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They overhauled their glossary, so it should be a good reference especially for the newbies.
I picked out a couple just to see what they had. Since FIP is nothing more than a short-hand to DIPS, I think a good addition would be something like “FIP is a popular [ubiquitous?] short-hand for Voros’ DIPS construction”. Voros deserves the lion’s share of the credit for FIP. Not sure what the THT and Fangraphs glossaries say about FIP.
This is a great little addition by David, so kudos to him.
David has now added age, which is fantastic. Here for example are the best players aged 21-27, since 1969.
Great job here!
One important note is that Marcel should be on the x-axis, and the actuals on the y-axis. So, when he lets you set the “minimum threshold”, say like “30 HR”, that should be “30 forecasted HR”, not “30 actual HR”. You can’t ask for all the guys who were observed to hit at least 30 HR, and ask for what the forecast was for those guys. By definition, any group who was observed to be high will include more good luck than bad luck (as a group, on average).
Sounds like this guy and Fangraphs ought to get together on… something.
In this great article by Mike Fast in BP a few months ago, he described a method by which he estimated catcher framing performance using Pitch f/x data. He was generous enough to provide a complete database for all catchers in 07-11.
From those numbers I computed an estimate of each catcher’s framing true talent by simply taking his total observed numbers and regressing toward the mean (zero) by adding 4500 called pitches (about 75 called pitches per game, BTW) of league average framing (zero of course), as he suggests in the article. I did not do any weighting by year, age adjustments or anything like that. I just used the 4 year combined numbers that Mike provided. (BTW, I later learned that there was an error in Mike’s computations, so I multiplied his run values by .65, as per Mike).
To test his numbers, I first broke the list of catchers and their true talent framing skill into two groups of around 25 players each (an arbitrary number of players in each group) - the best and the worst. The average framing skill in the best group, weighted by the number of PA they caught in 07-11, was +7.5 runs per 150 games, and for the worst group, it was -7.7. That is around a .05 runs per game influence, which would show up in their pitcher’s ERA, RA9, or ERC (component ERA). Only a part of that would show up in DIPS or FIP, since framing also influences BABIP.
Anyway, to test his number, I did a WOWY on those catchers. I looked at the results of all pitchers they caught when they were in the game and when they were not. I did not control for anything else, like park, batters, H/A, etc. A pretty standard WOWY analysis. We can thank Tango for that, BTW. I then looked at the WOWY differences in wOBA, SO, and BB rates.
I looked at 05-11 for some reason rather than just 07-11. So I used some in-sample data (07-11) and some out-of-sample data (05-06). The average catcher in the “good framing group” this time pro-rated to the number of PA they caught in 05-11 (rather than just 07-11) was +7.3 and for the “bad framing group”, -7.6, around the same as for 07-11. IOW, also around .05 runs per game.
Here are the results:
The good framing group had a wOBA difference of .008 points. IOW, looking at the same pitchers, when the good framing catchers caught them they allowed a wOBA of 8 points less than when some other catcher (a slightly bad framing catcher, on the average) caught them. That translates to around .24 runs per game - a lot more than we expected. The BB per PA had a .004 difference (around .15 fewer BB per game) and the K was .003/PA (.11 per game) more.
For the bad framing catchers, they had a .003 higher wOBA, or .09 runs per game, .11/game more BB, and .23/game fewer K. The runs per game number is also more than we expected.
However, we expect to find much more of a WOWY effect in the in-sample data than is expected using the regressed in-sample framing data, because the actual framing performance of these good and bad framing catchers was much more spread out than the estimated true talent numbers (the regressed performance).
The total number of “min” PA were 302, 434 for the bad framers and 88,738 for the good framers. So the standard error in wOBA is around 1.7 points for the good framers and .9 points for the bad framers. (That is not exactly how you do a standard error for a WOWY; in fact, the real SE’s might be almost double since a WOWY is a difference between two numbers.)
Now, this is not such a great test because most of the data is in-sample (07-11). IOW, in the WOWY test, I used the same data that Mike used to come up with his catcher framing numbers. While he did not use the same method at all (WOWY), it is possible that there are some dependency issues.
