Can Billy Hamilton's Legs Make Up for his Bat?

Photo Credit: Keith Allison

Billy Hamilton is having one of the strangest seasons I've ever seen.  He has been absolutely terrible at the plate.  Really, really bad.  Thus far, heading out of the All-Star break, he is hitting .220/.269/.287 with a 52 wRC+.  Compared to his 2014 season, when he was merely not good, he is making more contact (19% K rate in 2014, 15% in 2015).  However, this contact has been more often soft contact (22% Soft, 21% Hard in 2014, versus 23% Soft, 17% Hard contact in 2015) and he has been hitting slightly fewer ground balls (41.5% in 2014, 40% in 2015).  There have been times when I have been more confident in the pitcher's offense than in his, which doesn't happen very often with starting position players.

Remarkably, there are currently four qualified batters who have a lower wRC+ than Hamilton this year.

Alexei Ramirez, 43 wRC+, -1.0 WAR
Mike Zunino, 45 wRC+, -0.3 WAR
Chris Owings, 48 wRC+, -0.7 WAR
Omar Infante, 48 wRC+, -0.3 WAR
Billy Hamilton, 52 wRC+, +1.6 WAR

Ramirez and Infante, at least, have a history of being at least decent, if not plus, hitters for their positions.  Zunino is a catcher with a good defensive reputation.  Owings is a sophomore and former good prospect who has fallen on hard times.  If any of those guys don't pick up their offense in the second half, however, there's a good chance that they'll lose their starting position.  As a result, they won't achieve "qualified batters" status by the end of the season.

Going back 10 years (2005-2014), there are only five players who have managed a full season of playing time with a wRC+ lower than Hamilton's:

2006 Clint Barmes, 38 wRC+, -0.7 WAR
2010 Cesar Izturis, 46 wRC+, -0.4 WAR
2006 Ronny Cedeno, 48 wRC+, -1.8 WAR
2006 Angel Berroa, 48 wRC+, -1.5 WAR
2013 Alicedes Escobar, 49 wRC+, +1.1 WAR

(of note: Zack Cozart in 2014 had a 56 wRC+, +1.2 WAR)

Therefore, while Billy has been unquestionably bad at the plate, it hasn't quite risen to historical status.  The interesting thing to me about these two lists, beyond the magnitude of these hitters' offensive struggles, is that only two of them show up as having positive WAR: Alicedes Escobar and Billy Hamilton.  In both cases, the players' WAR totals are buoyed by strong baserunning numbers and strong fielding numbers.  There are those who will scoff at fielding data, but I can swallow that an elite defensive player like Escobar could still be worth 1 win above replacement.  That's still well below average, and not good.

Hamilton, though, currently has 1.6 WAR.  That puts him on pace for almost exactly 3 WAR, which would put him as a slightly above-average baseball player.  This is despite owning (if it doesn't improve) a batting line that would rank him 6th-worst since 2005.  Is that really possible, or is WAR broken when it comes to Hamilton?  Let's go component by component.

Hamilton's Baserunning

Perhaps more than any other ballplayer, Billy Hamilton's value comes on the basepaths.  While last year he was caught regularly, this year he has become far better at determining the best time to go.  Heading into last night's game, he had stolen 44 bases and been caught only 6 times, good for an 88% success rate.  That's Barry Larkin-level success rates, except that Larkin only topped 44 bases in one season.  Hamilton is on pace for 83 thefts.

And it's not just his basestealing.  Hamilton sails from first the third with ease, and scores from first on anything even remotely resembling an extra base (assuming he hasn't already stolen second).  He's been brilliant.

The two best baserunning totals since 2005 are Mike Trout in 2012 and Willy Taveras in 2008 (+14 runs each).  Hamilton is already 17th on that list at +10.6 runs, with 76 more games to play.  That puts him in really good position to be at the top of this list by the season's end.  He might even do that by the end of August.

Given everything we know about his actual baserunning abilities, I believe these numbers.  He's really special out there.  He might not reach +20 runs by the season's end (which he's currently on pace to do), but would anyone really be surprised if he posted the best baserunning season of the past decade at +15 runs or so?

