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Sabermetrics
Friday, February 10, 2012
Poz gives us an overview.
The only consideration you MAY have is that you have the peak in mid to late 20s because that’s when you have the most number of players to begin with (you can’t have 100 players at 6 WAR at age 18, if you don’t have 100 players to begin withl but you don’t have that, because they are not good enough at age 18).
Anyway, you could add the RATE of players who get 6 WAR, and that will give you… well… something. I don’t know what, but, something maybe.
Thursday, February 09, 2012
Not for the Expos, but the next best thing: the Jays. The one you want is probably “Intern - Baseball Operations”, but there are a few other non-tech jobs if you prefer those.
Tell ‘em you heard it from Tango, and it will help.
Wednesday, February 08, 2012
Glove-slap: NaOH.
Jeffrey S. Moorad, ’81, vice chairman and CEO of the San Diego Padres, headlines a panel of baseball and television executives at the Villanova Sports and Entertainment Law Journal Symposium, “Moneyball’s Impact on Business and Sports” on Friday, February 10 at noon.
Former Governor Edward G. Rendell, ’68, will be the moderator of the panel that also features Billy Beane, vice president and general manager of the Oakland A’s, Omar Minaya, senior vice president of baseball operations for the Padres and former general manager of the New York Mets, and Phil Griffin, president of MSNBC.
...
The symposium will be held from 12 until 2 p.m. on Friday, February 10, at the Pavilion on the campus of Villanova University. The event will be simulcast live to the Villanova Law website at http://www.law.villanova.edu. **Due to popular demand, the location of the event has been moved to The Pavilion**
There will be no cost for this event, and no CLE credits are offered. On-line registration is required; please click here to reserve a seat at the symposium.
I’ll be home in time to catch him on the 7:30pm broadcast. Should be fun!
UPDATE:
This was quite a surprising claim from Colin:
These values are on a very different scale, since due to the lack of an intercept the values have to sum to one for the first regression and to three for the second regression, but they’re also very different in a more meaningful sense; recasting the first year to 1 (which is practically already done for us), we get weights of 1/.92/.90.
As you know, Marcel uses 5/4/3 for hitters (meaning 1, .8, .6) and 3/2/1 for pitchers (1, .67, .5). (I think it was 3/2/1… can’t confirm right now.)
I personally use .9994^daysAgo for hitters and .9990^daysAgo for pitchers, which has the effect of being 1, .8, .64, .512 (and so on, each 80% of the previous) for hitters, and 1, .7, .49 (and so on, each 70% of the previous) for pitchers.
Tests from other research makes me think that it should be even more aggressive, so maybe 1, .7, .5 for hitters and 1, .5, .25 for pitchers. But, I haven’t researched that, so, I’ll just leave it there for now.
Colin has gone way to the other side, essentially going with a .9998 or .9999^daysAgo kind of model.
Now, I agree with the framework for his testing, that you should and must include the PA component when establishing the weights. Frankly, this is an important step. When I did it for Marcel, I basically forced everyone in the system to have at least 300 PA, so that I didn’t have to worry about this portion too much (I should have worried a little about this, at least). Indeed, if you give everyone at least 500 PA for each of the three years, this step becomes basically unimportant (no worries at all). That’s because the weighting of each year (the PA of year 1 divided by the PA of years 1 + 2 + 3) will be the same for each wOBA of year 1, 2, and 3.
So, getting back to Colin’s important point: he’s saying that if you introduce the PA weighting component, we see that every year is important. I find this very hard to believe. I mean, it’s an exciting finding if true, and I’d like to see more research on this for sure. My guess at the moment is that there’s a selection bias issue, with guys of limited number of years, or for young guys.
Basically, does Colin’s finding apply across-the-board, or is it really limited to a subset of the population? I’d bet on the latter, and I’d bet that the Marcel 5/4/3 would still hold for players who are regulars. In any case, it’s an exciting prospect to consider.
***
A correction to Colin’s note here:
The third, and perhaps most important, takeaway has to do with regression to the mean. We can add a simplistic version of regression to the mean to our forecasting model by adding a TAv_REG of .260 (the league average) with a PA_REG of 1200. (The PA_REG comes from the Marcels; it’s included here mostly for the purposes of illustration. The regression component in PECOTA is a more rigorous model based on random binomial variance—again, the purpose here is only to illustrate the concepts.
