A call for clinical Sabermetrics

OK, we get it.  We now know (or at least have half a dozen different contenders for) the proper definition of a replacement player and what the value above that replacement player is in wins, dollars, yen, and quatlus.  We know that multiple-Gold Glove award winner Derek Jeter is actually not a good shortstop.  People who have never been to this blog are using OBP properly in a sentence.  We have uber-stats (plural).  We have un-masked Torii Hunter as a fraud, figured out that Mark Ellis is pretty good, and even discovered that Albert Pujols is a halfway decent baseball player.  We’ve gotten to the point where we can describe a player’s abilities on an array of factors, and we’re pretty good at it.

Now what?

This isn’t a post to say that “there’s nothing more to be discovered in terms of figuring out things about baseball.”  Far from it.  In fact, there is plenty more to look into, and I’ll bet that there are some really interesting findings lurking around the corner.  A year (two? three?) from now, we’ll have new toys that we hadn’t even imagined before.  And two or three of them will be really super cool.  And I’ll spend my free time thinking about them.  And then someone will come up with something else.  Maybe it’ll even be me.

The point is that we’ve really only fought half the battle.  There’s another frontier in Sabermetrics that has only been lightly explored.  I’m a clinical psychologist by training, and there are two parts to my job (three if you count the endless paperwork.)  There’s diagnosing a problem and then there’s treating it.  I would argue that we, as Sabermetricians, are fairly good at the diagnosis part.  We can pick out flaws or strengths in a player’s game that the general public may not pick up on or maybe even the baseball insiders don’t.  But what difference does it make to know that if the conclusions won’t be turned into results?

Here, I’m not so much talking about recommendations like “The Indians should sign this guy!” or “Johnson is a steal at that price!”  Those are good recommendations to be sure, but not what I had in mind.  Here I’m thinking about finding out ways that we can change the players themselves.  Most of the recommendations in Sabermetrics up to this time have been around which players to avoid and which are under-valued.  But that requires signing new players and finding someone gullible enough to take the over-valued guy off your hands.  They’re personnel moves.  It’s just diagnosing the sick and quarantining them.  What about working with the guys you already have?  Can we use Sabermetrics to actually change individual players?

For some things, maybe not.  We probably aren’t going to make Frank Thomas run like Wily Taveras (nor will Wily Taveras ever hit like Frank Thomas).  And no, Jamie Moyer will never throw a 95 mph fastball.  (Something about a silk purse and a sow’s ear…)  We… the royal “we”… can’t change the physical characteristics of a player.  But we can change a player’s mind.

Consider the now-famous post on U.S.S. Mariner concerning Felix Hernandez’s pitch selection.  To simply say that King Felix likes to throw a lot of fastballs early in the game is descriptive (and true).  To point out the obvious that hitters were going to eventually pick up on it is changing the pitcher himself.  Now, his past behavior doesn’t predict his future behavior because of the awareness of the past behavior itself.  I have to wonder how many other pitchers fall into patterns (fastball-then-slider) without thinking about it, patterns that could be uncovered with just a little sleuthing through the data.  Make a pitcher aware of his pattern, and you break the pattern.  Suddenly, he’s a different pitcher.  If you know the answer to the question, it changes the question.

If there’s one mistake that the Sabermetric movement has made over the past few years (perhaps not intentionally, but certainly, it’s been made) is that we’ve reduced players to glorified, if quite advanced, Strat-o-matic cards.  Perhaps the craze around “clutch hitting” a few years back stunted our growth.  The evidence said that clutch hitting didn’t exist, and we over-extended the findings and either started denying that baseball players could be affected by psychological variables or simply stopped researching things like that.  Clutch hitting may not really exist (or more properly, the evidence says that it exists, but is a very minor part of the overall equation), but does that mean that no psychological factors might be in play?  Human beings learn from experience, but our models don’t do a good job taking that into account.  Human beings are prone to being affected by their emotions, perhaps not in the ways that we immediately think of (that’s why we need empirical data… hence the clutch hitting debate), but to suggest that players are immune to emotional and psychological concerns would be to suggest that players are robots.  Sabermetrics hasn’t “gone there” very much, at least not yet.