Hamilton's Fielding

Thanks in large part to MLB's condensed games, I've been able to watch at least the meaningful plays of almost every Reds game this season.  The thing that has stuck out to me, even more so than Hamilton's baserunning, is his defense.  I can't count how many times I've seen a ball hit hard into the gap, cursed in anticipation of a double, only to see Billy somehow close on the ball and catch it running.  And then there are all those times when he's made those brilliant diving plays in shallow left-center.  He's wicked fast, and to my eye it seems like he takes good routes.  He's been amazing to watch.

Hamilton currently has +11 UZR, which is the fielding metric that FanGraphs uses in WAR.  That ranks him 37th among center fielders over the past decade+,  and 2nd this season behind Kevin Kiermaier.  He is one of only 6 center fielders to have made a play that Inside Edge classified as a "Remote Chance" play, ranks 3rd among CF's among plays ranked as "Unlikely," and 9th among CF's on balls judged as having an "About Even" chance to catch them.

Last year, Hamilton was a +22 UZR center fielder, which is what he's pacing right now.  The best center field seasons of the past decade have been in the 20-30 range, topped by Franklin Gutierrez's 2009 season at +34 runs.  I think most of those are probably overestimates high due to the volatility of fielding stats, but they were all very good fielding players (a young Andruw Jones, Coco Crisp, Carlos Gomez, Michael Bourn, Willy Taveras, etc).

Therefore, I think evaluating Hamilton as a +15-20 run fielder in center field is pretty fair.

Offense + Baserunning + Fielding + Position = WAR

Let's put it all together.

Offense: Hamilton is on pace for -32 batting runs above average.  Let's assume, for now, that he won't improve and go with that.

Baserunning: Let's assume +15 runs as a baserunner

Fielding: I'm going to be slightly conservative and put him as a +15 fielder.

Position: I'll give him the standard +2.5 runs/season bump for CF's, prorated down to +2.1 runs for 86% playing time (he misses games here and there with small injuries due to his playing style).

Replacement: I'll peg Replacement Level as -2.25 wins below average, which is about -20 runs vs. average on the season, prorated down to -17 runs due to playing time (using 9 runs per win).

So, if this is correct, it would be Billy's valuation at:

-32 (offense) + 15 (BsR) + 15 (fielding) + 2 (pos) + 17 (field) = +17 runs above average, or 1.9 WAR.

In other words, Hamilton rates out as almost exactly a league average player.  I'm not arguing he's a plus player, and he's definitely not an all-star.  Just that he's a unique, extreme, league-average ballplayer.  You can even argue that I'm being conservative.  Maybe he's a +20 run fielder.  And his rest of season projections have him about equal to his 2014 offensive performance than his miserable 2015 performance (ZiPS has him at .251/.301/.346 the rest of the way).  But league average feels about right to me.  I really don't think Hamilton is killing the Reds this year, especially not hitting 9th as Price is smartly deploying him.

I don't know how long this will last.  I worry that Hamilton's all-out play in the field and on the bases will slow him down over the coming couple of years.  And if he slows, I don't think his game is amenable to almost any kind of aging.  But for now, I think he's an acceptable starting outfielder.

And who knows?  Maybe he'll still learn to hit a little bit.  After all, even just taking a few more pitches, given the generally poor results when he actually swings, could really help his value.

A Comparison of BIS Quality of Contact to StatCast Batted Ball Velocity

Yesterday, I showed that StatCast's Batted Ball Velocity data is correlated, at least, to performance statistics like ISO and HR/FB rate.  We've received another, similar, data source this year: BIS's Quality of Contact ball classifications.  For each batted ball, BIS classifies the balls as either "Hard," "Medium," or "Soft."  They're not recording actual velocity, but these aren't purely subjective either.  They apparently use a combination of hang time and landing spot information to determine the quality of contact category.

So, the question is, how well do these correlate to the (hopefully) more accurate StatCast data?  Pretty well!

If you look across the top row of the matrix, you see Batted Ball Velocity (on y-axis) plotted against Hard %, Med %, and Soft %.  You can see that it tracks very well with Hard Hit Ball %, in particular, and shows a pretty strong negative correlation with Soft %.  This is just as expected, but it's nice to see the data looking pretty solid.

