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Fielding
Tuesday, August 19, 2008
UPDATE: For anyone who entered data for any of the following players from the start of the poll 2 days ago until now (Aug 20, Wed, 2pm), please re-enter your data for those players only. I found a small bug which I quickly repaired. All other data has been properly recorded and stored. I apologize for this issue. I should have done a better job at quality control. (This only affected the first record of 11 teams.)
NYA Cano, Robinson
NYN Wright, David
OAK Crosby, Bobby
PHI Utley, Chase
PIT McLouth, Nate
SDN Gonzalez, Adrian
SEA Suzuki, Ichiro
SFN Rowand, Aaron
SLN Glaus, Troy
TBA Iwamura, Akinori
TEX Kinsler, Ian
TOR Overbay, Lyle
***
It’s the 6th Annual Fans’ Scouting Report.
Every year, I get a jump in participation, from the first year where I had close to 500 ballots, to last year where it exceeded 2000 ballots. If you’ve enjoyed my blog, appreciated my work, or somehow wondered what you could do in return, this is it. All I ask from you is 5-10 minutes of your time, and we’ll call it even. And, if you have a blog, please, spread the word.
I am extremely grateful to all those who take a few minutes out of their lives to share their observations with the rest of the fans. Your contributions exceed whatever you think it may be worth. As we continue to build upon the past reports, this data now begins to take on historical value.
I know only of gushing reports about the fielding of Paul Blair. Mickey Stanley sounds like some cross between Darin Erstad and Endy Chavez. That is based on the writings of the generation that preceded me. In my generation, I can only attest to the fielding prowess of Gary Pettis. The new generation knows as little about Pettis, as I know of Blair and Stanley. When the next generation comes along, I want to be able to point them to the Fans Scouting Report as the contemporaneous view of the fielding talents of Ichiro and Rolen. And Manny and Dunn. We can breathe life into their fielding accomplishments.
Thank you for your invaluable time.
Tom
Thursday, August 07, 2008
Recently, MGL and David Gassko did a presentation of defense (pitching and fielding) at MIT. MGL sent me his powerpoint presentation (2 MB). I converted it to HTML without all the nice pics and formatting, so for those interested, here you go.
Wednesday, August 06, 2008
There are alot of threads in this blog regarding the relative differences in positions, insofar as fielding is concerned. Here is a recap:
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Tuesday, July 29, 2008
Some great image links to Ibanez playing the field. Open up that page, and search “really, really, really, really, REALLY bad”. Fun stuff. Reminds me of Lonnie Smith.
Kudos to the site for making it super easy to read, and that article was also a pleasure to read.
When it comes to discussions about fielding, there is no, and I mean no, reason to listen to a single person about his observations. Really. Just because somebody says that Dunn isn’t that bad or Ibanez is horrible doesn’t mean we give it more weight because it’s on a blog, in print, or on the radio. That person’s observation is simply one in a pool, and I give it its due respect. Reds fans hated Dunn in 2007. And just as much in 2006. Though they didn’t hate him as much in 2005. And they saw him as an average corner OF in 2004. These observations actually mirror UZR fairly closely for those 4 years. From 2003-2007, Adam Dunn has the 4th worst total in UZR for outfielders. And Ibanez is also terrible in UZR. There is a 3 win difference between Endy Chavez and either of these guys with the glove. That is not something to sneeze at.
Thursday, July 24, 2008
I love these old articles, and I mean really old articles, that shows how little we’ve come.
What is great about this article is that it gives you a true model. Not all the mathematical gymnastics that gives sabermetrics a (much deserved) black eye. No. The author watches the baseball game like a baseball fan, or baseball expert. SME, or subject matter expert, in the real business world. He observes, and he tries to construct a model around those observations. Once you have that, you can try to construct, and deconstruct, a real-life baseball world.
In one small part, one can do this for the “clogging the bases” theory. It’s all fine and dandy to say that a slow runner will clog the bases for a fast runner. But, now you have to sit down and actually observe things, create a model. Once you do that, once you look at it like an SME, and not a gasbag on parade, you realize how almost entirely foolish the clogging theory is.
So, when trying to analyze fielding, model the baseball world first. The author, a hundred years ago, wrote:
In scoring, I place a small “T” above hits I believe too hard to handle, and a small “D” over hits which are doubtful either through bad bounding of the ball or other cause. Of the 424 hits through the infield, 162 were marked “T” and 49 were marked “D.” So the players reached the ball 211 times and failed to field it; and of the 213 times the ball went through untouched 46 were plain hit and run plays in which fielders were going the wrong way, in other words, blundering or being outgeneraled by the batsmen.
