Buy The Book
I’m going to make one post like this a day, focusing on a team that requires more participation.
Today, it’s the Padres. If you follow the Padres, have a Padres forum you frequent, know where the Padres fans congregate, then send them my way. Or post a link below, and I’ll post in that forum.
This is a discussion of defense (fielding + positional adjustment). I’m reposting from another thread, as it probably deserves its own thread. This is to benefit the newer readers mostly.
***
Dallas:
Let’s take an easy example. You have a bad fielding 1B. He’ll be about -10 runs relative to the average fielding 1B. Not a whole lot, but, 1B don’t have many opps to do damage to begin with (certainly not compared to SS or CF).
The positional adjustment for 1B is -12.5 runs. So a bad fielding 1B has a fielding+positional value of -10 -12.5 = -22.5 runs.
The DH “positional” value is ALSO -22.5 runs.
So, whether you have a bad fielding 1B, or you have a non-fielding DH, both will have an identical “defense” value.
***
When you see players move from CF to the corners, or vice versa, you see a shift of about 10 runs. This is why the positional value of CF is +2.5 and the corner OF is -7.5.
If you have a below average fielder in CF, say a -5 runs (relative to other CF), he will be an above average fielder in the corner (+5, relative to other corner outfielders). In BOTH cases, his “defense” value is -2.5 runs (+2.5 -5, or -7.5 +5).
This is the point of the positional value adjustment, that it puts players on the same scale. (Or tries to anyway.)
The positional adjustment in the OF is solid as a rock.
The positional adjustment in the IF (2B/SS/3B) is somewhat solid.
The positional adjustment BETWEEN OF and IF is a bit shakier, not the least of which because all the lefthanded throwers are in the OF, creating a glut there.
The positional adjustment BETWEEN those 6 positions and 1B is also a bit shaky (but not that much).
The catcher adjustment is a bit of a stab in the dark.
So, I end up with the following adjustments:
+12.5 C
+7.5 SS
+2.5 2B/3B/CF
-7.5 LF/RF
-12.5 1B
-22.5 DH
(DH get a +5 bonus on the hitting side because it’s hard to hit as a DH. You can also even do a similar adjustment for catchers. But, no need to get into that here right now.)
So, you look at that scale, and it kinda makes sense. We know, for a fact, that SS must be higher than 2B/3B: you almost NEVER see a 2B/3B become a SS, but tons of 2B/3B are former SS at some point of their lives.
Same idea for CF and the corners.
You kinda plug the two sets together, the 1B is obviously below the corners, and we suspect the C are above (huge scarcity). And voila, you get a quantified version of the fielding spectrum.
ANY of those numbers can go up or down by 1 or 2 runs. If you want to say the 2B should be +4 and 3B should be +1, I’m not going to argue. If you want to say it should be different by era, I’ll agree (but it won’t change that much, certainly not in my lifetime).
It’s a fairly basic system that crystallizes how you would naturally feel anyway. It passes the sniff test.
So, absent other research someone wants to present, then based on both my research, and the sniff test, that chart is pretty solid.
To argue against it means that you are going to bring something to the table. And, I could be wrong. But, absent actual research, we’ll be arguing between my nose (+ my research) against your nose (and no research).
Why would your nose win?
(Royal “your”.)
In the midst of an article that tried to do too much was some great nugget of research, which I will repost in its entirety:
In the Granderson article I pointed out that the teams in each league which rank highest in outfield UZR for 2011 – Boston and Arizona – also ranked #1 in their league in FB%. This remains true. However, this is obviously not sufficient proof of correlation, for a couple reasons. Not only is there a high possibility of coincidence in any single example, but both the D-Backs and Red Sox feature several outfielders traditionally regarded highly by both sabremetricians and scouts. For anybody who’s watched them consistently, it would be pretty hard to argue that the trio of Gerardo Parra, Chris Young, and Justin Upton isn’t among the best in the major leagues, no matter who’s on the mound.
