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Forecasting
Wednesday, October 24, 2007
Diamond Mind says 72% Redsox, with the Youkilis/Ortiz situation pretty much a tossup.
Betonline has a line of -235 Redsox, +195 Rox, which if I’m doing it right, translates to 68% Redsox.
Baseball Prospectus says 56% Rockies. BP does some “lefty/righty” adjustment on a team basis, which prima facie doesn’t seem right.
Redsox Runs Scored-Runs Allowed is: 867-657. Rox is 860-758. I know the DH affects things, but it affects things for runs scored and away. Of course, Ortiz is a premier DH, so it would affect the runs scored part alot more. On the other hand, if the AL is still the premier league, that probably balances it out somewhat. Anyway, that’s a 108 run difference in favor of the Redsox, which translates to a .560 record for the Sox, and .440 for the Rockies. If I’m doing this right, a binomial of .560, to win 4 before losing 4 means winning 63% of the time. (Interestingly, DMB says that a single-game .580 wins 70% of the time, even though the binomial says 67%, the difference being a result of the outcome not being based on .580 for each and every game.)
Basically, the odds are 2:1 against the Rockies. It’s interesting how out of line BP’s forecast is.
Tuesday, October 09, 2007
Nate checks in with how the ProTrade traders handicapped the season.
Thursday, October 04, 2007
Thankfully, there’s always somebody one step ahead of me, that saves me alot of time in doing stuff. This time, it’s Nate Silver evaluating various forecasts, including Marcel. He gives you a few different ways to evaluate things (which is good), but doesn’t give you the best one. Let me explain:
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Wednesday, September 26, 2007
Someone asked me this question. He also asked me specifically about my contention about the pitchers born 1954/1955 (those pitchers affected by the DH). Here then is how pitchers (as batters) did in adjacent seasons, of pitchers born since 1955:
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Tuesday, July 24, 2007
Building on the work I previously released, here is some more data:
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Tuesday, July 17, 2007
Comments?
Sunday, July 08, 2007
By , 09:59 PM
We don’t normally debate trades and signings on this blog, but…
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Tuesday, June 26, 2007
When you establish the true talent level of a player (or create a forecast, which is essentially the same thing), you want to know if he is healthy or not. And, if he’s not healthy, how unhealthy, and how persistent is his illness. So, you try to infer such things. If a player plays 159, 160, 154, 159, 161, 112 games, his OPS+ at any point in that stretch bottomed at 133, and the player is 27 years old at that point, we infer an injury. We don’t need to look more closely at the situation, though it could have been the case of someone even better usurping his playing time. You always have a certain uncertainty level. And if the player is 37 instead of 27, we may be more inclined to infer a longer rehab period. But, we still don’t know what kind of injury because the data doesn’t tell us much more.
John Walsh shows us the data for Curt Schilling. Now, we don’t need to infer if his performance was about balls falling in for a hit, or whether his true talent level was marketdly different. We remove that uncertainty level with the data. Depending on the nature of the illness, we’ll be able to either discount the data from this performance more, or place a greater premium on it. We’re always looking for the establishment of a new talent level, as opposed to randomness creating noise around the data. It’s data like this that we need.
And for MLB teams that are not doing this.... are you kidding me? What Walsh, Fox, Beamer, Sheehan, Appleman, et al are doing is the cutting edge of sabermetrics, the point where performance and scouting converge. This is the pot of gold that is being prospected.
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Further research would go into the “mix” of pitches, and the “strategy” of pitches, based on the game state (inning, score, base, out) conditions… i.e., Leverage Index.
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The data itself also has a certain amount of uncertainty, as can be easily seen with David Wells having a bunch of pitches being released from the wrong side of the mound (four feet from where it should be).
Thursday, June 21, 2007
Similar to what I did with strikeouts, I now look at walks.
UPDATE (Jun 21, 14:35): I misreported the direction of BB/K in the article. It has now been updated.
Tuesday, June 19, 2007
I updated my aging curves, this time for the time period 1957-2006. Here’s the step-by-step process:
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Tuesday, June 05, 2007
Guest-blogger Steve Walters over at the Wages of Wins blog links to an aging study by Ray Fair (PDF). I was asked to review the paper a few months ago, which I have reprinted in the comments section, and will repost here:
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Sunday, April 22, 2007
By , 07:24 PM
Here is a blog entry about peak offensive age. In it, the author references a study by Jim Albert which looks at the issue using some advanced and complex (to me at least) statistical techniques. Here is a post I wrote on BTF which also references the above-mentioned blog entry. My post sums up my thoughts on the matter. I was wondering what the many bright minds on this blog think of this issue. As I say below, I think the answer (what is peak offensive age) really illudes us. And it makes a difference when doing projections. Age ajdustments are important.
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Thursday, April 05, 2007
Nate Silver posted his first impressions on Matsuzaka after the first inning, with the following prediction:
IP H R BB K NP W-L
4.2 4 3 4 3 98 ND
Here’s the game, courtesy of Fangraphs, with Matsuzaka getting 10K, 1 walk, and +.367 in WPA. (Santana had only two starts better than that last year in terms of WPA.) Sean Forman chimes in with the best initial game of all Redsox pitchers since 1957, and Matsuzaka comes in fifth.