Interestingly, Batted Ball Velocity also tracks negatively with Med %, though it's a weaker relationship; the harder you hit the ball, the fewer medium-hit balls you will make.  Similarly, Hard Hit % is negatively correlated with Med % and Soft %.

Here's a correlation matrix of those data:

	BBVelo	Hard %	Med %	Soft %
BBVelo	---	0.660	-0.359	-0.502
Hard %	0.660	---	-0.713	-0.554
Med %	-0.359	-0.713	---	-0.185
Soft %	-0.502	-0.554	-0.185	---

Using Quality of Contact as a Surrogate for Batted Ball Velocity

For seasons prior to this one, where we don't have StatCast data available, it would be really nice to be able to estimate batted ball velocity based on these data.  Unfortunately, given how correlated each BIS variable is with the others, it's hard to use more than one of them in a regression because they introduce multicollinearity and, potentially, don't provide much additional information.  To check, however, I did an all possible subsets regression analysis, using combinations of Hard %, Med %, and Soft % variables to predicted batted ball velocity.  Here's the output:

This is kind of a weird figure, but what it shows is the quality of fit (as measured by adjusted R2 on the y-axis) versus the different possible models (shown on the x-axis).  What we see is that a simple regression predicting Batted Ball Velocity with Hard Hit Ball % alone gives an adjusted R2 of 0.43.  It is, by far, the best of the single-variable models.  Furthermore, adding additional variables provides almost no additional explanatory power (it maxes out at 0.46).  Therefore, our best option is to simply predict Batted Ball velocity using Hard %.

If you'd like to do this at home, this is the regression equation: Velocity = 80.69 + 26.6842 * Hard %

This will give you a pretty solid fit:

R2 = 0.43.  It looks like, most of the time, you'll be within about + 5% of the actual batted ball velocity using Hard %.  Maybe it gets better with larger samples; well see later in the season.

But hey, something is better than nothing! Right now, this gives us a basic format that will permit us to look at quality of contact in seasons prior to 2015.  And, like Batted Ball Velocity, Hard % tracks pretty well with variables like ISO and HR/FB...and, like BB Velocity, it does NOT track well with BABIP:

So, in short, while I love using actual velocity data, the Quality of Contact Data--and particularly the Hard % data--provided by BIS looks to be high quality and very usable.

Next up (probably): more of this stuff, but applied to Reds hitters

Batted Ball Velocity Data Predicts Performance

Jay Bruce leads the Reds with 92 mph Batted Ball Velocity,
but his ISO and HR/FB rates a just middle of the pack.
Photo credit: Trev Stair

We've long known that random events can influence hitters' batting lines.  We'll see Jay Bruce crush a ball to deep center field only to see it caught.  And then, the next batter, we'll see Brandon Phillips bloop a "dying quail" over the second baseman's head for a single.  Probably, we'd expect that a hitter's future results will relate better to how hard he hits the ball, rather than his past "luck" in "hitting it where they ain't."

The advent of StatCast batted ball velocity data in the gameday feed is an exciting development in that, for the first time, we have a direct measure of how hard hitters are striking the ball.  The data are still a bit hard to come by; our best source is at Baseball Savant, who scrapes the data together from gameday.  I know others have worked with these data already, but what follows is my first foray into analysis using these data.

Describing batted ball velocity data

I pulled average batted ball velocity data for all players in Savant's database using the link above.  One can do more specific comparisons using his pitchf/x search tool, but I was happy with overall average velocity for a first look (that said, I have no doubt that variation in his number is extremely important).  I linked this up, by name, to hitters from FanGraphs' database.  All data were through May 17, 2015.  Many thanks to these two sites for the data.

After culling out anyone who did not have velocity data, I then stripped down the sample to those with 60 PA or more.  It seemed like a good mid-range number.  That left 291 players in my dataset.  Here's a histogram of their velocity data:

The average batted ball velocity in this set of players was 88.5 mph, and you can see that the distribution is almost perfectly normal.  Very few major league hitters average over 95 mph, and very few average under 82 mph.  The latter is probably a selection process; if you don't hit the ball harder than that, you're not likely to be in the big leagues for long.

How well does batted ball velocity predict offensive numbers?