So, I looked back at all teams that finished at the extremes of the flyball scale since 2003. I do not claim that there is a perfect or, in the parlance of economics, a “strong” correlation. That is, a team with a 35% flyball rate wouldn’t have a dramatic disadvantage in OF UZR compared to one at 38%. There is, however, significant evidence that pitching staffs with extreme batted ball tendencies can dramatically effect their outfielders UZR numbers. (These extremes I defined at upward of 40% at the high end and below 33% at the low end.)
Average OF UZR for FB% > 40.0: 10.1
Average OF UZR for FB% < 33.0: -10.6
Of the sixteen teams at the high end of the range, five finished #1 in their league in OF UZR. Of the 21 teams at the low-end, only five finished with a UZR north of zero.
From these I would point to some interesting pieces of anecdotal evidence:
The 2010 Giants and their 40.7 FB% led the majors in outfield UZR by a substantial margin (40.7 to 31.6), despite the fact that they gave more than 1100 innings to Pat Burrell and Aubrey Huff, lead-footed former DHs who nonetheless somehow finished with positive UZRs for the season.
The 2007 Cubs had an exceptional 44.3 OF UZR in a season where they handed most of the innings to Alfonso Soriano, Jacque Jones, and Cliff Floyd, all of whom substantially outperformed their career numbers with some help from a Chicago staff that sent 40.6% of batted balls in their direction.
On the other side, the ’05 Cardinals, despite featuring some premier outfield talent in Jim Edmonds, Larry Walker, Reggie Sanders, and So Taguchi, finished with a -6.1 OF UZR, thanks to a pitching staff that put only 29.7% of batted balls in the air.
The difference between 30% and 40% can easily be several hundred plays, so when you consider Simon’s point about the significance of even a handful of mistakes in a few months of play, you can see what kind of advantage those extra opportunities provide.
Now, I know that MGL tries to handle the FB% bias. The more GB a team allows, the greater share of those GB that turn into outs. And the more FB a team allows, the greater share of those FB that turn into outs.
The author here, having only access to TEAM FB%, did a decent job of trying to find the bias. So, I’ll put it out there for MGL to verify the claim of bias, but break it down by individual pitchers. Do high FB% pitchers have fielders who end up with high UZR in the OF? And, is that because they happen, by coincidence to be paired to good outfielders, or is the bias more nuanced, more pervasive than it’s being adjusted for?
Thanks to Hippeaux for that great piece of research.
He said so right here:
In 2009, Ellsbury’s UZR — the number of runs he saved versus an average center fielder—was minus-9.7, one of the worst ratings among regular center fielders. Cameron’s was 11.4.
...
However, it’s worth noting that in the MVP-caliber season Ellsbury is having, restored in center field, he is suddenly among the game’s best defensive center fielders, according to the same statistical measure that once condemned him.Ellsbury has an 11.2 UZR this season, second-best among regular center fielders.
That’s around a 20 run swing! How can you possibly have a metric that adds 20 runs to his total value from one year to the next? Obviously, the metric is crappy.
What’s that? He didn’t say anything about home runs? But Jacoby’s HR totals in his two full seasons of 2008, 2009 were 9 and 8. And this year, he’s at 23 and counting. He’s already bumped up his HR count by 15, which means he’s already added at least 20 runs to his value.
So, we have a problem if a metric shows Jacoby increasing his fielding by 20 runs, but we don’t have a problem if a metric shows Jacoby increasing his power by 20 runs?
The fact of the matter is that all metrics are based on observations, and some metrics have an additional uncertainty in terms of counting opportunities. While most opportunities at the plate are created somewhat equal (Jacoby will face Verlander and Royals pitchers, just like everyone else will), not all opportunities on the field are created equal.
So, we’ve got an uncertainty level. I will say that showing UZR to one decimal place is a problem. It implies a level of precision that does not exist.
And then he goes on:
Another problem with numbers is that they’re only as good as their application. There is a baseball statistic called batting average on balls in play that applies to pitchers. Sabermetricians regard this stat as a measure of luck, positing that once a ball is put in play the outcome of the play is largely out of the pitcher’s control.
Based on that theory it’s simply good old-fashioned good fortune that Tigers ace and Cy Young-in-waiting Justin Verlander has a .234 BABIP and Red Sox righthander John Lackey, who earlier this season was getting hit harder than a piñata, sports a.335 BABIP.