My call is for a clinical Sabermetrics, one that goes beyond simply seeing probabilities as fixed and sees players as being affected by context.  I want to get inside the mind of a major league player.  It may seem impossible, but as a trained clinician, it’s often fairly easy to figure out what someone’s thinking from their actions.  And if we know that players, either individually or as a whole, are given to certain psychological patterns, we can either encourage them or intervene to stop them (as the case calls for.)  Maybe the effects are small, but then again, maybe there’s something big lurking out there.

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6 Responses to A call for clinical Sabermetrics

  1. Brian Cartwright says:

    As you mention with King Felix, I think the first place we will see this is with pitch f/x data. It can be made into digital pitching charts. For 100 years teams have been charting pitches on paper, and then trying to analyze it into something meaningful. With pfx, the pitcher can see a heatchart for any particular batter, who will chase the slider, who will take it, who will rake it – all up on the computer screen.

  2. Matt Swartz says:

    I was going to say pitch f/x data would probably be where it would play in too. I think that ultimately game theory will started to be useful in determine pitch choice. A few recent articles today and yesterday by Dave Allen at BaseballAnalysts.com would be a good place to start. Hitters and pitchers should be playing a bit of a mixed strategy Nash equilibrium where pitchers effectively randomize between throwing various pitches in various locations and hitters randomize between swinging and not swinging and setting up to swing at pitches in certain locations. If a pitcher like King Felix typically throws fastballs more often first pitch, hitters will respond by swinging for the fastball more often and he should react by throwing it less. It hitters swing seem to be hitting outside pitches better than inside pitches (holding hitter type constant), pitches should probably throw inside a little more often, etc. I think that’s really where some of the first big strides may be in “clinical sabermetrics.”

  3. jinaz says:

    The other example is Brian Bannister, of course. He indicated last offseason that he was an avid reader of this stuff, and directly tried to apply DIPS ideas to his own pitching. Prior to last season, he said he was going to consciously try to increase his strikeout rate to try to counter the expected regression of his lucky BABIP in 2007. He also made avid use of pitchf/x last season to analyze his pitches as a complement to watching video tape.
    The results? He did increase his strikeouts by a full batter per game (4.4 k/g to 5.4 k/g). Unfortunately, his walk rate also increased, his ground ball rate decreased, and his home run rate skyrocketed. And his BABIP flipped back to the other side of 0.300. The result was a pretty tough season (5.76 ERA).
    His xFIP actually did decrease last season thanks to the surge in strikeouts, and I saw an interview where he referenced that as an encouraging sign. But it’s not exactly a success story, either. It will be interesting to see what happens this year, provided Bannister is able to keep his job.
    -j

  4. Max says:

    Most of the recommendations in Sabermetrics up to this time have been around which players to avoid and which are under-valued. But that requires signing new players and finding someone gullible enough to take the over-valued guy off your hands. They’re personnel moves. It’s just diagnosing the sick and quarantining them. What about working with the guys you already have? Can we use Sabermetrics to actually change individual players?
    Dr. Mike Marshall might appreciate this. Here’s an excerpt of what he wrote to me in an email exchange:
    I have no problem with sabermetrics. My problem is when general managers only use these
    statistics to decide whom to draft or trade for. I believe that if they evaluate players
    on the basis of specific statistics, then they should teach their players how to improve
    these statistics.

  5. Millsy says:

    Max,
    I think it would be a mistake to assume that personnel decisions in MLB are made solely on statistical on-field analysis. Teams like the Indians and Rockies have developed techniques similar to the Wonderlic tests given to NFL players, among other things. Assuming that these teams don’t investigate how to improve their players, in my opinion, is quite a naive perspective on how General Managers do their jobs. If this weren’t the case, they wouldn’t bother hiring and firing coaches.
    Could this information be put to better use? I’m sure it can, and I agree with Pizza Cutter that this static view of player statistics can lead people in the wrong direction. There’s always room for innovation. But I think that people get so wrapped up in the ‘sabermetric’ views on blogs, news, etc., that they don’t realize this is not actually the only thing GMs and executives take into account when making personnel decisions. They do, however, have to rely heavily on them, as it is an efficient way to do business when your personnel is spread across the country.

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