It seemed to me that there were three primary variables that should be most directly affected by batted ball velocity:

• BABIP: the harder one hits the ball, the more balls in play should fall in as hits.  
• ISO: the harder one hits the ball, the more extra bases you should gain.
• HR/FB: the harder one hits the ball, the more often fly balls should become home runs.

I didn't look at overall performance statistics, like wOBA or OPS, because those numbers will be affected by non-contact events (strikeouts & walks).  Any affect on those summary numbers will occur specifically due to changes in the above statistics.

Let's go through those one by one.

Batting Average on Balls in Play (BABIP)

As it turns out, there was no significant effect of batted ball velocity on BABIP (P = 0.06, R2 = 0.012).  This really surprised me.  But maybe there is some signal that is lost in the overall comparison?  For example, maybe batted ball velocity is better for fly ball hitters, but is worse for ground ball hitters who are trying to beat out infield hits.  

Therefore, I decided to split up my hitters into three groups:

• Ground Ball Hitters: - hitters who had a ground ball % in the upper quartile of the data (a GB% greater than 50%)
• Fly Ball Hitters: hitters who had a ground ball % in the lowest quartile of the data (GB% less than 39.3%).
• Non-GB Non-FB Hitters: hitters who were in the two middle quartiles of the data.

Here's what happened:

Neat, right?  When you look at fly ball hitters, average batted ball velocity does predict BABIP, at least a little bit (p = 0.008, R2 = 0.07).  But there's no relationship for other hitters.  

I guess the lesson here is that BABIP is still a pretty volatile statistic, with other factors (luck/fielding/pitchers/parks/weather) playing a large enough role that it masks any potential effect.  Or, perhaps we need to be even more nuanced; maybe ground ball-speed guys, like Dee Gordon, might have a different relationship with batted ball velocity than ground-ball slow guys?  It's a topic for future study.

Also, another fun thing: there's not really much difference in BABIP between the different hitter types.  FB Hitter = .289,  GB Hitter = .305, Middle 50% Hitters = 0.302.  Compared to the spread in the data, that's not much of a difference.  ....  although it probably matters more in larger samples.

Isolated Power (ISO)

Isolated power (which is SLG - AVG) is a measure of how many of one's hits result in extra bases.  Here's the overall trend:

Batted Ball Velocity does predict isolated power (p < 0.0001, R2 = 0.22).  Here's the breakdown by hitter type, as I did for BABIP:

It looks like the relationship is pretty consistent across hitter types.  The one caveat is that the more fly balls you hit, the higher your ISO and the higher your slope.  In other words, by hitting fly balls, you are going to get more extra bases.  And increasing batted ball velocity results in a more extra bases if you're hitting fly balls than if you're hitting ground balls.  That all makes sense, I think.

Home Run per Fly Ball Ratio (HR/FB)

One more: does hitting the ball harder result in more home runs per fly ball?  For this one, I removed anyone from the dataset who hadn't yet hit a homer...because I don't like 0's when running regressions.

While HR/FB is a notoriously volatile number, even for hitters, there is a significant relationship once again (p < 0.0001, R2 = 0.19).  And if we break down by hitter type:

...much the same story.  Interesting thing with the ground ball hitters, though: despite mostly-similar batted ball velocity, their fly balls turn into home runs at a lower rate than the dedicated fly ball hitters.  This must be a swing angle effect; fly ball hitters probably use an upper-cut swing, and therefore will hit the ball hard and in the air, which converts into home runs.  In contrast, if ground ball hitters have more of a level swing, when they hit it in the air it is likely to be a mistake, and not among their harder-hit balls.  As a result, they turn into outs rather than home runs more often.

Can we predict future regression based on batted ball velocity?

So, we have two variables that are predicted well by batted ball velocity: ISO and HR/FB.  Can we predict players who will regress (positively or negatively) in these statistics based on how hard they've hit the ball thus far?

Let's look at isolated power first.  Here is a graph showing residual ISO (the difference between actual and expected ISO values, based on our regression line) of a bunch of players:

It's messy, but at least you can make out the guys on the extremes.  Players near the top of the graph have higher isolated power than their average batted ball velocity would predict.  In contrast, players with a residual below 0.0 show improvement in their ISO.

