Connor Panas
| Year | Age | Lvl / Team | PA | AVG/OBP/SLG | HR | SB | wRC+ | WAR | MLE AVG/OBP/SLG |
|---|---|---|---|---|---|---|---|---|---|
| 2018 | 25 | AATOR | 407 | .232/.296/.359 | 9 | 4 | 55 | — | .209/.262/.312 |
| 2019 | 26 | AAA/A+SD | 125 | .224/.336/.421 | 4 | 2 | 60 | — | .162/.256/.305 |
| 2023 | 30 | INTLBrisbane Band… | 50 | .317/.440/.659 | 3 | 0 | 204 | — |
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) | |||||||||||||||||||||||||||||||
| 2015 | 22 | ROK | GCL Blue Jays | — | 26 | 23 | 9 | 1 | 0 | 1 | 2 | 3 | 1 | 1 | 0 | 391 | 462 | 565 | 1.027 | 174 | 421 | 12 | 8 | 455 | 109 | — | — | — | — | — | — |
| 2016 | 23 | A | Lansing Lugnuts | — | 383 | 321 | 74 | 10 | 3 | 16 | 45 | 97 | 12 | 3 | 4 | 231 | 347 | 430 | 776 | 199 | 274 | 25 | 12 | 344 | 57 | — | — | — | — | — | — |
| 2017 | 24 | A+ | Dunedin Blue Jays | — | 459 | 402 | 111 | 20 | 3 | 18 | 41 | 99 | 15 | 4 | 0 | 276 | 365 | 475 | 840 | 199 | 325 | 22 | 9 | 372 | 73 | — | — | — | — | — | — |
| 2017 | 24 | INTL | Canberra Cavalry | — | 84 | 73 | 18 | 4 | 1 | 5 | 5 | 11 | 5 | 1 | 0 | 247 | 337 | 534 | 872 | 288 | 224 | 13 | 6 | 376 | 140 | — | — | — | — | — | — |
| 2018 | 25 | AA | New Hampshire Fisher Cats | — | 407 | 370 | 86 | 16 | 2 | 9 | 25 | 84 | 9 | 4 | 3 | 232 | 296 | 359 | 656 | 127 | 276 | 21 | 6 | 295 | 55 | — | — | — | — | — | — |
| 2019 | 26 | AAA | El Paso Chihuahuas | — | 33 | 30 | 8 | 2 | 0 | 3 | 3 | 7 | 0 | 0 | 0 | 267 | 333 | 633 | 967 | 367 | 250 | 21 | 9 | 413 | 95 | — | — | — | — | — | — |
| 2019 | 26 | A+ | Lake Elsinore Storm | — | 92 | 77 | 16 | 3 | 2 | 1 | 10 | 17 | 5 | 2 | 0 | 208 | 337 | 338 | 675 | 130 | 254 | 18 | 11 | 314 | 48 | — | — | — | — | — | — |
| 2023 | 30 | INTL | Brisbane Bandits | — | 50 | 41 | 13 | 3 | 1 | 3 | 7 | 6 | 2 | 0 | 0 | 317 | 440 | 659 | 1.099 | 341 | 312 | 12 | 14 | 470 | 204 | — | — | — | — | — | — |
| ▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents) | |||||||||||||||||||||||||||||||
| 2015 | 22 | ROK MLE | GCL Blue Jays | — | 26 | 24 | 8 | 1 | 0 | 1 | 1 | 4 | 1 | 1 | 0 | 333 | 385 | 500 | 885 | 167 | 327 | 15 | 4 | 330 | 109 | — | — | — | — | — | — |
| 2016 | 23 | A MLE | Lansing Lugnuts | — | 383 | 336 | 57 | 7 | 3 | 12 | 30 | 140 | 12 | 3 | 4 | 170 | 262 | 315 | 577 | 146 | 229 | 37 | 8 | 253 | 57 | — | — | — | — | — | — |
| 2017 | 24 | A+ MLE | Dunedin Blue Jays | — | 459 | 416 | 87 | 18 | 2 | 11 | 27 | 140 | 15 | 4 | 0 | 209 | 282 | 341 | 623 | 132 | 284 | 31 | 6 | 276 | 73 | — | — | — | — | — | — |
| 2018 | 25 | AA MLE | New Hampshire Fisher Cats | — | 407 | 378 | 79 | 14 | 2 | 7 | 18 | 99 | 9 | 4 | 3 | 209 | 262 | 312 | 574 | 103 | 255 | 24 | 4 | 249 | 55 | — | — | — | — | — | — |