Is there a stat to account for putting common sense in play?
Of course he would pick the example that would most support him. How about Pedro Martinez in 1999 (23-4, 2.07 ERA, 313 K, 37 BB) and 2000 (18-6, 1.74 ERA, 284 K, 32 BB)? Two superlative seasons, where Pedro was all a pitcher can be. But his BABIP in 1999 was .325, and in 2000 it was .237. Are we suggesting therefore that Pedro, when the ball was put in play, was really crappy in 1999 but was super awesome in 2000? Or, maybe, just maybe, the number of hits a pitcher allows has alot of components to it, that goes beyond the pitcher’s talent level?
Dude, I have no problem when you say this:
Whatever happened to the good, old-fashioned eye test or context? Formulating an opinion has been replaced by formulas when it comes to dissecting and discussing the games we love. Statistics have overrun sports the same way weeds spread through a deserted parking lot.
If the 1950s-’60s-era debate about who was a better player Mickey Mantle or Willie Mays was happening today, fans would simply look at who had the higher OPS-plus (On base percentage-plus-slugging percentage adjusted for ballpark conditions). It was Mantle. Or they would calculate who possessed the higher average WAR (wins above replacement value). It was Mays.
Where’s the romance in that?
Go ahead and do that! No one is stopping you from the eye test, from the romance, from writing an entire article without quoting a single number. Go ahead and wow us with John Updike prose. We LIKE to read stuff like that. The best sports book I ever read was Ken Dryden’s The Game. I have no idea if he ever used any numbers. If he did, I don’t remember. He wrote a fantastic book nonetheless, and I’d rather re-read that book, then re-read any saber book.
Instead of writing an article saying how you don’t like what we do, why don’t you write an article writing what you do like? And the challenge to you: do so without quoting us a single number. Romance us.
The red shading in that picture represents the area in which an average center fielder will catch the ball more often than not, if the ball is in the air for between three and four seconds (that’s the hang time for most fly balls). The blue shading represents the extra ground Bourjos covers.
“That right there … that’s the truth,” Hunter said Wednesday after looking at the diagram.
“He’s the blue?” asked Angels pitcher Jered Weaver, who has been a major Bourjos beneficiary this season. “That’s ridiculous.”
When Bourjos saw the image, his first reaction was to smile. It was the same smile he flashed when he made his ridiculous catch later that night.
“That’s pretty cool,” he said. “They don’t usually have those sorts of things for defense.”
Curtis Granderson is not the worst-fielding CF in baseball because he’s not the worst-fielding CF in baseball. Or words to that effect.
I’m not giving my opinion either way, but if you are going to argue for someone not being the worst, at least present something, anything. Are you suggesting that he might be the 2nd worst? The best? Where exactly? And who is the worst?
What I really like about the BIS Fielding Good / Bad Plays is that they look at each play, one at a time, and try to categorize it. They’ve got a list of some 80 categories that they put each play in. I’d put that into data recording. This data is the lifeblood of saberists, and the reason that I really appreciate anyone who evaluates each play one at a time. It’s why scouting, true scouting, is valuable: they see what the numbers don’t see.
The next step is to figure out if there’s any bias in their recordings. Until this is done, the data can only be half-useful. Which is still quite useful.
Great piece by Max.
So, let me reiterate the issue. When using indirect standardization (i.e., when using whatever existing fielding metric), you are entitled to say that both Player A (+20 plays) and Player B (+22) performed better than the average shortstop, but there is no way you can infer Player B performed two plays better than Player A.
...I believe fielding metrics should shift to the direct standardization method when data become more objective, detailed and unbiased. Until then the indirect standardization is an improvement over no standardization at all when players face different set of opportunities (but that’s when improper ranking might come out).
Indeed, back in the original UZR, MGL used direct and then switched to indirect after comments at the old Baseball Boards.