Here's a list of the largest residual players:

So, yes, of course the guys who are expected to decline have high ISO's, and the guys expected to improve have low ISO's.  But we've got more precision than just a sort of ISO now.  Giancarlo Stanton, for example, has an ISO of 0.293 currently, and yet he hits the ball so hard that his residual is only slightly positive.  Similar things can be said about Joc Pederson.  On the other side of the coin, Jordan Schafer, Cesar Hernandez, and Ichiro Suzuki all hit the ball very lightly, and so their low ISO's (0.04-0.06) all seem very appropriate.

I don't want to overstate the effect, but this should help us anticipate player who will regress.

Also: Grady Sizemore is playing this year?  I had no idea.

Now, let's do HR/FB:

Again, higher residuals = better HR/FB than expected based on batted ball velocity.  Here's the players who stand out:

Again, we have more information here than just picking the highest and lowest HR/FB guys.  Giancarlo Stanton hits the ball really hard and has a high HR/FB, and this is not disputed by the regression.  Ichiro and Billy Hamilton are at the low end, and that doesn't seem strange.  But when there's a mis-match between HR/FB and BB Velocity, they show up on this chart.

So, maybe we can make better predictions now.  That said, I think a lot more would need to be done before this is ready for any kind of "real" use (in fantasy baseball, or otherwise).  A lot of the guys on the "probably will regress" list are fly ball hitters, and therefore we'd expect a higher HR/FB as a result.  A lot of guys on the "probably will improve" list are ground ball hitters.  At the least, if we're trying to project, we need to take that into consideration.  I'm just not there yet.

Nevertheless, I think this is promising enough that I just put a reminder in my planner to go back and check on this at the start of July and see how we did, compared to players who had similar HR/FB or SLG but had corresponding batted ball velocity.

Next up: how does StatCast batted ball velocity data compare to the BIS Hard-Hit ball data?

The Misery of Jay Bruce

I hadn't looked at FanGraphs' Cincinnati Reds team page lately.  Too painful.  But I popped over there tonight to have a look.  After momentary smiles at what Devin Mesoraco and Todd Frazier have managed this year, and a nod of "yeah, pretty solid" at Billy Hamilton, I found myself scanning through the rest of the numbers with predictable dismay.  I knew it would be bad, and hence my need to stay away until now.  But it is a story of despair and agony.  Cozart's .259 wOBA.  Phillips' injury-shortened season with rate states still down from last year's disappointment.  Votto's 272 PA's.

As I scanned, though, I realized that I was missing Jay Bruce.  I looked again, and couldn't find him.  Finally, I realized that he was on page 2.  Jay Bruce.  -11.7 offensive runs vs. average.  -16 runs in the field by UZR.  -1.3 WAR.  

To call it the worst season of Bruce's career is a massive understatement.  Bruce has always been at least "solid."  This year, he hasn't just been average or disappointing.  He's been disastrous.  With the exception of his base-stealing totals (I haven't looked, but I'm guessing that was from earlier this year?), everything in his line has shown decline.  Walk rates are down.  Strikeout rates are up.  OBP is down.  ISO is down.  BABIP is down.  HR/FB is down.  It goes further:

Bruce's ground ball rate is WAY up.  His fly ball rate is down.  His line drive rate is down.  Bruce has become a groundball machine, which prevents his power from helping him do anything productive...and hence the low ISO.

Earlier this season, he was talking about trying to improve his approach, becoming more selective and looking for his pitch to hit.  His plate discipline profile doesn't match that anymore:

This year, Bruce has swing at more pitches outside the zone, and fewer pitches in the zone, thanany year of his career.  His overall swing rate is down, but that's mostly because pitchers aren't throwing the ball in the zone as often as they did in 2013--if he'll swing out of the zone, they don't have to challenge him.  He's making contact at a decent rate, but it's clearly not hard contact; he's hitting the ball into the ground.

Pitchers aren't throwing that much differently to him.  

A few more fastballs (by pitchf/x, that increase is mostly two-seamer fastballs).  A few more change-ups.  Fewer sliders.  It looks to me like pitchers just aren't as concerned about him this year.  One of the classic ways to get Bruce out was to bury a slider down and in on him, but it's as if pitchers no longer have to rely on that pitch to get through the at-bat.