I think the current scorekeepers (STATS, BIS, MLB.com) fail us in some respect. They obviously love baseball, but for whatever reason, don’t treat themselves as SMEs. There is tons that they are not recording, things that a hundred years ago, they thought of, and actually recorded (in an unofficial, yet clearly with great care, capacity).
Look at the images at the end of that article, like this one, and tell me why the heck are we so behind the times in 2008, and yet so far ahead of our time in 1910?
Monday, July 14, 2008
This is the average from 2003-2007. This means that 43% of payroll has gone to pitching. This is lovely, since I give out 42.9% of WAR to pitching.
43.2% P
8.4% RF
8.4% 1B
7.9% LF
6.8% 3B
6.3% CF
5.8% C
5.5% SS
4.1% 2B
3.6% DH
Unless 2B are way undervalued relative to 3B and 3B are way overvalued, it seems to me that the average 3B is probably a better player than the average 2B. I’m sure there is some under or over valuation going on, especially if you got a great player arb-eligible, and a not-so-good player who is a free agent being paid the same thing. It would seem that the gap couldn’t be that high even considering that bias.
***
2B+SS+3B (IF) get 16.5% of the payroll, while the OF gets 22.6%. If you have a 90MM payroll, that means your OF are getting 5.5MM more per team than your IF. Offensively-speaking, the OF probably generate about about 3 more wins than the IF. With a 2.5MM per win, that would imply paying 7.5MM for their offense. Since the gap is only 2.0MM, that probably implies that MLB thinks that the IF is about 2MM better than the OF for fielding, or about 0.8 wins total.
My fielding spectrum gives +0.5 wins for SS/CF, 0 wins for 2B/3B, and -0.5 wins for LF/RF. In all, I’m giving -0.5 wins for the OF and +0.5 wins for the IF, for a gap of 1.0 wins.
It seems that teams are paying based on this fielding spectrum.
Tuesday, June 24, 2008
As Pizza and I fight over the best-sounding fielding system (WOWY v OPA!), he offers some background as followup to his introduction last week.
Gotta admit, while wowee is good, it’s really something that kids would say; opa is something that adults say, and with pride around plenty of other adults.
Monday, June 09, 2008
By , 12:22 AM
I am going to present each team’s total UZR so far in 2008 along with what I would have projected given each player’s number of games played:
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Friday, June 06, 2008
I don’t know when THT rolled out Fangraphs WPA stats, but I like it. They give you the team-level totals. The Angels for example are a total of +6.5 wins (meaning their actual wins minus losses is 13 games… as you can see, the player performances are a perfect match to their teams’ record). That breaks down as -2.0 wins for batting, +4.7 for starters, and +3.7 for relievers.
Fangraphs is likely using a run environment that is too high, meaning it gives too much credit to pitchers. The total for the 30 MLB teams should have batting as exactly 0.0 and starters+relievers as exactly 0.0. Checking now… the total of the 30 MLB teams is -51 wins on offense and obviously +51 on pitching. So, until David A. updated the run environment charts he uses for the win expectancies, you need to mentally add +51 wins per 70,000 or so PA to your hitters (i.e., about +0.1 wins per 200 PA), and remove 51 wins per 16,000 or so IP to your pitchers (about -0.2 wins per 70IP). As you can see, no big deal at the player level so far.
Also note that starters are way behind relievers, as they always are. Relievers get the advantage in that their run environment is being compared against a fixed point (say 4.50 runs per game), when in fact relievers, because they are relievers, should actually be compared to a lower run environment. But, the charts I provided David doesn’t allow for that to be handled. I can do it, but it’s a pain in the butt.
In any case, since you likely want to compare to replacement level, not average, you’ll have to make an adjustment anyway, so you might as well do it after-the-fact, and not in real-time.
Anyway, the reason I happen to discover THT’s WPA charts, is that Rally was talking about the Mariners fielding. It seems that Safeco is always at the center of issues with fielding stats. They’ve got close to the league-low in both infield and outfield fielding. It’s hard to believe that a team with Beltre and Betancourt and Ichiro can be that dismal. Either the other players are so dreadful as to bring them all down, or one or all of these guys aren’t as good as their reps.
At some point, I’ll be looking at Safeco’s PBP data to see if there’s something strange with their data.
Saturday, May 31, 2008
By , 02:12 AM
I’m talking about by cutting off hits in the gap (or wherever) and by the threat of a strong and/or accurate arm. As far as I know, none of the defensive systems takes this into consideration. UZR and my “arm lwts” do not.
