rumble Ponies | — | 82 | 74 | 17 | 3 | 0 | 2 | 7 | 31 | 1 | 0 | 0 | 230 | 309 | 351 | 660 | 122 | 347 | 38 | 9 | 284 | 78 | — | — | — | — | — | — |
| ▸ Career Totals | |||||||||||||||||||||||||||||||
| Career | — | MLE | — | 1853 | 1676 | 347 | 80 | 8 | 35 | 126 | 648 | 32 | 24 | 10 | 207 | 275 | 327 | 602 | 120 | 309 | 35 | 7 | 264 | 65 | -78 | +1 | +0 | +7 | +40 | -3.1 | |
| / 162G | — | MLE | — | 650 | 588 | 122 | 28 | 3 | 12 | 44 | 227 | 11 | 8 | 4 | 207 | 275 | 327 | 602 | 119 | 310 | 35 | 7 | 264 | 65 | -27 | +0 | +0 | +2 | +14 | -1.1 | |
How the minor-league lines were translated
One row per minor-league stint. Open a row to see each step: the raw rate, the park adjustment, the league/level factor, the age adjustment (hitters) and the resulting MLE rate. Factors are the same ones listed on the MLE Factors page.
2024 AABinghamton Rumble Ponies · 82 PAwOBA .320 → .284 (−36)K% 32.9% → 38.4%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 32.93% | ÷1.046 | ×1.220 | — | — | 38.39% |
| BB% (unintentional) /PA | 12.20% | ÷0.996 | ×0.700 | — | ×1.012 | 8.67% |
| HBP% /PA | 1.22% | — | ×0.850 | — | ×1.012 | 1.05% |
| Singles /BIP | 27.91% | ÷0.992 | ×0.970 | — | ×1.012 | 27.61% |
| Doubles /BIP | 6.98% | ÷0.954 | ×0.860 | — | ×1.012 | 6.37% |
| Triples /BIP | 0.00% | ÷1.062 | ×0.950 | — | ×1.012 | 0.00% |
| Home runs /BIP | 4.65% | ÷0.834 | ×0.780 | — | ×1.012 | 4.40% |
Factors used: league row EL. Park (home-season share, regressed): HR 67, 1B 98, 2B 91, 3B 112, K 109, BB 99 — applied at half strength. Age: age 24 vs a level average of 24.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average).
Result: wOBA .320 → .284; ISO .129 → .119; BABIP .357 → .347. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).
2023 AABinghamton Rumble Ponies · 404 PAwOBA .329 → .276 (−52)K% 30.7% → 37.4%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 30.69% | ÷1.002 | ×1.220 | — | — | 37.38% |
| BB% (unintentional) /PA | 10.15% | ÷1.015 | ×0.700 | — | ×1.027 | 7.19% |
| HBP% /PA | 1.98% | — | ×0.850 | — | ×1.027 | 1.73% |
| Singles /BIP | 22.22% | ÷0.993 | ×0.970 | ×0.980 | ×1.027 | 21.85% |
| Doubles /BIP | 11.11% | ÷1.032 | ×0.860 | ×0.980 | ×1.027 | 9.33% |
| Triples /BIP | 0.44% | ÷1.150 | ×0.950 | ×0.980 | ×1.027 | 0.37% |
| Home runs /BIP | 4.44% | ÷0.925 | ×0.780 | — | ×1.027 | 3.85% |
Factors used: league row EL. Park (home-season share, regressed): HR 85, 1B 99, 2B 106, 3B 130, K 100, BB 103 — applied at half strength. Age: age 23 vs a level average of 24.8 → +2.7% on BB/HBP/hit rates (1.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.980.
Result: wOBA .329 → .276; ISO .163 → .134; BABIP .345 → .320. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).
2023 AAASyracuse Mets · 38 PAwOBA .465 → .394 (−72)K% 21.1% → 26.3%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 21.05% | ÷0.961 | ×1.200 | — | — | 26.30% |
| BB% (unintentional) /PA | 15.79% | ÷0.967 | ×0.690 | — | ×1.050 | 11.83% |
| HBP% /PA | 5.26% | — | ×0.940 | — | ×1.050 | 5.19% |
| Singles /BIP | 27.27% | ÷0.997 | ×0.930 | — | ×1.050 | 26.71% |
| Doubles /BIP | 4.55% | ÷0.933 | ×0.830 | — | ×1.050 | 4.25% |
| Triples /BIP | 4.55% | ÷1.150 | ×0.970 | — | ×1.050 | 4.03% |
| Home runs /BIP | 9.09% | ÷1.003 | ×0.700 | — | ×1.050 | 6.66% |
Factors used: league row International League. Park (home-season share, regressed): HR 101, 1B 99, 2B 87, 3B 130, K 92, BB 93 — applied at half strength. Age: age 23 vs a level average of 27.2 → +5.0% on BB/HBP/hit rates (4.2 yr younger than average).
Result: wOBA .465 → .394; ISO .300 → .232; BABIP .400 → .375. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).
2022 A+Brooklyn Cyclones · 459 PAwOBA .334 → .260 (−74)K% 24.0% → 31.6%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 23.97% | ÷0.995 | ×1.310 | — | — | 31.56% |
| BB% (unintentional) /PA | 10.24% | ÷1.020 | ×0.600 | — | ×1.012 | 6.10% |
| HBP% /PA | 1.53% | — | ×0.770 | — | ×1.012 | 1.19% |
| Singles /BIP | 26.04% | ÷1.026 | ×0.870 | ×1.003 | ×1.012 | 22.43% |
| Doubles /BIP | 7.99% | ÷1.030 | ×0.780 | ×1.003 | ×1.012 | 6.14% |
| Triples /BIP | 0.69% | ÷1.060 | ×0.850 | ×1.003 | ×1.012 | 0.57% |
| Home runs /BIP | 2.78% | ÷0.925 | ×0.880 | — | ×1.012 | 2.67% |
Factors used: league row South Atlantic League. Park (home-season share, regressed): HR 85, 1B 105, 2B 106, 3B 112, K 99, BB 104 — applied at half strength. Age: age 22 vs a level average of 22.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.003.
