| Player |
In Play 횟수 |
실제 아웃수 |
예상된 아웃수 |
DER | Predicted DER | Difference |
|---|---|---|---|---|---|---|
| Reggie Sanders | 1942 | 170 | 150.73 | 0.088 | 0.078 | 0.00992 |
| Carlos J Quentin | 1156 | 96 | 85.83 | 0.083 | 0.074 | 0.00879 |
| Casey Blake | 2586 | 210 | 191.62 | 0.081 | 0.074 | 0.00711 |
| Damon J Hollins | 1440 | 134 | 124.64 | 0.093 | 0.087 | 0.00650 |
| Mark DeRosa | 1654 | 125 | 115.00 | 0.076 | 0.070 | 0.00605 |
| Kevin Mench | 1541 | 112 | 102.93 | 0.073 | 0.067 | 0.00588 |
| Ryan Freel | 1122 | 101 | 94.94 | 0.090 | 0.085 | 0.00540 |
| Jose Guillen | 1774 | 164 | 154.51 | 0.092 | 0.087 | 0.00535 |
| Jay Gibbons | 1107 | 97 | 91.66 | 0.088 | 0.083 | 0.00482 |
| J.D. Drew | 3472 | 284 | 267.61 | 0.082 | 0.077 | 0.00472 |
| Alex I Rios | 2862 | 218 | 205.27 | 0.076 | 0.072 | 0.00445 |
| Juan Encarnacion | 3085 | 219 | 208.81 | 0.071 | 0.068 | 0.00330 |
| Vladimir Guerrero | 3258 | 253 | 243.64 | 0.078 | 0.075 | 0.00287 |
| Emil Brown | 1349 | 110 | 106.51 | 0.082 | 0.079 | 0.00259 |
| Jacque Jones | 3476 | 275 | 266.55 | 0.079 | 0.077 | 0.00243 |
| Austin Kearns | 3928 | 346 | 337.89 | 0.088 | 0.086 | 0.00206 |
| Moises Alou | 2026 | 154 | 150.84 | 0.076 | 0.074 | 0.00156 |
| Russell Branyan | 1163 | 87 | 86.07 | 0.075 | 0.074 | 0.00080 |
| Bobby Abreu | 4047 | 293 | 292.60 | 0.072 | 0.072 | 0.00010 |
| Trot Nixon | 2700 | 212 | 211.95 | 0.079 | 0.079 | 0.00002 |
| Joe Borchard | 1060 | 84 | 84.06 | 0.079 | 0.079 | -0.00006 |
| Jeff B Francoeur | 4434 | 317 | 317.93 | 0.071 | 0.072 | -0.00021 |
| Brad B Hawpe | 3769 | 280 | 281.06 | 0.074 | 0.075 | -0.00028 |
| Jay Payton | 1173 | 89 | 89.42 | 0.076 | 0.076 | -0.00036 |
| Ichiro Suzuki | 3252 | 250 | 251.21 | 0.077 | 0.077 | -0.00037 |
| Shawn Green | 3393 | 220 | 222.29 | 0.065 | 0.066 | -0.00068 |
| Jason Lane | 2049 | 155 | 156.74 | 0.076 | 0.076 | -0.00085 |
| Randy Winn | 1996 | 184 | 185.72 | 0.092 | 0.093 | -0.00086 |
| Milton Bradley | 2518 | 191 | 194.41 | 0.076 | 0.077 | -0.00136 |
| Jermaine Dye | 3915 | 305 | 310.61 | 0.078 | 0.079 | -0.00143 |
| Nick Markakis | 2843 | 240 | 244.33 | 0.084 | 0.086 | -0.00152 |
| Geoff Jenkins | 3333 | 247 | 254.04 | 0.074 | 0.076 | -0.00211 |
| Michael Cuddyer | 3637 | 245 | 259.18 | 0.067 | 0.071 | -0.00390 |
| Jeromy Burnitz | 1988 | 120 | 128.64 | 0.060 | 0.065 | -0.00435 |
| Bernie Williams | 1347 | 98 | 104.01 | 0.073 | 0.077 | -0.00446 |
| Jeremy R Hermida | 2003 | 157 | 166.44 | 0.078 | 0.083 | -0.00471 |
| Xavier Nady | 2560 | 187 | 202.29 | 0.073 | 0.079 | -0.00597 |
| Magglio Ordonez | 3893 | 258 | 281.26 | 0.066 | 0.072 | -0.00598 |
| Brian Giles | 4169 | 298 | 332.48 | 0.071 | 0.080 | -0.00827 |
☞ It seems every year I run the PMR for rightfielders I encounter the same problem, and it has to do with Ichiro Suzuki:
That's right, Ichiro is very slightly negative (actually, I'd call him neutral). But people who watch him disagree with this finding.He ranks at the top in centerfield, indicating he can chase down balls.
My belief is that Ichiro plays deep in rightfield to take away the long hits. He's making a tradeoff between catching balls that might go as doubles, triples or home runs and giving up short singles that a fielder playing at normal depth levels would catch. When he goes to center, he plays more conservatively there since he's not used to the position, but in right he takes chances.
One suggestion over the time I've presented this data is to use the actual distance of balls rather than the velocity of the ball as a parameter for outfielders. I've always felt velocity was a pretty good proxy for distance, and it allowed me to have the same model for infielders and outfielders. But I thought of a way to incorporate the distance without changing the model. I simply divide the distance by 100, except on ground balls and low line drives. Basically, on balls that infielder have a chance to field, use velocity. On balls that are too high for them to field, use distance. Here's a table using a model that mixes the two.
☞ 데이빗 핀토의 2006년 중견수 PMR 순위입니다. 핀토의 수비스탯은 MGL의 UZR의 아이디어를 보다 구체적으로 실현했으며, 전 타구를 대상으로 측정을 했다는 점에서 더 신뢰도를 높일수 있었습니다.
I was basically repeating work done by Mitchel Lichtman which he named the Ultimate Zone Rating (UZR).
핀토는 다음의 6가지 요소를 바탕으로 수비수의 능력을 측정했습니다.
1. 공이 떨어진 지역
2. 타구의 유형 (플라이볼, 그라운드볼, 라인드라이브, 번트)
3. 타구의 강도 (슬로우, 미듐, 하드)
4. 경기장
5. 투수의 유형 (좌,우투수)
6. 타자의 유형 (좌,우타자)