Consider a player with 650 PAs in three straight seasons, or 1950 total PA. Using the Marcel weighting of 1/.8/.6, that comes out to 1560 effective PA— in other words, throwing out 20 percent of a player’s PAs during that time period. That means 56 percent of a player’s forecast comes from his own performance, and 44 percent comes from the regression to the mean component. Using weights of 1/.92/.90 yields 1833 effective PA, throwing out only about six percent. Using the same regression component, that’s 60 percent of a player’s forecast coming from his own production and only 40 percent coming from regression to the mean. (And if you follow from the conclusions above and start using more years to forecast a player as well, even less regression to the mean is necessary.)
There’s a calculation error in there. Marcel uses 5/4/3/2 model, with the 5/4/3 being the weights for years T, T-1, T-2, and the 2 being the weight for regression toward the mean (using 600 PA as the seasonal number). So, if you had say 700 PA in year T, 400 in year T-1, and 500 in year T-2, you get these effective weights:
year T: 700 x 5
year T-1: 400 x 4
year T-2: 500 x 3
regression: 600 x 2
That 600x2 is the same for everyone. Colin’s calculation error is that rather than using 5/4/3, he used 1/.8/.6. The net effect is that he showing a far bigger regression amount than Marcel is actually doing.
Tuesday, February 07, 2012
Oh, about 4MM$ a year for the next 3-4 years. Kershaw signed two years, to forego his first two arb years. How much would he have gotten, had he had the same performance, but not won the Cy?
I think Matt would be in a better position to answer that. However, if we look at the big four (Felix, Verlander, JJ, Weaver), we see a pretty typical pattern for one year deals, and what we should consider as the upper limit: 3-4MM$ the first year, 7-8MM$ the second year, 13-14MM$ the third year, and 20MM$ if it gets there for a 4th year.
As Dave reminds us, young guys sign away their early years as well. Lincecum got 2/23 for his first two years, but he came off TWO Cy Youngs.
Cole Hamels signed 3/20.5, which is a discount had he gone year-to-year like the Big 4 did (25MM$ or so for their first 3 years).
Kershaw just signed 2/19, and one would think that if he signed for a third year, he’d have gotten another 15MM$ or so to come in at 3/34. Signing multi-year also means you are getting a discount, so, that probably means he’d have expected to get say 38MM$ had he gone year-to-year, compared to the 25MM$ the Big 4 got. That’s a 13MM$ bonus for Kershaw over the Big 4 for those 3 years. Or 15MM$ bonus over Hamels.
Basically, a Cy Young really takes the sting out of arbitration, and accelerates a pitcher’s service clock by one year. What Lincecum did for his year 1 and year 2 arb deal (of 2/23) makes more sense to think of it for his year 2 and year 3 arb deal. (The Big 4 for example were paid about 21MM$ going year-to-year.) Same thing with Kershaw, who signed for 2/19, which is very much in line with thinking that his Cy Young accelerated his service time, since it matches to the Big 4’s year 2 and year 3.
I didn’t realize how few people actually parked when going to a Yankees game.
Monday, February 06, 2012
I love the idea, off the bat.
But, I don’t like the question. The peripherals already includes the diversity. A better thing to ask is if a pitcher with more diversity performs better than someone with less diversity, if you control for the fact they have the same fastball speed. (We will end up with a selection bias at the low-speed end, but not the high-speed. Well, we might, because we don’t have a measure for strike zone location, which, really, is what pitching is all about.)
Anyway, would love to see more work on this.
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Comments • 2012/02/09
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Sabermetrics
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Poll
ESPN has the list of story lines.
Sunday, February 05, 2012
I’ve been running the Scouting Report and the Community Survey balloting because I firmly believe in their value. The scouting report gives “shape” to a player’s fielding description, while I’ll put up the Community up against any one’s playing time forecasts.
But for the Forecaster’s Challenge… well, I’m wondering if I’ve said everything that needs to be said about that. Am I doing it simply to keep doing it, or am I learning something new. I don’t think I’m learning something new any longer.
What say you?
Saturday, February 04, 2012
Great stuff available at BrooksBaseball.