Result: wOBA .334 → .260; ISO .128 → .108; BABIP .351 → .294. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).
2021 ASt. Lucie Mets · 278 PAwOBA .372 → .283 (−89)K% 24.1% → 34.7%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 24.10% | ÷0.945 | ×1.360 | — | — | 34.68% |
| BB% (unintentional) /PA | 14.39% | ÷1.041 | ×0.690 | — | ×1.015 | 9.68% |
| HBP% /PA | 3.24% | — | ×0.780 | — | ×1.015 | 2.56% |
| Singles /BIP | 24.53% | ÷1.028 | ×0.920 | ×0.957 | ×1.015 | 21.30% |
| Doubles /BIP | 9.43% | ÷0.935 | ×0.830 | ×0.957 | ×1.015 | 8.13% |
| Triples /BIP | 0.63% | ÷1.137 | ×0.910 | ×0.957 | ×1.015 | 0.49% |
| Home runs /BIP | 4.40% | ÷0.988 | ×0.800 | — | ×1.015 | 3.62% |
Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 98, 1B 106, 2B 87, 3B 127, K 89, BB 108 — applied at half strength. Age: age 21 vs a level average of 22.0 → +1.5% on BB/HBP/hit rates (1.0 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.957.
Result: wOBA .372 → .283; ISO .168 → .130; BABIP .359 → .308. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).
2021 A+Brooklyn Cyclones · 150 PAwOBA .298 → .254 (−44)K% 29.3% → 38.0%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 29.33% | ÷1.041 | ×1.350 | — | — | 38.05% |
| BB% (unintentional) /PA | 8.67% | ÷1.006 | ×0.710 | — | ×1.027 | 6.28% |
| HBP% /PA | 1.33% | — | ×0.810 | — | ×1.027 | 1.11% |
| Singles /BIP | 19.10% | ÷0.957 | ×0.910 | ×1.027 | ×1.027 | 19.16% |
| Doubles /BIP | 7.87% | ÷0.930 | ×0.830 | ×1.027 | ×1.027 | 7.40% |
| Triples /BIP | 0.00% | ÷1.028 | ×0.820 | ×1.027 | ×1.027 | 0.00% |
| Home runs /BIP | 5.62% | ÷0.925 | ×0.830 | — | ×1.027 | 5.18% |
Factors used: level-wide A+ factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 85, 1B 91, 2B 86, 3B 106, K 108, BB 101 — applied at half strength. Age: age 21 vs a level average of 22.8 → +2.7% on BB/HBP/hit rates (1.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.027.
Result: wOBA .298 → .254; ISO .165 → .141; BABIP .279 → .273. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).
2019 ROKGCL Mets · 72 PAwOBA .475 → .339 (−136)K% 20.8% → 30.0%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 20.83% | ÷1.000 | ×1.440 | — | — | 30.00% |
| BB% (unintentional) /PA | 5.56% | ÷1.000 | ×0.620 | — | ×1.008 | 3.47% |
| HBP% /PA | 4.17% | — | ×0.720 | — | ×1.008 | 3.02% |
| Singles /BIP | 14.29% | ÷1.000 | ×0.820 | — | ×1.008 | 11.80% |
| Doubles /BIP | 12.24% | ÷1.000 | ×0.720 | — | ×1.008 | 8.88% |
| Triples /BIP | 4.08% | ÷1.000 | ×0.720 | — | ×1.008 | 2.96% |
| Home runs /BIP | 12.24% | ÷1.000 | ×0.780 | — | ×1.008 | 9.62% |
Factors used: level-wide ROK factors (no league-specific row for this league/era). Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 19 vs a level average of 19.5 → +0.8% on BB/HBP/hit rates (0.5 yr younger than average).
Result: wOBA .475 → .339; ISO .438 → .312; BABIP .341 → .255. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).
2018 ROKGCL Mets · 95 PAwOBA .225 → .154 (−71)K% 26.3% → 37.9%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 26.32% | ÷1.000 | ×1.440 | — | — | 37.89% |
| BB% (unintentional) /PA | 8.42% | ÷1.000 | ×0.620 | — | ×1.022 | 5.34% |
| HBP% /PA | 0.00% | — | ×0.720 | — | ×1.022 | 0.00% |
| Singles /BIP | 19.35% | ÷1.000 | ×0.820 | ×1.000 | ×1.022 | 16.23% |
| Doubles /BIP | 4.84% | ÷1.000 | ×0.720 | ×1.000 | ×1.022 | 3.56% |
| Triples /BIP | 1.61% | ÷1.000 | ×0.720 | ×1.000 | ×1.022 | 1.19% |
| Home runs /BIP | 0.00% | ÷1.000 | ×0.780 | — | ×1.022 | 0.00% |
Factors used: level-wide ROK factors (no league-specific row for this league/era). Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 18 vs a level average of 19.5 → +2.2% on BB/HBP/hit rates (1.5 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.000.
Result: wOBA .225 → .154; ISO .057 → .037; BABIP .258 → .210. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).
Not traceable per stint: some stints lack the counting stats the component translation needs. Level-wide factors that apply instead — .