| 2019 | 26 | AAA MLE | El Paso Chihuahuas | — | 33 | 31 | 5 | 1 | 0 | 2 | 2 | 9 | 0 | 0 | 0 | 161 | 212 | 387 | 599 | 226 | 207 | 27 | 6 | 309 | 95 | — | — | — | — | — | — |
| 2019 | 26 | A+ MLE | Lake Elsinore Storm | — | 92 | 80 | 13 | 2 | 2 | 1 | 7 | 23 | 5 | 2 | 0 | 163 | 272 | 275 | 547 | 113 | 210 | 25 | 8 | 239 | 48 | — | — | — | — | — | — |
| 2015 | 22 | ROK MLE | GCL Blue Jays | — | 26 | 24 | 8 | 1 | 0 | 1 | 1 | 4 | 1 | 1 | 0 | 333 | 385 | 500 | 885 | 167 | 368 | 15 | 4 | 330 | 109 | — | — | — | — | — | — |
| 2015 | 22 | ROK RAW | GCL Blue Jays | — | 26 | 23 | 9 | 1 | 0 | 1 | 2 | 3 | 1 | 1 | 0 | 391 | 462 | 565 | 1.027 | 174 | 421 | 12 | 8 | 455 | 109 | — | — | — | — | — | — |
| 2016 | 23 | A MLE | Lansing Lugnuts | — | 383 | 336 | 57 | 7 | 3 | 12 | 30 | 140 | 12 | 3 | 4 | 170 | 262 | 315 | 577 | 146 | 239 | 37 | 8 | 253 | 57 | — | — | — | — | — | — |
| 2016 | 23 | A RAW | Lansing Lugnuts | — | 383 | 321 | 74 | 10 | 3 | 16 | 45 | 97 | 12 | 3 | 4 | 231 | 347 | 430 | 776 | 199 | 274 | 25 | 12 | 344 | 57 | — | — | — | — | — | — |
| 2017 | 24 | A+ MLE | 2 teams | — | 543 | 489 | 103 | 21 | 2 | 13 | 32 | 165 | 20 | 5 | 0 | 211 | 287 | 342 | 628 | 131 | 288 | 30 | 6 | 276 | 73 | — | — | — | — | — | — |
| 2017 | 24 | A+ RAW | 2 teams | — | 543 | 475 | 129 | 24 | 4 | 23 | 46 | 110 | 20 | 5 | 0 | 272 | 360 | 484 | 845 | 213 | 308 | 20 | 8 | 373 | 73 | — | — | — | — | — | — |
| 2018 | 25 | AA MLE | New Hampshire Fisher Cats | — | 407 | 378 | 79 | 14 | 2 | 7 | 18 | 99 | 9 | 4 | 3 | 209 | 262 | 312 | 574 | 103 | 263 | 24 | 4 | 249 | 55 | — | — | — | — | — | — |
| 2018 | 25 | AA RAW | New Hampshire Fisher Cats | — | 407 | 370 | 86 | 16 | 2 | 9 | 25 | 84 | 9 | 4 | 3 | 232 | 296 | 359 | 656 | 127 | 276 | 21 | 6 | 295 | 55 | — | — | — | — | — | — |
| 2019 | 26 | A+ MLE | 2 teams | — | 125 | 111 | 20 | 4 | 2 | 3 | 9 | 32 | 5 | 2 | 0 | 180 | 272 | 333 | 605 | 153 | 224 | 26 | 7 | 257 | 60 | — | — | — | — | — | — |
| 2019 | 26 | A+ RAW | 2 teams | — | 125 | 107 | 24 | 5 | 2 | 4 | 13 | 24 | 5 | 2 | 0 | 224 | 336 | 421 | 757 | 196 | 253 | 19 | 10 | 340 | 60 | — | — | — | — | — | — |
| 2023 | 30 | INTL MLE | Brisbane Bandits | — | 50 | 41 | 13 | 3 | 1 | 3 | 7 | 6 | 2 | 0 | 0 | 317 | 440 | 659 | 1.099 | 341 | 312 | 12 | 14 | — | — | — | — | — | — | — | — |
| 2023 | 30 | INTL RAW | Brisbane Bandits | — | 50 | 41 | 13 | 3 | 1 | 3 | 7 | 6 | 2 | 0 | 0 | 317 | 440 | 659 | 1.099 | 341 | 312 | 12 | 14 | 470 | 204 | — | — | — | — | — | — |
| 2015 | 22 | ROK MLE | GCL Blue Jays | — | 26 | 24 | 8 | 1 | 0 | 1 | 1 | 4 | 1 | 1 | 0 | 333 | 385 | 500 | 885 | 167 | 327 | 15 | 4 | 330 | 109 | — | — | — | — | — | — |
| 2016 | 23 | A MLE | Lansing Lugnuts | — | 383 | 338 | 57 | 7 | 3 | 12 | 30 | 140 | 12 | 3 | 4 | 169 | 262 | 314 | 576 | 145 | 229 | 37 | 8 | 253 | 57 | — | — | — | — | — | — |
