Using Statcast to Analyze the 2015/16 Royals Outfielders

I’m working under the hypothesis that you can use launch angle on balls hit to the outfield to determine an outfielder’s 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 bucket hits to the OF by launch angle (in two-degree increments) and calculate 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 reasonably projectable year to year
2) Does improvement in my angle metric result in improvement in other defense metrics

First let’s 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 equation (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. On the higher-angle fly balls, it does appear that Dyson has done a better job this year tracking them 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 the 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 equation (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 do 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 too much. And he has a 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 where the balls are unlikely to be hits anyway 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 too shallow, cheating to ensure his reputation for robbing sure hits stays intact 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 projectable year to year this would be.

What about All-Star and defensive stalwart Lorenzo Cain?

Again the red points are for ’16 Cain while the blue is ’15. The left linear regression equation (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 trendline 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 backslid some from his 2015 self. So what do UZR and DRS say? DRS in 2016 is 11 whereas it was 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 regressions 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 do UZR and DRS agree with Orlando taking what appears to be a reasonable step forward? Surprisingly 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 in ’15.

Projecting using Orlando’s earlier year is, like with Cain, excellent. There is an R2 of .90 between the two data sets.

So for my questions:

1) Are the hit-likelihood 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 do 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 done with regards to hit speed, as I already alluded to in my earlier article:

Using Statcast to Substitute the KC Outfield for Detroit’s

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.



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