Using Statcast To Project Trea Turner

Projecting players, especially younger players coming off short rookie campaigns is a difficult endeavor, and there are many different systems out there you probably already turn to for advice and perhaps encouragement. You have Steam, ZIPs, PECOTA, Marcel, etc. Today I’d like to consider the relative value of Statcast and by extension xStats as a potentially worthy companion in your quest to evaluate young talent in the league. To be clear, I’m not trying to say xStats is on the level of those other systems, because it isn’t.  The real projection systems are much more sophisticated and created by much more intelligent people, xStats is only meant to offer a different lens through which you may look at different aspects of the game and hopefully lead you to asking a few questions you may not have otherwise asked. Please don’t take this too seriously!

Looking at Trea Turner:

In 2016, the Nationals took their time calling up Trea Turner, but once handed the opportunity he made a big time splash at the top of their lineup.  Somewhat unexpectedly, the Nationals shoved him into center field, and it went surprisingly well.  He has many innate gifts on a baseball field, including foot speed and poise, so perhaps it shouldn’t be a huge surprise he adapted to the outfield so well.  But, hey, I don’t care who you are, playing a position for the first time at the major league level has to be pretty hard.  

Offensively, Turner showed surprising power, hitting six home runs in AAA and 13 more in the big leagues.  He hit only 5 home runs in 2014 and 9 in 2015, so these 19 proved to be a pretty big step forward for him.  xStats measured this power and awarded it 11.9 xHR when accounting for park and environmental factors like game day temperature. In other words, according to xStats, this power had more to do with consistently exceptional contact, and less so about luck. Last week I wrote about ideally hit balls, those which are hit between 21 and 36 degrees of vertical angle and a minimum of 96 mph exit velocity. In 2016 Turner managed to hit 41 of these, 10 of which for home runs. Which is a below average rate for such ideally hit balls, which tend to have a home run rate around 50%.  This power has been accounted for in the xStats results you’ll see below.

Turner managed a well above average BABIP in 2016, an absolutely absurd .388 for the season. Below I have a table depicting his average success rates for batted ball types in the majors.  Note that these include his numbers from 2015 and 2016 combined.

Batted Ball Type Success Rates
type BIP BABIP xBABIP lgBABIP SLG xSLG lgSLG wOBA xOBA lgwOBA
FB 55 .222 .198 .122 1.019 .922 .729 .559 .500 .390
GB 138 .391 .298 .277 .420 .350 .308 .354 .289 .255
LD 66 .581 .606 .616 1.139 1.053 1.066 .719 .704 .717
PU 19 .000 .028 .014 .000 .040 .020 .000 .031 .015

(I wanted to include batting average in this chart but ran out of space)

You can see Turner’s success rates for ground balls and fly balls both far exceed the rest of the majors in both in game success and the expected results according to xStats.  However, the xStats results tend to be quite a bit closer to the major league averages.  Take note of his line drives in this chart, though.  His line drives have below average success rates, but above average extra base potential. As though his line drives were sitting somewhere between a true line drive and a fly ball.  Taking a peek at his average launch angle, it is 16.6 degrees for line drives, which is a bit lower than the 17.4 degree MLB average. His line drives also have an exit velocity which is nearly 2 mph faster than average.  Generally speaking line drives are known for exceptionally high BABIP singles, but Turner’s trend more towards relatively low (I stress relatively) BABIP and high slugging.  Perhaps this is a meaningless blip on the radar that will average out over time.  I’m not putting much stock in it, personally, but it is a point of interest you may want to watch going forward.

On to the real issue here, though- BABIP.  How in the world did he manage to have a .388 BABIP?!  Especially with below average line drives, this seems almost impossible!  Yes, he had pretty great ground balls, that that only accounts for a bit of this difference.  If I drop his ground ball success down to league average, he still has a BABIP hovering around .360.  No, his BABIP was driven by fly ball success rate, which is perhaps the least stable of the ways to achieve a high BABIP.  Great ground balls you can attribute to speed, which he has.  Great line drives you can attribute to batting approach.  Great fly balls, well, that seems like, perhaps, bad outfield positioning? Some luck? His xBABIP on fly balls is nearly 100 points lower than his registered BABIP.  And the league average is another 21 points beneath that.  I don’t feel the least bit uncomfortable stating his high BABIP is unsustainable.  Not only because it is ludicrously high, but due to the manner it achieved this high mark.  Perhaps you could say his below average line drives will rebound towards average.  You can absolutely say his flyballs will regress.  His ground balls?  Well, I’m not so sure about that, elite speed has a way of making those stick around, at least for the first few years of a player’s career.

As for Runs Scored and RBI, I am using the tool created by Ryan Brock to convert the xStats slash line. Trea Turner, as far as I can tell, will bat at the top of the order of a pretty strong Nationals Lineup.  Last year he seemed to bat primarily second, and I’m not sure how much that will change.  With his foot speed, power, and on base potential, especially if he can develop a higher walk rate at the major league level, he makes for an ideal top of the order batter. In terms of fantasy, he may have more value batting second, since it will, on average, increase his RBI potential without sacrificing many plate appearances, base stealing opportunities, and of course run scoring opportunities.  Fortunately, he bats right handed, so he can stand between guys like Daniel Murphy and Bryce Harper to split up the lefty batters.    

So, here you have it, the xStats, um, not projection?  It isn’t a projection!  It’s just a, um, I don’t know.  A nonprojection!  A guess?  I don’t know why I put projection in the title, that word scares me.  I don’t know what to call it.  Either way, you see ZiPS, you see xStats.  You see the difference between the two (in this case xStats minus ZiPS).  Both of these methods use a very, very different process to evaluate players.  xStats uses statcast data and nothing else, it clings to batted ball velocity and launch angle. ZiPS is quite different, and there are many resources you can look at to learn more about it.  At the end of the day, though, you see very similar results.  Eerily similar, perhaps. xStats docks a few triples and grants a few more home runs, on the back of those ideally hit fly balls no doubt. It also docks a few points of average and on base percentage, but adds bonus slugging. xStats does nothing to predict walks or strikeouts, so it assumes those will stay steady from year to year. So take xOBP with that grain of salt.

Trea Turner
PA R H 2B 3B HR RBI ISO BABIP AVG OBP SLG wOBA
ZIPs 594 77 154 27 10 14 66 .163 .346 .282 .331 .445 .347
xStats 594 95 155.4 27.7 7.2 19.6 70 .186 .352 .277 .313 .458 .348
Difference 18 1.4 0.7 -2.8 5.6 4 .023 .006 -.005 -.018 .013 .001

 

So, where would this place Trea Turner in the overall group of Shortstop/second basemen/center fielders?  Well, it ranks in way up there. The extra few homers xStats feels are comfortably hiding in his bat really fuel the difference in judgment here. Many feel he has 12-14 home run potential and thus label him a top 3 second basemen, and by extension a top 40 or so position player.  These extra five homers, though, they change the equation a bit.  If you believe in this power as strongly as xStats does, perhaps you should buy Turner for that upside potential.

We hoped you liked reading Using Statcast To Project Trea Turner by Andrew Perpetua!

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Andrew Perpetua is the creator of CitiFieldHR.com and xStats.org, and plays around with Statcast data for fun. Follow him on Twitter @AndrewPerpetua.

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MtnDrewski
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MtnDrewski

What does xStats guess for his stolen bases?