It's becoming my hypothesis that you can use launch angle on balls hit to the OF to determine an outfielders relative strength.
The more i look at the data, the more convinced I'm becoming.
So i downloaded the 2015 and 2016 KC Royals statcast data to see if I could compare their major outfielders performance year to year and see a couple things. What i've done is bucketed hits to the OF by launch angle (in 2 degree increments) and calculated a percentage of that contact resulting in a HIT or an OUT. Simple as that. So what i'm comparing between years is:
1) Are the hit likelihood percentages for each angle by OF resonably projectable year to year
2) Does improvement in my Angle metric result in improvement in other defense metrics.
First lets look at Jarrod Dyson. He's one of the best outfielders in MLB. He recorded, per fangraphs, 11 DRS in 2015 and to date has 18 DRS in 2016. His 2015 UZR/150 was 18.4 and in 2016 to date it's 28.7. So both of the "new-traditional" type defense stats are saying, he's not only good but he's getting better in 2016 versus 2015. What does my angular stat suggest?
The red points are for '16 Dyson while the blue is '15. The left linear regression eqn (with the .837 R2) is 2015 while the right (R2 .7796) is 2016. This shows dyson as a similar player year to year, but likely a bit better. The higher angle fly-balls, it does appear that Dyson has done a better job this year of tracking down; however, it also appears that in 2015 he did a bit better catching some of the lower angled fly balls. So it's not entirely clear, from this graph, why Dyson is per DRS and UZR having such a better defensive year. To have something like this happen, it could indicate that maybe Dyson is starting to play deeper than before. This would limit he likelihood of him catching the low angled line drives to the OF, but help track down more true fly balls. I'd certainly be interested to see if Dyson is actually doing that very thing this year.
When it comes to projecting year to year, the R2 for Dyson's '15 to '16 hit likelihood % was:
0.532. In real life this is a pretty strong correlation, so i'd say it's a reasonable estimator.
How about we look at KC OF defensive darling Alex Gordon:
Again the red points are for '16 Gordon while the blue is '15. The left linear regression eqn (with the .939R2) is 2015 while the right (R2 .8424) is 2016. It jumps right out to you how much smoother Gordon's regressions are than Dyson's. Maybe experience leads to that, who knows. So the 2016 regression line (the dashed one) shows that contact to him in the OF is a bit more likely to land for a hit now in 2016 than it was in 2015. This would suggest that Alex Gordon is having a worse year defensively in '16 than '15. How does DRS and UZR/150 compare? Well Alex has a DRS of 3 in 2016 and had a DRS of 7 in 2015. So he does seem to be trending a bit lower, though not all too much. And an UZR/150 in 2016 of 9.9 whereas that was 10.5 in 2015. So in this case it all sort of agrees. Gordon seems to be a step or two slower (age and injuries easily could account for that) and as a result his defense has stepped backward a bit. Interestingly he's still doing about the same job on balls that are high likelihood hits, the more difficult plays, it's really at the end of the spectrum which is where the balls are unlikely to be hits anyways that Alex seems to be struggling. So maybe the "skills" are still there, but the athleticism has just faded a bit and he can't run down those long fly balls anymore. This is sort of the opposite of Dyson. Maybe Gordon is in fact playing to shallow, cheating to ensure his reputation for robbing sure hits stays in tact while losing a bit of overall range creating a situation where some balls land that probably should have been outs.
When it comes to projecting year to year, the R2 for Gordon'15 to '16 hit likelihood % was:
0.778. This is excellent and i think it is clearly visible from the chart just how project-able year to year this would be.
What about allstar and defensive stalwart Lorenzo Cain?
Again the red points are for '16 Cain while the blue is '15. The left linear regression eqn (with the .8876 R2) is 2015 while the right (R2 .9073) is 2016. Well this is interesting, it's just as though you shifted the line up ever so slightly. A 2016 higher trend line would indicate that contact to the outfield around Lorenzo would be more likely than last year to result in a base hit. This would indicate he too has backslide some from his 2015 self. So what does UZR and DRS say? DRS in 2016 is 11 whereas it as 18 in 2015. But UZR/150 is currently 15.4 in 2016 and it was only 14.1 in 2015. So there is a bit of confusion as to Cain's 2016 performance, relative to '15. Clearly he is still an excellent outfielder by all measures, but i would lean toward him trending in the negative direction in 16 and moving forward.
Given the two linear regressoins and data sets, you'd have to believe you could use this data to project very accurately the future year. And you'd be right. Cain's year to year R2 checks in at 0.955.
Well what about newcomer Paulo Orlando? he already seems to be living up to the newfound tradition of excellent KC Outfield defense:
Paulo Orlando is sort of the exact reverse of Cain. His trend has basically just taken an entire step down. This means balls are less likely to be hits now than before. So does UZR and DRS agree with Orlando taking what appears to be a reasonable step forward? Suprisingly no. DRS from 15 to 16 has jumped from 8 to 12, but Orlando has played a lot more innings which more or less would explain that growth. And his UZR/150 went from 14.0 in 2015 to 8.7 now in 2016. So these metrics both seem to think Orlando is the same if not a little worse than '15.
Projecting using orlando's earlier year is, like Cain, excellent. an R2 of .90 between the two data sets.
Conclusions:
So for my questions:
1) Are the hit likihood percentages projectable year to year? This seems to be a resounding yes, at least in the case of KC royals. The R2 was always greater than 0.5 with two instances of the four being over 0.9! I'm starting to believe this really could mean something in regards to defense evaluation.
2) How does my angle measure compare to UZR/DRS? There does seem to be some differences; however, this is basically the norm in the "new" defense evaluations. No universal system has been developed and there are plenty of cases where UZR and DRS themselves have disagreements.
I do think in the end this has some merit and i will be looking further into it. I also think similar work can be down with regards to hit speed, as i already alluded to in my earlier article
http://www.fangraphs.com/community/using-statcast-to-substitute-the-kc-outfield-for-detroits/
i think it's important to view both the angle and hit speed as two pieces and going forward that's something i'm hoping to include for these players.
