TL;DR It massively cheats and it’s bad, just ignore it.
First, OAA finally lets us compare all outfielders to each other regardless of position and without need for a positional adjustment. Range Defense Added unsolves that problem and goes back to comparing position-by-position. It also produces some absolutely batshit numbers.
From 2022:
Name | Position | Innings | Range Out Score | Fielded Plays |
Giancarlo Stanton | LF | 32 | -21.5 | 6 |
Giancarlo Stanton | RF | 280.7 | -6.8 | 90 |
Stanton was -2 OAA on the year in ~300 innings (like 1/4th of a season). An ROS of -21.5 over a full season is equivalent to pushing -50 OAA. The worst qualified season in the Statcast era is 2016 Matt Kemp (-26 OAA in 240 opportunities), and that isn’t even -*10*% success probability added (analogous to ROS), much less -21.5%. The worst seasons at 50+ attempts (~300 innings) are 2017 Trumbo and 2019 Jackson Frazier at -12%. Maybe 2022 Yadier Molina converted to a full-time CF could have pulled off -21.5%, but nobody who’s actually put in the outfield voluntarily for 300 innings in the Statcast era is anywhere near that terrible. That’s just not a number a sane model can put out without a hell of a reason, and 2022 Stanton was just bad in the field, not “craploads worse than end-stage Kemp and Trumbo” material.
Name | Position | Innings | Range Out Score | Fielded Plays |
Luis Barrera | CF | 1 | 6.1 | 2 |
Luis Barrera | LF | 98.7 | 2 | 38 |
Luis Barrera | RF | 101 | 4.6 | 37 |
I thought CF was supposed to be the harder position. No idea where that number comes from. Barrera has played OF quite well in his limited time, but not +6.1% over the average CF well.
As I did with the infield edition, I’ll be using rate stats (Range Out Score and OAA/inning) for correlations, each player-position-year combo is treated separately, and it’s important to repeat the reminder that BP will blatantly cheat to improve correlations without mentioning anything about what they’re doing in the announcements, and they’re almost certainly doing that again here.
Here’s a chart with year-to-year correlations broken down by inning tranches (weighted by the minimum of the two paired years)
LF | OAA to OAA | ROS to ROS | ROS to OAA | Lower innings | Higher Innings | Inn at other positions year T | Inn at other positions year T | n |
0 to 10 | -0.06 | 0.21 | -0.11 | 6 | 102 | 246 | 267 | 129 |
10 to 25 | -0.04 | 0.43 | 0.08 | 17 | 125 | 287 | 332 | 128 |
25 to 50 | 0.10 | 0.73 | 0.30 | 35 | 175 | 355 | 318 | 135 |
50 to 100 | 0.36 | 0.67 | 0.23 | 73 | 240 | 338 | 342 | 120 |
100 to 200 | 0.27 | 0.78 | 0.33 | 142 | 384 | 310 | 303 | 121 |
200 to 400 | 0.49 | 0.71 | 0.37 | 284 | 581 | 253 | 259 | 85 |
400+ inn | 0.52 | 0.56 | 0.32 | 707 | 957 | 154 | 124 | 75 |
RF | OAA to OAA | ROS to ROS | ROS to OAA | Lower innings | Higher Innings | Inn at other positions year T | Inn at other positions year T | n |
0 to 10 | 0.10 | 0.34 | 0.05 | 5 | 91 | 303 | 322 | 121 |
10 to 25 | 0.05 | 0.57 | 0.07 | 16 | 140 | 321 | 299 | 128 |
25 to 50 | 0.26 | 0.59 | 0.14 | 36 | 186 | 339 | 350 | 101 |
50 to 100 | 0.09 | 0.75 | 0.16 | 68 | 244 | 367 | 360 | 168 |
100 to 200 | 0.38 | 0.72 | 0.42 | 137 | 347 | 376 | 370 | 83 |
200 to 400 | 0.30 | 0.68 | 0.43 | 291 | 622 | 245 | 210 | 83 |
400+ inn | 0.60 | 0.58 | 0.32 | 725 | 1026 | 120 | 129 | 92 |
CF | OAA to OAA | ROS to ROS | ROS to OAA | Lower innings | Higher Innings | Inn at other positions year T | Inn at other positions year T | n |
0 to 10 | 0.00 | 0.16 | 0.09 | 5 | 161 | 337 | 391 | 83 |
10 to 25 | 0.00 | 0.42 | -0.01 | 17 | 187 | 314 | 362 | 95 |
25 to 50 | 0.04 | 0.36 | 0.03 | 34 | 234 | 241 | 294 | 73 |
50 to 100 | 0.16 | 0.56 | 0.09 | 70 | 305 | 299 | 285 | 100 |
100 to 200 | 0.34 | 0.70 | 0.42 | 148 | 434 | 314 | 305 | 95 |
200 to 400 | 0.47 | 0.66 | 0.25 | 292 | 581 | 228 | 230 | 86 |
400+ inn | 0.48 | 0.45 | 0.22 | 754 | 995 | 134 | 77 | 58 |
Focus on the left side of the chart first. OAA/inning behaves reasonably, being completely useless for very small numbers of innings and then doing fine for players who actually play a lot. ROS is simply insane. Outfielders in aggregate get an opportunity to make a catch every ~4 innings (where opportunity is a play that the best fielders would have a nonzero chance at, not something completely uncatchable that they happen to pick up after it’s hit the ground).
ROS is claiming meaningful correlations on 1-2 opportunities and after ~10 opportunities, it’s posting year to year correlations on par with OAA’s after a full season. That’s simply impossible (or beyond astronomically unlikely) to do with ~10 yes/no outcome data points with average talent variation well under +/-10%. The only way to do it is by using some kind of outside information to cheat (time spent at DH/1B?, who knows, who cares).
I don’t know why the 0-10 inning correlations are so low- those players played a fair bit at other positions (see the right side of the table), so any proxy cheat measures should have reasonably stabilized- but maybe the model is just generically batshit nonsense at extremely low opportunities at a position for some unknown reason as happened with the DRC+ rollout (look at the gigantic DRC+ spread on 1 PA 1 uBB pitchers in the cheating link above).
Also, once ROS crosses the 200-inning threshold, it starts getting actively worse at correlating to itself. Across all three positions, it correlates much better at lower innings totals and then shits the bed once it starts trying to correlate full-time seasons to full-time seasons. This is obviously completely backwards of how a metric should behave and more evidence that the basic model behavior here is “good correlation based on cheating (outside information) that’s diluted by mediocre correlation on actual play-outcome data.”
They actually do “improve” on team switchers here relative to nonswitchers- instead of being the worst as they were in the infield, again likely due to overfitting to a fairly small number of players- but it’s still nothing of note given how bad they are relative to OAA’s year-to year for regular players even with the cheating.