from Amazon
Sunday, February 12, 2012
Whitney Houston
A collection of some of her best songs in one spot, plus her Grammy performance in 1989.
Saturday, February 11, 2012
NHL Injury Dataset
Great stuff, courtesy of Gabe.
http://behindthenet.ca/scratches.txt
Why do players get crappy caps?
Brandon McCarthy is asking on behalf of most players:
The hats: Really scraping the bottom of the barrel here, but I think this is the most common complaint. MLB issues these spring training hats that are pretty universally hated. They usually have a silly line(s) on them so they’re different from the regular season hats, and the material feels like a wet dish rag if you sweat in them (which you will). Every day that you put them on, you get the same sad feeling Ralphie got in A Christmas Story when he’s wearing the footed pajamas.
Clutch analogy
In reading my old article on Clutch, I was struck with an analogy.
Reds fans hated having Adam Dunn up in the clutch. They look at him like driving an SUV in the middle lane, never diverting from that lane, regardless of the traffic situation. He’s all about HR, BB, SO, and come hell or high water, he’s going to stick to his plan.
The other hitters they had at the time, Encarnacion, Phillips, Junior, Hatteberg, et al, are driving a Civic, bobbing and weaving through traffic, making big gains on occasion, though losing time with all the lane-changing and the occasional bad lane change. Sometimes, those balls in play go through the fielders, and some times, well, the fielders keep getting them out.
Look at them five miles later, and Dunn ends up ahead anyway. The fans simply wants to see action, and, like Vegas tourists and lane-shifting drivers, they’ll remember all the clutch hits, winnings, and speed gains, and simply downplay the outs in the clutch and getting stuck behind traffic and Vegas losses.
A mile gained is a mile gained, but a mile lost was unavoidable anyway.
Who is Jeremy Lin?
The story begins:
This kid out of nowhere – out of Harvard University, out of the Reno Bighorns and Erie Bayhawks – had done it again, done it with a devastating 38 points, seven assists, four rebounds and two steals in the Knicks’ 92-85 victory over the Lakers.
“Players don’t come out of nowhere,” Bryant said.
What he was trying to say was this: The talent’s there, but sometimes the opportunity isn’t. It takes the right circumstances and timing, the right coach, right system. And sometimes, it takes desperation to try anything. And for these New York Knicks, well, Jeremy Lin constituted anything.
...
Twenty-four hours earlier, Bryant had been bemused over this Lin story. He wanted details, wanted to know the fuss. “Well, he’s got to deal with me now,” Bryant said.
...
No one in the history of the NBA has scored as much in his first three starts, but this has nothing to do with the statistics. It’s the feel, the touch, the spirit and the purity of it all. No Amar’e Stoudemire. No Carmelo Anthony. And it doesn’t matter what spare parts are thrown together with Lin because he’s elevated everyone, transformed five fingers into a fist.“It’s a completely different team,” center Tyson Chandler said. “You can’t look at this team the same.”
...
Before D’Antoni had run out of players to try for these Knicks a week ago, before he had thrown Lin into a game with the New Jersey Nets, the Knicks’ front office had a decision to make: Do we guarantee Lin’s contract for the rest of the season, or release him with Tuesday’s deadline?Knicks executive Mark Warkentien had been calling trusted associates in the NBA’s D-League, league sources told Yahoo! Sports, and asking them: Who does Lin play like? Who’s a good comparison? The Knicks had to make a decision based on old information, old scouting reports. And then, finally, D’Antoni dispatched Lin into the game against the Nets. Here was the answer, the unfolding of a week that has made Lin a sporting and cultural spectacle.
“A great story,” Bryant said. “It’s a testament to perseverance and hard work. A good example for kids everywhere.”
Those who worked with Lin in the D-League a year ago will tell you: He’s so grounded, so smart, so savvy, that he’s the perfect person to keep his bearings within a world exploding around him. Lin shrugs and simply says, “I am not really too worried about proving anything to anybody.”
And his wiki page reads as someone with steak and little sizzle, and recruiters prefer the sizzle.
Friday, February 10, 2012
Jose Molina
Max gives us a component-by-component analysis of Jose Molina. Bottom-line: if you believe he controls the strike zone, then he’s a great catcher.
Reader Mail of the Day: Why do we need X years of fielding data? And what about outliers?
Pete:
When it’s said 3 years’ defensive data is needed to judge a player, what does that mean? I’ll use Biggio as an example (since they’re talking about him at Baseballthinkfactory) - from ‘92-’02, he was worth about -5 runs/year fielding except for ‘97 where he was +19. Was he (1) a generally poor fielder who had a good/lucky year, or (2) still a poor fielder in ‘97 that looks good only because of the noise in the numbers?
Me:
You never throw data away, unless you have a REALLY REALLY good reason to do so. And even then, it better be REALLY REALLY REALLY good.
The more data you have, the less you need to regress. So, you need two years of fielding data to tell you as much as one year of hitting data. Would you make conclusions based on one year of hitting data? No? Then, you need more than two years of fielding data.
Performance through the ages
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.
Win expectancy charts used in football… in 1983!
http://sagarin.com/sports/wham_bam.pdf
Some familiar names, notably Sagarin and Wayne Winston. Football, more than baseball, is a great sport for using win expectancy charts. Whereas in baseball the pitcher has a large set of pitches and locations to choose from (and won’t even necessarily hit the target), and the batter has to choose to swing or not, and game theory and randomization will play a huge role here, in football you have a much more finite set of choices, and the play is over after the play (as opposed to baseball because of the count). The clock, penalties, the turnovers, etc, all add great variables that make the win expectancy really valuable for football.
Glove-slap: Kevin.
Thursday, February 09, 2012
Psst… wanna intern in Canada?
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
Moneyball at Villanova
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.
MGL: Today on Clubhouse Confidential
I’ll be home in time to catch him on the 7:30pm broadcast. Should be fun!
New PECOTA
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.