Earlier this season, I wrote about how Bruce was going to the opposite field more often in 2013.  This year, well:

It's pretty tough for me to say without some summary statistics, but it looks like more of a pull-oriented distribution this year.  For one thing, while he had a nice number of home runs scattered between center field and left field in 2013, every single one of his home runs this year was to his pull side.  Similarly, his ground ball outs (purple) to the infield are all clustered to the pull side.  In the outfield, I'm not sure that I see a specific pattern, and looking only at ground balls, line drives, or fly balls didn't really help (not shown here).  

::sigh::  There's no insight here from me (as usual).  It's been a miserable season, and I certainly don't see anything here that I can identify as something that Bruce needs to do to get better.  He just needs to get better at everything.  My hope is that a big part of the problem has been his leg injury, as Bryan Price has alluded to several times this month.  With an offseason to heal, hopefully Bruce can be in line for comeback player of the year honors in 2015.  If he doesn't I don't see a way to expect much improvement from the Reds' offense...even if they are able to bring in some help.  

How pessimistic should we be?  Well, Bruce's ZIPS projection entering the season was for him to hit .254/.329/.485 with a .344 wOBA.  His updated projection?  .240/.312/.444 with a .328 wOBA.  According to the algorithm, there's still reason to expect him to be a solid hitter.  That's encouraging, despite how bad he's been this year.  But that's a far less intimidating line than he projected to be before his struggles this year.

...I'm not even going to talk about his fielding.  I'm just hoping that's short term injury + fielding stat volatility.  The key word there is hoping.

Using Sound to Scout Players

Sorry for my absence.  Part of it is that the Reds have frankly been rather hard to watch over the past month.  But the issue is that my family and I are living in France for the semester.  We arrived a few weeks ago, and are starting to find our way.  I'm traveling with my university's students in their study abroad program.  It's an amazing experience, but it definitely is hard to keep up on baseball when 7pm EST games start at 1am local time.

In any case, I was listening to Effectively Wild today.  Robert Arthur was on a few weeks back talking about his study that evaluated how the audio signature of the crack of the bat related to the result of the batted ball.  It's great stuff.  His principle finding was that the peak frequency (i.e. pitch) differed substantially between ground ball outs, ground ball hits, line drives, and home runs.  Better-struck balls result in higher frequency sounds.  That makes sense, because those sounds are the result of faster bat speed and more energy being put into the ball.  It's really exciting stuff, and indicates that there's probably quite a lot to the claims that we hear from Baseball People about the sound of balls coming off bats.

The potential applications of these data are really exciting.  The most exciting thing is that these data could be used as another way to evaluate hitters.  Bat crack data should be able to tell us some combination of how hard, and how squarely, batters are hitting balls.  Only those with exceptional power should be able to achieve the highest pitch of bats cracking, once we controlled for bat type and (maybe) pitch type.  Better hitters should have consistently higher pitch than poorer hitters.  My guess is that pitch data should be less noisy than BABIP data, and so they might be useful when we're trying to make "Bonafide or Bonifacio" judgements.

This could be used to evaluate pitchers as well.  Are pitchers really inducing weaker contact?  Or are hitters squaring up the ball well, but just hitting it to defenders unusually more often.

The challenge is the data collection.  MLB compressed games will help, but it's still a lot of data to gather, isolate, sort, and then analyze.  Hopefully some young, enterprising people will go after this, as it has enormous potential.

Reprints: How Hitting Statistics Explain Runs Scored

This week in the SABR101x course, we're covering hitting statistics.  The lesson was very similar to an article I wrote 6 years ago on this site comparing the ability of different offensive stats to predict runs scored (many others have written such articles; it's a classic approach to addressing the question of hitting stat quality).

In it, I argued that there wasn't much of a problem to using OPS if it improved communication because the gains from it to other, better stats (like wOBA) were so meager.  Fortunately, in the time since, FanGraphs has popularized wOBA so much that I feel pretty comfortable just reporting it and ignoring OPS altogether.  And in many cases, I've even moved on to using wRC+ to get the advantages of park controls and run environment-neutrality.  OPS just isn't necessary anymore.

In any case, I thought it would be fun to reproduce that article here.  Here's the most relevant graph.  The rest appears below the jump.