Mark gives us some stats:
Lowest Pct of Balls Turned Into Outs
On Balls Hit to RF Hole
Pct
Pirates 70.6
Phillies 72.3
Braves 74.6
Yankees 75.1
Astros 75.3
Diamondbacks 75.3
Brewers 76.1
>> MLB leader:Nationals, Rays: 83.2
We don’t know why of course. Just the what.
According to Mark and/or BIS, this is how much territory Gardner and the average LF covers on balls in the air for 3-4 seconds. Trying to do some quick math, that gives me a coverage area of about 100 feet x 100 feet for the average LF, and 125 x 125 for Gardner. That sounds about right.
UPDATE: The radius as I did the calculation was 58 feet for the average fielder and 70 feet for Gardner. That’s about 20% more distance for Gardner in the same amount of time. Gardner being 20% faster than the average LF would seem to be correct. You can create a more complex model of course, and also that Gardner might take a more direct route, etc.
Ben shows that the Rays have been +164 runs (using Dewan) since 2008. Fangraphs has Dewan’s number at +171 runs:
+171 Rays
+164 Cards
+159 Jays
+146 Mariners
+129 A’s
(huge dropoff after that)
...
-74 Whitesox
-99 Redsox
-206 (!!) Royals
FWIW: UZR actually has Reds and Jays in the negative! It has the Giants comfortably at #2.
Anyway, Ben noted that:
The second part of Maddon’s tweet is also telling: the Rays are very aggressive with defensive positioning. The Rays have easily been the most aggressively shifting team in baseball over the past few seasons. In a division notorious for pull-happy sluggers, the Rays do their best to neutralize the impact of balls in play.
Team Shifts
Rays 256
Indians 168
Mets 151
Brewers 113
Angels 106
Fun look at Mike Scioscia:
Mike Scioscia isn’t a stathead kind of guy. He’s not as anti-stat as you might imagine—he’s extremely wary of sample-size issues, he doesn’t believe in distinguishing between earned and unearned runs, and he thinks pitcher wins are a bad way of measuring starters—but he is extremely skeptical of advanced defensive stats. He says they don’t account for the role of advanced scouting, positioning and, yes, even the catcher’s role in calling pitches that reflect the scouting and positioning. There’s only one defensive statistic that he thinks can accurately reflect the player’s role: Catcher Runs Allowed. “An absolute tool as to how a catcher relates to a pitcher’s performance,” he called it.
“Let me put it to you this way,” he once said. “If you string out 162 games and you have one catcher who is giving up one run a game less when he catches, on the net runs end of it, he’s 162 runs ahead, right? So the other catcher has to produce 162 runs more than the other guy just to break even. I think a catcher is going to influence a game and a season behind the plate more than he is with his four at-bats a night.”
Perfect: the one advanced metric he approves of is one that had essentially been considered disproven for a decade. And the one advanced metric he approves of is the one that conveniently makes him look like he was a superstar.
An A’s fan’s proposal.
Everyone is moving their 1B to 3B! JT has some fun with this, and tries to see the implication. Given that he has some good amount of time at 3B and is no longer there, -10 to -15 would have been my prior.
Steven quotes Colin:
This version of FRAA avoids the pitfall of subjectivity inherent in zone-based ratings. “In contrast to other popular metrics, FRAA does not use any stringer-recorded observational data,” Colin explains. “Serious discrepancies have been noted between data providers, and research has shown that in larger samples use of that sort of batted-ball data introduces severe distortions in the metrics that impede accuracy. Without evidence that the batted-ball data has redeeming value in the short term, it seems imprudent to use that sort of data in our evaluation of player defense.”
First, I think FRAA does use stringer-data, since I believe Colin uses which fielder (is marked as the one who) picked up the basehit. Perhaps that was in a previous version, and Colin decided to go with 100% factual data (which is what my WOWY also does).
The rest of the statement is as much a philosophical point than anything. For example, with WOWY, I intentional limit myself to factual data, not so much because I don’t believe that the subjective data is without value, but because I don’t want to include the uncertainty level of subjective data. In that respect, it’s a bit like FIP, that intentionally focuses on non-BIP events. Colin however goes one step further and is arguing that the status quo should be to be against subjective data until it’s been proven to add value.