| 2017 | 24 | A+ MLE | 2 teams | — | 543 | 486 | 103 | 21 | 2 | 13 | 32 | 165 | 20 | 5 | 0 | 212 | 287 | 344 | 630 | 132 | 284 | 30 | 6 | 276 | 73 | — | — | — | — | — | — |
| 2018 | 25 | AA MLE | New Hampshire Fisher Cats | — | 407 | 377 | 79 | 14 | 2 | 7 | 18 | 99 | 9 | 4 | 3 | 210 | 262 | 313 | 575 | 103 | 255 | 24 | 4 | 249 | 55 | — | — | — | — | — | — |
| 2019 | 26 | A+ MLE | 2 teams | — | 125 | 110 | 20 | 4 | 2 | 3 | 9 | 32 | 5 | 2 | 0 | 182 | 272 | 336 | 608 | 155 | 209 | 26 | 7 | 257 | 60 | — | — | — | — | — | — |
| ▸ Career Totals | |||||||||||||||||||||||||||||||
| Career | — | MLE | — | 1534 | 1379 | 280 | 50 | 10 | 39 | 97 | 446 | 49 | 15 | 7 | 203 | 279 | 339 | 618 | 136 | 267 | 29 | 6 | 254 | 58 | -77 | +0 | +0 | -16 | +33 | -6.1 | |
| / 162G | — | MLE | — | 650 | 584 | 119 | 21 | 4 | 17 | 41 | 189 | 21 | 6 | 3 | 204 | 280 | 341 | 621 | 137 | 268 | 29 | 6 | 254 | 58 | -33 | +0 | +0 | -7 | +14 | -2.6 | |
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.
2019 A+Lake Elsinore Storm · 92 PAwOBA .306 → .239 (−67)K% 18.5% → 25.1%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 18.48% | ÷0.987 | ×1.340 | — | — | 25.09% |
| BB% (unintentional) /PA | 10.87% | ÷0.977 | ×0.710 | — | ×0.968 | 7.65% |
| HBP% /PA | 5.43% | — | ×0.790 | — | ×0.968 | 4.16% |
| Singles /BIP | 16.67% | ÷0.990 | ×0.910 | ×0.950 | ×0.968 | 14.08% |
| Doubles /BIP | 5.00% | ÷0.940 | ×0.820 | ×0.950 | ×0.968 | 4.01% |
| Triples /BIP | 3.33% | ÷0.900 | ×0.760 | ×0.950 | ×0.968 | 2.59% |
| Home runs /BIP | 1.67% | ÷0.900 | ×0.920 | — | ×0.968 | 1.65% |
Factors used: league row CAL. Park (home-season share, regressed): HR 80, 1B 98, 2B 88, 3B 80, K 97, BB 95 — applied at half strength. Age: age 26 vs a level average of 22.8 → −3.2% on BB/HBP/hit rates (3.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.
Result: wOBA .306 → .239; ISO .130 → .107; BABIP .254 → .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).
2019 AAAEl Paso Chihuahuas · 33 PAwOBA .400 → .309 (−92)K% 21.2% → 28.4%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 21.21% | ÷0.965 | ×1.290 | — | — | 28.35% |
| BB% (unintentional) /PA | 9.09% | ÷1.050 | ×0.710 | — | ×1.018 | 6.26% |
| HBP% /PA | 0.00% | — | ×0.980 | — | ×1.018 | 0.00% |
| Singles /BIP | 13.04% | ÷1.033 | ×0.900 | — | ×1.018 | 11.57% |
| Doubles /BIP | 8.70% | ÷1.044 | ×0.810 | — | ×1.018 | 6.87% |
| Triples /BIP | 0.00% | ÷0.900 | ×0.810 | — | ×1.018 | 0.00% |
| Home runs /BIP | 13.04% | ÷0.950 | ×0.780 | — | ×1.018 | 10.90% |
Factors used: league row PCL. Park (home-season share, regressed): HR 90, 1B 107, 2B 109, 3B 80, K 93, BB 110 — applied at half strength. Age: age 26 vs a level average of 27.2 → +1.8% on BB/HBP/hit rates (1.2 yr younger than average).
