The series on objective probabilistic model of range (PMR) continues by looking at leftfielders. I’ll show teams as a whole at the position, plus individuals who were on the field for 1000 balls in play. First the teams:
Team | In Play | Actual Outs | Predicted Outs | Actual DER | Predicted DER | Index |
NYA | 2947 | 363 | 297.9 | 0.123 | 0.101 | 121.8 |
ARI | 2870 | 335 | 304.0 | 0.117 | 0.106 | 110.2 |
TBA | 3455 | 358 | 325.0 | 0.104 | 0.094 | 110.1 |
KCA | 3740 | 353 | 323.7 | 0.094 | 0.087 | 109.1 |
LAN | 2766 | 272 | 253.8 | 0.098 | 0.092 | 107.2 |
WAS | 2546 | 305 | 288.1 | 0.120 | 0.113 | 105.9 |
SEA | 3586 | 350 | 331.2 | 0.098 | 0.092 | 105.7 |
ATL | 2983 | 285 | 270.5 | 0.096 | 0.091 | 105.4 |
DET | 3106 | 349 | 332.2 | 0.112 | 0.107 | 105.0 |
SFN | 1492 | 150 | 143.3 | 0.101 | 0.096 | 104.7 |
BOS | 3106 | 313 | 302.9 | 0.101 | 0.098 | 103.3 |
HOU | 3304 | 259 | 252.8 | 0.078 | 0.076 | 102.5 |
MIL | 3285 | 263 | 257.8 | 0.080 | 0.078 | 102.0 |
ANA | 3816 | 350 | 343.5 | 0.092 | 0.090 | 101.9 |
CIN | 3424 | 290 | 285.0 | 0.085 | 0.083 | 101.8 |
TEX | 3116 | 309 | 305.7 | 0.099 | 0.098 | 101.1 |
MIN | 2807 | 330 | 329.1 | 0.118 | 0.117 | 100.3 |
BAL | 3638 | 353 | 356.7 | 0.097 | 0.098 | 99.0 |
CHA | 3520 | 310 | 317.1 | 0.088 | 0.090 | 97.8 |
CLE | 3817 | 319 | 329.0 | 0.084 | 0.086 | 97.0 |
SDN | 2880 | 273 | 282.5 | 0.095 | 0.098 | 96.6 |
PIT | 1662 | 128 | 133.5 | 0.077 | 0.080 | 95.9 |
FLO | 3273 | 282 | 294.7 | 0.086 | 0.090 | 95.7 |
NYN | 2664 | 276 | 290.0 | 0.104 | 0.109 | 95.2 |
TOR | 3572 | 297 | 313.1 | 0.083 | 0.088 | 94.9 |
CHN | 2998 | 247 | 262.5 | 0.082 | 0.088 | 94.1 |
PHI | 2507 | 242 | 257.1 | 0.097 | 0.103 | 94.1 |
OAK | 3261 | 285 | 310.9 | 0.087 | 0.095 | 91.7 |
SLN | 3294 | 262 | 287.6 | 0.080 | 0.087 | 91.1 |
COL | 3336 | 220 | 263.5 | 0.066 | 0.079 | 83.5 |
I can’t say I’m surprised to see the Yankees and Rays near the top. Brett Gardner is known for his defense, and Sam Fuld made some amazing plays for the Rays before Desmond Jennings moved into the lineup.