Now, he’s asking about evidence, and it’s a good enough point. One way to find that is to run a correlation of UZR and FRAA (and DRA and PMR and whatever else) against next year’s (unadjusted) outs per BIP. That’ll tell us which stat does better.
You do need to be careful here though. Let me take a clearer example that is based on pitching to make my analogy. Let’s say you have FIP and you have SIERA. FIP includes a pitcher’s HR rate as a “skill” component, while SIERA ignores it altogether. And then we run a correlation of FIP and SIERA against next year’s RA9 (runs allowed per 9 innings). Well, if none of the pitchers changes teams, and all your parks are either Petco or Coors, then a pitcher that gives up lots of HR at Coors will continue to give up lots of HR at Coors. And so, his FIP will correlate strongly. SIERA on the other hand won’t know that the pitcher is pitching at Coors.
So, if UZR does a great job of removing park and pitcher bias, but then you have a bunch of fielders who have the same pitchers and parks, then you are comparing a context-adjusted metric (UZR) against a non-context-adjusted metric (outs per BIP). The more you adjust, the less you make your metric correlate against next year’s stat! (To a point.)
One way around this is to only look at players that switch teams. This way, you increase the uncertainty, but at least you won’t have a bias.
That’s what I would recommend, that you look at say all SS from 1993-2009 who switched teams in the following year, and then run a correlation of their UZR and FRAA to the next year’s outs per BIP. Repeat this for 3B, 2B, OF, and let’s see what we get.
I think this would move the discussion forward from a theoretical objection, to a practical one.
Kahrl did her darndest to highlight an easy saber-sell: OF arms. There’s really not much disagreement among the systems. And the systems also are loosely linked to the Fans Scouting Report.
But seeing the comments in there shows how fans really want an explanation when their guy is not in the leader boards. The Fans Scouting Report has Jay Bruce at the top, matching some of those commenters. And had Kahrl gone back to 2009-2011 instead, Bruce would come in at #4 among RF.
So, I’m on board with the way Kahrl went about it. But two things to make sure to present it better:
1. When you can go two years instead of 1, do it.
2. Acknowledge the “obvious” guys that should rank higher, but don’t, as at least a sample size issue that limits the usefulness of metrics to some extent. Or maybe they are not as good. But you have to talk about them so that they are not ignored.
A good example is Justin Verlander. He should be awesome, he is awesome, but his ERA is not awesome. You have to talk about why to some extent. Same thing here with Jay Bruce. And had she gone to two years, it would not be an issue, because he’s right there.
Anyway, good job overall, and just need to sharpen the edges.
Seminar with Shane Jensen, at Cornell, Tue, Apr 26.
Who wants to attend and take notes?
Glove-slap Dan.
May 16 22:50
Dodgers’ win reversed because Mattingly did not attest to proper score!
May 16 20:44
How to beat the shift
May 16 20:02
Sponsoring MLB jerseys
May 16 19:34
Now you frame it, now you don’t
May 16 16:56
Did Manny Pacquaio actually quote Leviticus?
May 16 16:06
Does changing your pitch frequency lead to substantial change in results?
May 16 14:18
Extra Innings: One-minute review
May 16 14:16
This particular criticism of UZR is unfounded
May 16 13:21
Psst… wanna intern for the Astros?
May 16 12:23
Arena wars
THREADS
May 16, 2012
Now you frame it, now you don’t
May 16, 2012
Dodgers’ win reversed because Mattingly did not attest to proper score!
May 16, 2012
Does changing your pitch frequency lead to substantial change in results?
May 16, 2012
Sponsoring MLB jerseys
May 15, 2012
Andre The Hawk Dawson speaks
May 15, 2012
Euro 2012 Preview
May 15, 2012
How to beat the shift
May 15, 2012
Will Pujols end the season with at least 30 HR and .500 SLG?
May 15, 2012
Kershaw v Strasburg, part 2
May 15, 2012
Did Manny Pacquaio actually quote Leviticus?
Recent comments
Older comments
Page 3 of 342 pages « First < 1 2 3 4 5 > Last »Complete Archive – By Category
Complete Archive – By Date