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Friday, April 25, 2008
By , 06:22 AM
I’m not sure what to make of the data. I think that it is fraught with potential problems, but the results are interesting nonetheless, and a very good first pass by Dan. I am curious to see what the brain trust here thinks of his data.
http://www.beyondtheboxscore.com/2008/4/24/459913/a-strike-is-a-strike-right#comments
Monday, April 21, 2008
One of my issues under consideration is how to handle the situation when the “without” is alot smaller than the “with”. WOWY(*), as you know looks at a player’s performance with some parameter and without. Let’s take the case of Cal Ripken, and some his pitchers (guys who pitcher predominantly, or totally with the Orioles, with Ripken as his shortstop). One such pitcher is Jeff Ballard. He had some 70 innings without Ripken as his shortstop and 700 with. Normally, I scale the “without” data to the “with” because I want to keep my “with” untouched. I want his actual innings, and outs, and balls in play to reflect his actual totals. But, in cases like this, I’m very uncomfortable scaling up by a factor of 10 the “without” data.
I have some options here:
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Thursday, April 03, 2008
No! Are you crazy?
The Marlins will end up trading Ramirez before he hits free agency. The Marlins exist to turn a profit, before they exist to make the playoffs. The trade value of Ramirez is highest if he plays at SS. Since there will be some MLB out there deluded enough to think that he’s a capable shortstop, Marlins will get the most value by keeping him at SS.
I mean, look at what they just did with Cabrera. He should be a corner OF or 1B, but they managed to convince the Tigers that he is a 3B, even though the Tigers already had one of the best fielding 3B in the game today.
It works out like this:
value = wins on offense + fielding wins relative to position + win value of position
Regardless of position, there will be some MLB team that will have “fielding wins relative to position” equal to zero. The win value of SS is 1.0 greater than a corner OF (MLB teams have figured that one out already). That’s 4.4 million dollars every year of non-free agency right there of overvaluation that the Marlins will trade against.
I can’t wait to see which team will overpay for him.
Monday, March 31, 2008
In a very low-key, almost invisible announcement, John Walsh gives us the OF arms on the THT stats pages.
Friday, March 14, 2008
Goalies have save percentages. Now, catchers have block percentage. How real is it? Varitek and Ausmus’s performances are 4 standard deviation from the mean (on the good side), while IRod is 4 SD on the bad side. The standard deviation of all these z-scores is 1.93. This would imply a year-to-year correlation of r=1-1/1.93^2=.73. (If it was not a skill at all, then the SD of the z-scores would have been 1.00.)
The average number of opps was 190 opps. This would imply an r=.50 level at opps = 70 balls in the dirt. (That is, 190 divided by 190+70 equals 0.73.) Dan didn’t provide the sample number of innings for each catcher. If he can tell us that, we can figure out how many games 70 balls in the dirt represents.
Monday, March 03, 2008
See, before you spit out your UZRs and PMRs and the like, you start with the basic information, which USSM does (click on the chart, if you can’t see it in your browser in full). Then, you take the next step, which Lookout Landing does. It’s all part of a progression as to how people think. First, you count the successes, then you give it context to other years. That’s what USSM does. Then, you add an additional context: opportunities. That’s what Lookout Landing does. Then, you refine the opportunities: that’s WOWY and TotalZone can do for you. Then, you make further refinements, and you get PMR and UZR. By the time you get to PMR and UZR, you are a believer. Until then, as long as they stand on their own, they are just a part of a puzzle.
Going straight to UZR or PMR without the other steps is like giving a 14-year old a Porsche.
Tuesday, February 26, 2008
By , 07:06 AM
From 00 to 07, I charted all successful throws from all infielders, including pitchers and catchers, to the first baseman, including the back end of DP’s. I did not include throws where the first baseman made an error or the runner was safe on a hit (late throw, but no error). I only recorded a throw as successful if it led to an out.
I also looked at ROE’s where the fielder made a throwing error. I used STATS data.
So I had lots of “pairs of players” where one player was the first baseman and the other was another infielder. For each pair, I had number of total throws and number of those total throws that were a throwing error.
I then used a similar “with and without you” method that Tango uses and Rally used. First of all, I only included those pairs where a fielder made at least 20 throws to any one first baseman.
To figure the “with and without you” for each first baseman, I went through every fielder who made at least 20 throws to him and subtracted the error rate (throwing errors divided by total throws) for that combo from the corresponding error rate for that fielder (at the same position) and every other first baseman. I weighted this number (the error rate difference) by the minimum of the two “number of throws.” I think Tango always weights by the actual number of throws made to that first baseman, and I am not sure what Rally did.
So then I got a “weighted rate difference” for each first baseman and simply multiplied that by the total number of throws to that first baseman by all of the fielders combined. That gives me a total number of errors above or below average for each first baseman. Each of those is worth around .75 runs I think (the run value of a throwing error plus the absolute value of an out).
