Is Ian Kinsler the second coming of Chris Shelton?

After two and a half weeks into the 2007 season, your second-leading home run hitter is… Ian Kinsler?  Kinsler, who took over the Rangers starting second base job full time last year (after not even breaking camp with the club out of Spring Training), hit 14 HR in 120 games last year.  Following this performance, the major projector systems had him pegged for hitting between 13 and 20 HR over a full season of starting.  Right now, he’s on a pace to hit roughly 6 million homeruns this year.  At least to hear some people talk.
On the surface, Kinsler smells like this year’s version of Chris Shelton.  Shelton hit 10 HR in April 2006 and was hailed as the next big thing, but ended up in Toledo for part of the year and hit 6 more major league home runs during the rest of the season.  I used Shelton as an exemplar in an earlier post on the dangers of reading too much into small sample sizes, and Kinsler’s 46 PA at the time of this writing is a mighty small sample size from which to draw any conclusions.
Can he keep it up?  Well, the question begs a bit of analysis.  What else do homerun hitters do?  I ran a quick stepwise regression to see what was associated with HR rate (log of the odds ratio).  I threw in a bunch of predictors to see what would shake out, including rates of different types of hits (fly ball, ground ball, etc.), swing contact rate, swing rate, rates of different outcomes (2B+3B, singles, K’s, BB’s).  Five variables emerged as significant predictors before the contribution to the R-squared started getting trivial.  All in all, I nailed down about 75% of the variance.  My data set was all hitter-seasons from 1993-1998 with at least 100 PA.
First of all, home run hitters also hit a lot doubles and triples.  Kinsler has seven homeruns and only one double to his name this year (as compared to 27 doubles, a triple, and 14 HR last year).  So far, it’s not looking so good for his keeping up his current insane pace.  It looks more like he’s had a few doubles pushed over the fence by some fortuitous gusts of wind.
Second, hitters who make less contact with the ball (raw percentage of the time swinging that bat meets ball, no matter what happens from a foul tip to a moon shot) actually hit more homeruns (see this study for further investigation).  That particular data wasn’t available on Kinsler for this year, although last year, he was in the top 10% in MLB in making contact with the ball (88%).  It’s possible that he’s experimenting, successfully apparently, with a longer swing, but in general contact percentage is remarkably stable from year to year, with an intra-class correlation (AR1 rho) of .77.  This means that about 60% of the variance in contact rate from year to year is consistent within a player.  It’s hard for a leopard to change his spots.  Kinsler was a contact hitter last year, and he did pretty well for himself.  It’s hard to believe he would change much, but he might. 
The final three predictors showed that HR hitters generally hit more fly balls and pop ups (that is, balls in the air), and that they walk more often.  Kinsler’s stats do show that he’s hitting more balls into the air this year (48% vs. 44% last year), although those rates for this year are based on small sample sizes.  Also, fewer of those fly balls are staying in the infield (13.8% vs. 6.3%).  Here’s the big difference: Last year, 8.8% of Kinsler’s fly balls went for homeruns.  This year, 43.8% of them have.  There’s no earthly way that a jump like that can be sustained.  He might have altered his swing a bit, but don’t expect that sort of rate to continue.
Kinsler shows a lot of signs that over a small number of plate appearances, he got a little lucky.  My guess is that deep down, he’s still really just the guy that people were pegging for 15-20 HR this year.  He might finish with 25 (figure that for the rest of the season he hits 15-18, which was the rate he was more or less predicted at), and I don’t want to besmirch a second baseman who can do that, but please people (especially you fantasy owners), don’t think that you’re going to get a 60 HR season out of him.
But if you own him in your league and want to trade up, remember, all you need is an owner who doesn’t know the basic laws of statistics.  The one who traded for Chris Shelton last year.  Talk to him, especially if he owns Chase Utley.

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8 Responses to Is Ian Kinsler the second coming of Chris Shelton?

  1. John Beamer says:

    Scuse my ignorance, but what is an intraclass correlation?

  2. Pizza Cutter says:

    Think of it as a year-to-year correlation, but over multiple observations instead of just two.

  3. Sean Smith says:

    I should see what I can get for Kinsler. I’ve got Kinsler and Brian Roberts in a head to head league, I’ll play Kinsler if I need more power and Roberts if I need steals.
    Funny that just 2 years ago Roberts went on one of these streaks. That didn’t last too long.
    I got lucky when somebody actually dropped Roberts after a week, and took Kinsler only when the Rev Halofan beat me to Howie Kendrick. Poor Howie.

  4. John Beamer says:

    PC — you mean there might be four observations, for yr1, yr2, yr3, yr4 …?
    Thanks for the explanation –useful for sure

  5. Carlos Rubi says:

    “Here’s the big difference: Last year, 8.8% of Kinsler’s fly balls went for homeruns. This year, 43.8% of them have.”
    Thanks a lot for digging that up.

  6. Pizza Cutter says:

    John, in theory, the number of observations could be infinite. Not only that, a participant doesn’t have to have data in all years, either. It’s a fantastic technique, one that I’m surprised more Sabermetricians haven’t taken advantage of.

  7. John Beamer says:

    Sounds interesting … are there any good web links you can point me to … by good I mean not *too* complicated 🙂

  8. Pizza Cutter says:

    I checked, but nothing came up that made sense. Further complicating that is that there are a bunch of measurements that are called “intra-class correlations” I’m a clinical statistician, so some of the more advanced theoretical derivations and formulae are beyond me, but I have used it before in clinical settings, and it has worked beautifully. I see no reason why it can’t be used here. It’s relatively easy to calculate, and in our discussions of regression to the mean, it would be a more accurate measure than a simple year-to-year correlation to plug in.

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