It’s interesting to me when we classify things as “Fastball”, when obviously a Moyer fastball and a Strasburg fastball moves differently. First, there’s the difference in speed (16mph difference), which means that gravity has more time to impact a Moyer fastball (you can see this in the charts a bit further down). The end result is that a Moyer fastball sinks more than a Strasburg sinker.
But, then there’s also those pitchers that throw alot of fastball AND sinkers, and so, they will try to differentiate them enough so they can get value from the two pitches. For example, Strasburg doesn’t throw a sinker alot, but when he does, the movement is a couple of inches different from his fastball. It’s also two mph slower. Moyer on the other hand throws his sinker and fastball at the same speed, but it has significantly different movement on the pitches: 7 inches on the horizontal and 5 inches on the vertical.
And it’s not just Moyer. CC also throws fastballs and sinkers, and the speed difference is only 1 mph, the horizontal is 6 inches and the vertical is 3. Felix throws his sinker and fastball at the same speed, with 7 inches of difference on the horizontal and 3 on the vertical.
Verlander is like Strasburg, in that he rarely throws a sinker, and he gets 3 inches on the horizontal, and 4 on the vertical, at the same speed (so, a bit more movement differentiation than Strasburg, but not as much as CC/Felix). And maybe the reason is that his fastball already gets so much movement to begin with. Verlander’s fastball on the horizontal moves as much as Felix’s sinker on the horizontal. So, he really doesn’t have much room to maneuver there. One might think that Verlander can improve his repertoire by following a Felix/CC model of limiting movement on the fastball, to differentiate it more to the sinker. But, it’s hard to argue with the success of Verlander/Strasburg to think that they can actually pitch better.
Anyway, all to say that since a Verlander/Strasburg fastball really lies half-way between a Felix/CC fastball/sinker, that it’s not really helpful to simply classify their pitches as “fastball”. It’s really a Verlander-fastball and a CC-sinker and a CC-fastball.
I think it’s perfectly fine that when you treat the pitcher as his own universe, that we stick with the standard classifications. But, when we combine pitchers, it’s may be more helpful to distinguish them based on their movement and speed, rather than how they clustered for a particular pitcher.
By , 06:48 PM
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…
Friday, February 03, 2012
Good job trying to identify the Jayson Werth comps. It looks like Ichiro is the most comparable case for Werth.
It’s not that RBIs are ”wrong”, it’s that the context is missing. Wins for pitchers aren’t wrong. But, the attribution pretends that the hitters, bullpen, and fielding is absorbed by the pitcher.
Sabermetrics is about the hidden game of numbers. It’s trying to give context, meaning to those numbers.
Numbers, that stand there by themselves, are meaningless, as surely as words strung together have no meaning without understanding the rules.
When I read that blog post above, I see someone who looks at the surface, sees some heads bobbing out of the water. But how can you tell which is Jessica Alba and which is not? You need to dive into the water, and that’s what sabermetrics does. You can swin along and not drown, or you can just wait for us to drain the water out of the pool.
Same service time, similar in age. Over the last 4 years, 125 starts for one and 124 for the other. 48-43 for one and 44-41 for the other. 3.77 ERA to 3.72 ERA. 12 innings apart. One gave up 6 more HR, the other gave up 15 more 2b+3b. 59 more K, but 25 more walks+hit batters. Even their contracts over their last two years: 9.3MM$ for one and 9.45MM$ for the other.
Danks of course signed that big contract extension which we talked about, with the first year coming in at 8MM$. It’s no surprise that Garza was going to eventually settle at something a bit above that figure. (In the Danks thread, I had figured close to 10MM.) He’s not at the Felix/Verlander/JJ/Weaver level, so he wasn’t going to get 13-14MM$. He’s one level below that, and 9MM$ is right about there.
It’ll be interesting if he ends up signing a 5/65 deal like Danks did to supplant the arb deal.
Fun stuff from Poz. This would be quite simple to program. You create 100 threads, all starting with the same 15 names. Someone logs in, gets assigned a thread, and has 60 seconds to put in a name, and then the next person in queue gets an awaiting thread, and he puts in his answer. This way, you’ve also got some open thread.
You compile the results, and bam, 8500 votes later, we’ve got Poz’s answer.
from Amazon
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