The best way to test his numbers is to use out of sample data (and hope that the catchers had around the same skill that they had with the in-sample data).
So first I only used Mike’s data from 07-09 (and did the appropriate regression of course) and then I did a WOWY from 05-06, and 10-11 (4 years).
The average catcher in the bad framing group (based on only 07-09 framing numbers), prorated by the number of PA they caught in 05-06 and 10-11, was -8.9 per 150, and in the good group, +8.1. That is around .057 runs per game.
Here are the results of the out-of-sample WOWY. These numbers should be close to (rather than larger) the true talent estimates, unlike the in-sample numbers.
Bad framers
wOBA diff: .09 runs/game
BB diff: .114 BB/game
K diff: .19/game
Good framers
wOBA diff: .03 runs/game
BB diff: .076 BB/game
K diff: .114/game
These numbers combined, (.03 + .09)/2, or .06 runs per game, are exactly in line with what we would expect from Mike’s numbers, which is very comforting. In fact, I love it!
Later today, I will do the same test on Max Marchi’s numbers, which were also derived from the pitch f/x data, but use a different method I think…
I downloaded Bill James Baseball IQ onto my iphone (I don’t think it is available on droid phones, but I’m not sure). Here is the web site for the app on Acta Sports:
http://www.actasports.com/titles/bill_james_baseball_iq_app/
It is pretty cool. You can read a description and see some screen captures on the above site, but basically it allows you to see heat maps and color maps of batters and pitchers (in all combinations, counts, situations, etc.) for K zone, batted balls, pitch type, etc.
Best of all, the app is free! Seems to me that they could have charged for this one, but I know nothing about the best way to make money from apps. It also seems like they could use these graphics more often on TV broadcasts.
Anyway, give it a try and see what you think…
I’ve received a few requests in the last two weeks for an injury database.
I know there were a couple out there floating around. Josh maybe? Or Zimm? Anyway, if someone wants to help out the community, please post a link to your injury DB. Ideally, you have it cross-referenced to MLBAM or Retro IDs.
Does anyone know of an up-to-date (including the 2011 season) web site (or some other place) that has MLB ID’s of players?
This one:
https://github.com/geoffharcourt/mlb_rosetta
Does not seem to be updated anymore.
And I don’t think the Lahman or BDB includes 2011. Does anyone know if those will be updated to include 2011?
Clay is giving it to us… the raw data, the translated data, and the “forecasted” data. Great stuff.
I don’t know if this is available for the public, or for BPro-subs. A great tool. (Presumably at least the article is available to the public, because it sells the app.)
Seems to me this is the kind of thing you’d want for an iPhone especially.
What is the addition of Pujols and Iannetta worth to the Angels, assuming they take Mathis’ and Abreue’s place?
I ran a quick sim against an average team. The gain is 5.5 wins per 150 games versus a RHP, and 10.4 wins versus a LHP.
Interestingly, of that 5.5 win gain versus RHP, 3.6 is from Iannetta over Mathis (assuming roughly equal defensive value), which leaves only 1.9 for Pujols.
Against LHP, only 2.1 of those wins are from Iannetta/Mathis, so 8.3 are from Pujols/Abreu.
Anyway, if we assume 2/3 of their PA are against RHP, we get a total upgrade of 7.1 wins.
This probably overstates the upgrade, though, since Mathis was not nearly the full time catcher last year (he only played in 93 games).
Some updates on Fangraphs. Check out the various tabs in there. Tons of good stuff.
And, if you have suggestions, post them here. David is pretty much incomparable in terms of turnaround time of taking suggestions and implementing them.
Brian Burke links to it, and gives it the love it deserves.
Fangraphs rolls out even more customization options.
The one that I asked David to do was to be able to see a player’s stats split between teams. This would be useful in cases like KRod, and you want to see his LI with the Mets and Brewers in 2011 in the leaderboards. I asked David about this just last week, so the turnaround is just fantastic.
I might have to push him on other stuff that I use BR.com for, like times through the order for starting pitchers, etc. Having it on a leaderboard, easily exportable, sure is a timesaver.
BPro rolled it out. Check back a little later, as I wasn’t able to get any data yet.
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?
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