The individuals:
Fielder | In Play | Actual Outs | Predicted Outs | Actual DER | Predicted DER | Index |
Brett Gardner | 2387 | 294 | 237.4 | 0.123 | 0.099 | 123.9 |
Gerardo Parra | 2051 | 244 | 216.6 | 0.119 | 0.106 | 112.7 |
Vernon Wells | 2408 | 241 | 214.8 | 0.100 | 0.089 | 112.2 |
Sam Fuld | 1505 | 162 | 146.7 | 0.108 | 0.097 | 110.4 |
Josh Hamilton | 1472 | 153 | 140.7 | 0.104 | 0.096 | 108.7 |
Ryan Braun | 2826 | 237 | 219.8 | 0.084 | 0.078 | 107.8 |
Alex Gordon | 3419 | 313 | 292.1 | 0.092 | 0.085 | 107.2 |
Martin Prado | 1656 | 165 | 158.4 | 0.100 | 0.096 | 104.2 |
Tony Gwynn | 1165 | 110 | 107.4 | 0.094 | 0.092 | 102.5 |
Nolan Reimold | 1524 | 146 | 143.4 | 0.096 | 0.094 | 101.8 |
Jonny Gomes | 1269 | 112 | 110.8 | 0.088 | 0.087 | 101.1 |
Desmond Jennings | 1000 | 100 | 98.9 | 0.100 | 0.099 | 101.1 |
Carl Crawford | 2342 | 235 | 233.2 | 0.100 | 0.100 | 100.8 |
Carlos Lee | 1509 | 113 | 114.6 | 0.075 | 0.076 | 98.6 |
Delmon Young | 1886 | 208 | 214.2 | 0.110 | 0.114 | 97.1 |
Jason Bay | 1922 | 204 | 210.3 | 0.106 | 0.109 | 97.0 |
David Murphy | 1278 | 123 | 127.0 | 0.096 | 0.099 | 96.8 |
Logan Morrison | 2295 | 197 | 207.9 | 0.086 | 0.091 | 94.7 |
Juan Pierre | 3282 | 278 | 295.0 | 0.085 | 0.090 | 94.2 |
Michael Brantley | 1396 | 114 | 121.5 | 0.082 | 0.087 | 93.8 |
Alfonso Soriano | 2062 | 169 | 181.2 | 0.082 | 0.088 | 93.3 |
Raul Ibanez | 1983 | 195 | 209.1 | 0.098 | 0.105 | 93.2 |
Eric Thames | 1132 | 91 | 99.1 | 0.080 | 0.088 | 91.8 |
Josh Willingham | 1862 | 155 | 170.5 | 0.083 | 0.092 | 90.9 |
Ryan Ludwick | 1864 | 155 | 173.6 | 0.083 | 0.093 | 89.3 |
Matt Holliday | 2310 | 182 | 204.0 | 0.079 | 0.088 | 89.2 |
Carlos Gonzalez | 1094 | 76 | 87.4 | 0.069 | 0.080 | 86.9 |
Gardner and Fuld were indeed very good, and Desmond Jennings held his own. I’m impressed with Carlos Lee’s ranking. At this point, I would think he would be old and slow, but it looks like he gets to most of the balls that he should catch. Juan Pierre, Alfonso Soriano, Raul Ibanez, and Matt Holliday all lost a step.
If arm was included Crawford is a lot lower. I don’t think I have seen such a poor throwing LF’er for the Red Sox in over 45 years (with Williams and yaz going back another 20 years before 67 that I did not see).
Only 1 assist (on a cut off). Oh my.
Hoping it was his wrist to blame.
I’m not surprised that Brett Gardner rates high, but I’m surprised he rates *that* high. He has a higher index than anyone but Jack Hannahan, and saved more outs than any other player.
What is the expected win value of getting an extra 57 outs over 150 games, David? I mean, how many runs does an outfielder save by getting an extra 57 outs, on average?
Gritner!
James » I believe the book has a fly out worth 0.28 runs (in the fielder’s favor). So assuming all fly outs, 16 runs. FanGraphs puts his fielding runs at 25.8, so I would think those range numbers are reasonable.
Remember, we’re talking about him playing in a big field at Yankees stadium. When you build the parameters off everyone else, Gardner is going to look really good.
David, I don’t think that’s the right way to do it. The .28 Book value of an out means this: on average, the expected runs for the situation goes down by .28 when the batter makes an out. It’s the difference between the situation before the AB and the situation after the fly out.
But if Gardner gets 57 extra outs, we need a different statistic. We aren’t looking for the exp run difference between pre-AB and post-out. We’re looking for the exp run difference between the fielder catching that batted ball and the fielder missing that batted ball.
I don’t know what that value is, but it’s sure to be MUCH bigger than the .28 in The Book. (I bet it’s greater than a run per extra out when we’re talking about OF outs.)
Okay, here’s a rough estimate. Suppose 36 of those outs would have been singles and 21 would have been doubles. (It’s just a wild guess, I have no idea.) So 36 of the catches add an out *and* prevent a single, while 21 of them add an out *and* prevent a double. A double is worth .77 and a single .47, so 36*.75 + 21*1.05 = about 46 runs. (Which is a lot more than FanGraphs estimates.)
James » I’ll have to think about this. The run estimates is the linear runs coefficients. So you should be able to figure out his runs on each out or hit, and get his total, where 0 would be average. Of course, you need to share the hits with other fielders, since more than one fielder can be responsible for a hit, but give all the credit for the out to the fielder who makes the play.