Is Todd Frazier Seeing More Pitches?

John Fay penned a nice profile on Todd Frazier yesterday for the Enquirer.  In it was this little nugget from Todd:

Frazier credited hitting coach Don Long and assistant hitting coach Lee Tinsley for helping him.

"They really don't get too in-depth with me," Frazier said. "But they see little things and help me out with that. Everything's been nice and slow. I'm seeing a lot of pitches. I rarely do that."

Frazier has been taking so many pitches that his father, Charles, mentioned it.

"He said, 'You look so comfortable up there. I've never seen you take so many pitches in my life. I'm kind of getting frustrated seeing you take the those pitches down the middle.' I said, 'Dad, it's not the pitch I'm looking for.'

"Those Jersey people get a little hasty. They understand now that I'm working on something and it feels pretty good up there."

Here are his FanGraphs plate discipline stats:

Frazier is showing a very slight drop in his overall swing rate this year compared to last year.  That is despite a slight increase in the number of pitches he's seeing in the strike zone (zone % is up 1%).  However, it appears to be due not to taking pitches in the zone (as suggested by the quote).  Rather, Todd is showing an increased tendency to take pitches out of the zone (lower O-Swing%).

This drop in swing percentage also seems to span across all pitch types:

Frazier just loves to swing at offspeed pitches, no?

What does it all mean?

It's hard to pin any one thing to these plate discipline statistics.  The first expectation I would have is that he'd show increased walk rates.  And he's not:

However, he is showing a number of improvements that could be linked to his decreased swinging rates:

• He's shown a big improvement in his strikeout rates, as I discussed in this post.  Todd may be chasing fewer pitches out of the zone, resulting in fewer swinging third strikes.
• He's also showing decreased swinging strike percentages (11% this year after 11.5% last year, 11.8% career).
• He's showing better results when he barrels the bat: his BABIP is up a bit compared to last year (though actually down from his career average of .285), and his home run per fly ball rate is also up (career 13.6%).

Frazier may not be a star, but he's a very nice player to have on a ballclub.  He contributes excellent defense at a demanding defensive position (+11 UZR/150 for his career, very similar DRS numbers), and is an above-average hitter who appears to still be getting better.  He has contributed an average of 3 WAR each of the last two seasons, and is headed for arbitration this offseason for the first time.  Given his tendency for a low batting average, he's likely to be a bargain throughout arbitration, unless he goes crazy with his power numbers.  I'm looking forward to watching him play at the hot corner for years to come.

Should Billy Hamilton swing less?

Embedded in my Rockies series preview at Red Reporter yesterday was this bit on Billy Hamilton.  I thought it was an interesting little digression, even if it's basically amounting to lamenting what a player does not do rather than what they do (a bias I usually try to avoid):

Billy Hamilton returned to the starting lineup Sunday after a long time off, and immediately had an impact. He got on base three times, and scored the first run of the game thanks to an errant (and arguably rushed) throw by Justin Morneau on a bunt single, followed by a Skip Schumaker groundout. This is the first series preview that I've done this year in which his actual wOBA meets or exceeds his projection. There's no question that he's been better since his awful first two weeks. And, according to the measures at FanGraphs, he is already about a third of the way to his projected season WAR. Therefore, the projections, at least, think that what we've seen of Hamilton, in sum, is a good indication of what we'll see from him moving forward.

There was a short but interesting discussion on a recent FanGraphs audio about the Minnesota Twins' apparent new hitting strategy, which basically involves taking a lot of pitches (they rank last in baseball in Swing %). One of the arguments that Dave Cameron made is that the players who stand to benefit most from a more selective approach are hitters like Hamilton. Hamilton is not a guy who gains a lot when he swings the bat, compared to a guy who has a lot of power and can add real value when he places the bat on the ball. Hamilton's offensive value is entirely wrapped up in his ability to get to first base, and little else.

Hamilton's swing rate is actually right about average (45% career vs. 45% MLB average), but that hasn't translated into a lot of extra walks (only 5% walk rate). I think he can be effective when he hits the ball on the ground, but he is not really a ground ball hitter (46% career ground ball rate is about average). Given that he makes good contact (86% career vs. 79% MLB average), however, I'd love to see a Hamilton that adopts a Marco Scutaro-type hitting approach: be patient, take pitches, and only swing when you get your perfect pitch...or when you have to with two strikes. His contact rate should make him an effective two-strike hitter.