Here are the best and worst since 00, in “errors saved per 1000 throws,” with at least 2000 throws to him. 1000 throws is about the equivalent of one full year. To get runs saved per year, just multiply by .75.
Best
Name No. throws Errors saved per 1000 throws (per season)
Mientky 4189 6.0
Sexson 6004 5.2
Berkman 2005 4.8
Conine 3100 4.8
Olerud 4481 4.2
T. Clark 2813 3.1
T. Lee 3996 3.1
Teixera 4335 3.0
Worst
Name No. throws Errors cost per 1000 throws (per season)
Karros 2986 -5.8
Jeff Bagwell 4931 -4.2
Julio Franco 2010 -4.1
Casey 6225 -3.3
Thome 4476 -3.2
Hatteberg 3967 -2.3
Keep in mind that these are sample numbers. If I had to guess a regression rate to estimate true talent, I would say 50% at 2000 (wild guess). Interestingly, if I sum up all the first basemen who had no more than 300 throws in those 8 years, which are mostly fill-ins, they are a combined -2.8 per 1000. So, saving bad throws is definitely a skill that comes with experience, although players with more than 300 but less than 1000 throws did just as well as, if not better than, more experienced players. That might mean that there are quite a few full-time first basemen who are bad at this skill, but are still first basemen because they hit well and cannot play anywhere else, if that makes any sense.
I did not adjust for the pool of “other first basemen” for each first baseman. I agree with Tango that it is likely around zero for most of the players, especially those with large samples of throws themselves. Of course, as with all of the “with and without you’s” you are technically comparing a player to the rest of the players and not to the league as a whole (including himself). Kind of like computing a park factor without including the “other park correction factor” which allows you to include a portion of the home park data in the pool of road park data (so that you can compare a park to ALL the parks in a league and not just the “other parks"). In this case, it shouldn’t really matter much. I’ll put the entire list on Google docs later today. Maybe someone can do an ICC (intra-class correlation) in order to figure the correct regression rate to go from these sample numbers to an estimate of true talent based on the number of throws, although I am not sure if you would regress based on the actual number of throws to each first baseman, the total of the “minimum of the two”, or the total of the harmonic means of all the pairs. Probably the last one.
Thursday, February 21, 2008
Kevin Kernan quotes and comments on Jeter’s view of fielding stats:
“Every [shortstop] doesn’t stay in the same spot, everyone doesn’t have the same pitching. Everyone doesn’t have the same hitters running, it’s impossible to do that.” Jeter, 33, pointed out you can get the exact same ground ball off the exact same pitcher and there could be an average runner or there could be Ichiro running. “How can you compute that?” he asked. You can’t.
Wow. I mean, that is virtually the same quote that Derek Jeter gave to Jack Curry of the NY Times last April. Don’t believe me? Here it is:
‘’They think they have a mathematical equation that figures everything out,’’ Jeter said. ‘’Like every single person is out there with the same runner and the same pitcher and the ball is hit in the same exact place. It seems like once somebody says one thing about you, people tend to run with it and we never hear the end of it.’’
Forget about those “we’ll take it one game at a time” type of crappy Crash Davis approved quotes that players rehearse. Derek Jeter has taken to repeating the same thing about fielding systems to a whole new level.
Anyway, I wrote to Kernan, and asked him to read my article in THT08 (in light of his proclamation that “You can’t” measure Jeter’s objections), and get some feedback from Jeter. I’d like to see that response.
Monday, February 18, 2008
By , 02:02 AM
A few months ago, I wrote an article for THT, in which I looked essentially at two things: One, how speed, as measured by a Bill James-like speed rating correlates with defense, as measured by UZR (a lot, especially in the OF as would be expected), and whether fast players did comparatively better than slow players in large outfields, as is assumed according to CW.
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Sunday, February 17, 2008
From 1957-2006 (excluding 1999), there have been 50 shortstops that have played at least 1037 games at SS (specifically, at least 28,000 outs). This is what I did. In 1977 at Olympic Stadium, ten of these 50 shortstops played there. In all, their teams allowed 4.15 runs per 9 innings, on a total of 1156 innings. In the 336 innings that these ten shortstops did not play at Olympic Stadium in 1977, those teams allowed 4.23 runs per game. It is a reasonable expectation that guys who played the most SS in MLB since 1957 are at least above average fielding SS. And, this little data here points in the right direction: our good fielding SS, when they were on the field, allowed .08 fewer runs per game than the rest of the league.
I repeat this for all parks in 1977. I repeat this for all years. What we end up is the performance of teams with good fielding SS, compared to their counterparts in the same parks in the same years. What are our totals?
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