Result: wOBA .400 → .309; ISO .367 → .285; BABIP .250 → .207. 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 AANew Hampshire Fisher Cats · 407 PAwOBA .287 → .249 (−38)K% 20.6% → 24.4%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 20.64% | ÷1.033 | ×1.220 | — | — | 24.37% |
| BB% (unintentional) /PA | 5.90% | ÷0.961 | ×0.700 | — | ×0.998 | 4.29% |
| HBP% /PA | 2.21% | — | ×0.850 | — | ×0.998 | 1.88% |
| Singles /BIP | 20.63% | ÷0.995 | ×0.970 | ×0.980 | ×0.998 | 19.67% |
| Doubles /BIP | 5.59% | ÷0.980 | ×0.860 | ×0.980 | ×0.998 | 4.80% |
| Triples /BIP | 0.70% | ÷1.086 | ×0.950 | ×0.980 | ×0.998 | 0.60% |
| Home runs /BIP | 3.15% | ÷0.945 | ×0.780 | — | ×0.998 | 2.59% |
Factors used: league row EL. Park (home-season share, regressed): HR 89, 1B 99, 2B 96, 3B 117, K 107, BB 92 — applied at half strength. Age: age 25 vs a level average of 24.8 → −0.2% on BB/HBP/hit rates (0.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.980.
Result: wOBA .287 → .249; ISO .127 → .105; BABIP .276 → .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).
2017 A+Dunedin Blue Jays · 459 PAwOBA .363 → .276 (−88)K% 21.6% → 30.4%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 21.57% | ÷1.007 | ×1.420 | — | — | 30.42% |
| BB% (unintentional) /PA | 8.93% | ÷1.118 | ×0.750 | — | ×0.988 | 5.92% |
| HBP% /PA | 3.27% | — | ×0.930 | — | ×0.988 | 3.00% |
| Singles /BIP | 23.10% | ÷1.002 | ×0.920 | ×0.966 | ×0.988 | 20.24% |
| Doubles /BIP | 6.60% | ÷0.940 | ×0.960 | ×0.966 | ×0.988 | 6.43% |
| Triples /BIP | 0.99% | ÷1.150 | ×0.810 | ×0.966 | ×0.988 | 0.67% |
| Home runs /BIP | 5.94% | ÷1.265 | ×0.880 | — | ×0.988 | 4.08% |
Factors used: league row FSL. Park (home-season share, regressed): HR 153, 1B 100, 2B 88, 3B 130, K 101, BB 124 — applied at half strength. Age: age 24 vs a level average of 22.8 → −1.2% on BB/HBP/hit rates (1.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.966.
Result: wOBA .363 → .276; ISO .199 → .139; BABIP .325 → .284. 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).
2016 ALansing Lugnuts · 383 PAwOBA .337 → .253 (−84)K% 25.3% → 36.5%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 25.33% | ÷0.944 | ×1.360 | — | — | 36.49% |
| BB% (unintentional) /PA | 11.23% | ÷0.970 | ×0.690 | — | ×0.990 | 7.90% |
| HBP% /PA | 3.13% | — | ×0.780 | — | ×0.990 | 2.42% |
| Singles /BIP | 20.09% | ÷1.044 | ×0.920 | ×0.977 | ×0.990 | 17.12% |
| Doubles /BIP | 4.46% | ÷0.982 | ×0.830 | ×0.977 | ×0.990 | 3.65% |
| Triples /BIP | 1.34% | ÷0.918 | ×0.910 | ×0.977 | ×0.990 | 1.28% |
| Home runs /BIP | 7.14% | ÷0.968 | ×0.800 | — | ×0.990 | 5.84% |
Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 94, 1B 109, 2B 96, 3B 84, K 89, BB 94 — applied at half strength. Age: age 23 vs a level average of 22.0 → −1.0% on BB/HBP/hit rates (1.0 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.977.
Result: wOBA .337 → .253; ISO .199 → .151; BABIP .274 → .229. 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).
2015 ROKGCL Blue Jays · 26 PAwOBA .443 → .330 (−113)K% 11.5% → 16.6%
| Component | Raw | Park ÷ | Level factor × | GB/FB × | Age × | = MLE |
|---|---|---|---|---|---|---|
| K% /PA | 11.54% | ÷1.000 | ×1.440 | — | — | 16.62% |
| BB% (unintentional) /PA | 7.69% | ÷1.000 | ×0.620 | — | ×0.975 | 4.65% |
| HBP% /PA | 3.85% | — | ×0.720 | — | ×0.975 | 2.70% |
| Singles /BIP | 35.00% | ÷1.000 | ×0.820 | — | ×0.975 | 27.98% |
| Doubles /BIP | 5.00% | ÷1.000 | ×0.720 | — | ×0.975 | 3.51% |
| Triples /BIP | 0.00% | ÷1.000 | ×0.720 | — | ×0.975 | 0.00% |
| Home runs /BIP | 5.00% | ÷1.000 | ×0.780 | — | ×0.975 | 3.80% |
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 22 vs a level average of 19.5 → −2.5% on BB/HBP/hit rates (2.5 yr older than average).
Result: wOBA .443 → .330; ISO .174 → .128; BABIP .421 → .327. 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 — .