Of course, that's easier said than done. Hitters can't just change their approach at the drop of a hat. They are reaction machines that have trained themselves to make split-second decisions in a time frame that doesn't allow a thoughtful, considered approach. One can't discount the argument that a hitter's aggressiveness (or patience) is part of what has made him successful to this point in his career, and trying to become more patient (or more aggressive) could have unintended, negative consequences on other aspects of how a player hits. But that doesn't stop me from dreaming about it!

Jay Bruce Changed His Approach Last Year

Photo credit: Trev Stair

On the heels of yesterday's (rather inconclusive ) post about Matt Adams, I decided to take a closer look at Jay Bruce's career splits.  My main question was how much a batter's splits on how often they hit balls to left, right, and center vary over their career.  I choose the left-handed Bruce because most teams employ the split against him, as he's known as a pull hitter.  He also talks about the importance of using the entire field in interviews, so it's something he's apparently working on.  Here he is, for example, talking about Devin Mesoraco:

"He understands that staying to the big part of the field and really taking what the pitcher gives him is something that's going to beneficial for him," said Jay Bruce. "It's not so much a feast-or-famine thing when you're cutting down the area of the field you use."

First, here are the percent of batted balls that Bruce hit to left, right, and center field (courtesy of FanGraphs):

2014's sample size is insanely low.  I almost didn't include it, but it does provide some insight into why Bruce has struggled so far this year.  In terms of any future prediction, however, I'm ignoring it.

For most of Bruce's career, he's been pretty constant, with a slight trend toward more balls hit to center field and fewer hit to left (his opposite field).  Last year, however, he made a pretty dramatic change.  He hit far fewer balls to his pull side, and far more to left and center field.  In other words, as he referenced, he was using the entire field, not just right field.  It may be a coincidence, but last year he posted the second-highest BABIP of his career (0.322; career 0.294).

Enter Blake Murphy's article this morning.  He reported that:
1) Balls thrown to the outside of the plate are hit to the opposite field more often, and:
2) Pull hitters often hit balls in the air to the opposite field, not on the ground, negating the concern with pitching lefties outside when deploying the infield shift.

So, I went to Baseball Savant to compare Bruce's 2012 and 2013 seasons.  First, here are all of his ground balls and line drives between the two seasons:

Jay Bruce: Line Drives & Ground Balls in 2012 & 2013

The increase in balls hit to left field last season is pretty dramatic--even after we eliminate all of his fly balls.  Bruce was definitely hitting more balls to his opposite field last year.

Now, let's look at what he did on pitches inside and outside.  Here is Bruce on balls thrown to him on the inside half of the plate (zones 3, 6, 9, 12, & 14 at Baseball Savant).

Jay Bruce - Line Drives & Ground Balls on Inside Pitches

Not a big difference.  Pitch Bruce inside and he'll try to pull the ball.  That's more or less what would be expected, because he's a power hitter.

Ok, now, what if you pitch him outside?

Jay Bruce - Ground Balls & Line Drives on Outside Pitches

Wow.  In 2012, if you pitched Bruce outside, he often (usually?) would still hit it to his pull side.  In 2013, however, he started going the other way with the pitch far more often.  And look at the results: he got 75 hits on balls on the outside half of the plate last season, including six home runs (3 were opposite-field).  If you're spraying the ball all over the field, you become far tougher to defend, and you're likely to pick up more hits.

Despite his success, it is worth noting that of his hits to left field in 2013, only eight were on ground balls (and one of those was an infield hit).  All the rest were on line drives, which may or may not be defendable by infielders.  And, as you can see, he's just not making outs on ground balls to third, either (probably because teams are usually shifting on him).  Therefore, if I'm defending Jay Bruce, it makes sense to play your outfield "honest," but you can probably still deploy the shift against him and succeed most of the time.

Now, so far this year, Bruce is struggling.  When he's making contact, he's hitting almost everything to right field.  If he can get back to what he did in 2013 and use the entire field, especially on outside pitches, he stands to see him performance increase.  That's something I'll be watching to see him do in the coming month.