I’m using Nate as my foil, but it could have been any other intelligent, well-respected analyst out there. If Nate was right, what would have been the reaction from his readers? I dunno. But his prediction is the complete opposite of what really happened. Nate was wrong, and the opposite of would have happened if he was right should happen now.
The truth of the matter is that we all know sh-t. You, me, Nate, the manager, the opposing hitters. We can’t tell who’s on or who’s off. Maybe only the pitcher himself knows, and that’s a maybe.
This is the same thing with all the stock predictions. Do you know how many stock predictions are given out on TV and in major media every month? Thousands. Do you know how many track these guys, to see how well they do? I found one site, and every year the Wall Street Journal does an analysis. And you know what? Virtually all these experts can predict the future as well as mom&pop. And with all those expert football picks of the week? Long-term, they’re almost all around .500. The BP chats are filled with “what do you think this guy will do”.
That’s why I hate predictions. They mean nothing, unless you are held accountable.
Six or seven years ago, I dared ask the question on Baseball Boards. I laid out the process and came up with the answer: yes. The discussion that followed was illuminating and exasperating. The least appealing of the comments, rather than criticizing the process, criticized the conclusion! The problem, which no one pointed out at the time, was regression toward the mean, the single-most important concept to understand, if you are going to analyze sample data. I didn’t know about the concept back then. Once you handle that, the answer changed dramatically: no. Ruth would still be RUTH, but not so RUTHIAN.
The process was similar to what Dick Cramer did, as explained in The Hidden Game of Baseball, but I handled the age adjustment (he didn’t, it seemed). His results can be dismissed. I intended to finally write the followup for THT two years ago, but ended up shelving it. I’ve always intended to finish it up.
BP’s Between the Numbers looked at the issue, but, the execution was lacking. The drawing of the adjusted line really didn’t make much sense. It almost seemed like the author realized the problem, and couldn’t put his finger on it. That work too I would dismiss. Bill James’ timeline is also an effort to just put something in. Now we’ve got David Gassko handling the problem. If we look at his chart, we see that a player around 1950 would have his wOBA of .400 drop to .340 today. That’s a drop of 31 runs per 600 PA. This seems almost as preposterous as Cramer’s findings, even though he took care of the regression toward the mean issue. (My guess is that David didn’t handle the age adjustment.) But, there’s a part two coming up, so we’ll see what he did. IIRC, my work suggests about a 10-run change per 600 PA in that time period, and virtually flat in the last 30 years or so. I really ought to dust that off. The process is semi-reasonable, and the results pass the sniff test.
Tuesday, April 03, 2007
First off, Diamond-Mind has released their forecasts to the general public. Big kudos to them, as these forecasts now have very limited value (presuming most of the value is pre-draft).
I’ll be going through the forecasts from the following ten systems: THT, DMB, PECOTA, ZIPS, Chone, Marcel, Bill James, Pete Palmer, MGL, Shandler. My first step is to link all of their forecasts by some sort of player id (easily the worst part of the job). I also have to take all the community forecasts, parse them, standardize them, and come up with the results for each player. And then link their MLB player ID to all the other forecasters. Then the fun begins.
Stay tuned…
Thursday, March 29, 2007
I like the way this injury report is presented and said in plain english.
Wednesday, March 28, 2007
OOTP 2007 is a baseball computer game. A few weeks ago, I was offered access to a pre-release version. At the risk of sounding like a classic Phil Hartman (RIP) character, I’m just a simple guy, and the enormous number of parameters under my control scared and confused me. But, for serious gamers, the game’s got tons of fans. But, that’s not why I’m here. Apparently, something I did made it into the game:
http://www.ootpbaseball.net/clubhouse.php?view=news&gid=26&id=1304
Previous versions of the game, however, had every skill rising and falling in a standard umbrella pattern. OOTPv2007 is different. For the first time, it is modeling each skill as a separate component. Quite honestly, one of the biggest challenges that I looked at when I began testing player aging was to determine what baseline to use to assess quality. In the end I focused on work published by TangoTiger.
I’ve been meaning to update those charts that he references. Hopefully I can get to that at some point soon. Also, if anyone wants to make use of my Win Expectancy (WE) and Leverage Index (LI), as Fangraphs is using them, just ask. I grant a free licence, as long as you don’t charge your readers/customers (extra) for them.
A great post on USS Mariner comparing Todd Walker to Jose Vidro:
http://ussmariner.com/2007/03/27/todd-walker/
And there’s more. The Bill James sim scores has each player as the other’s top comp. They are both 2B, with around 1200 career games played, with Walker being 1 year and 3 months older. They are two of the three worst-fielding 2B according to the Fans. Marcel’s forecast: Vidro .346/.415 (490 PA), Walker .349/.438 (495 PA), which is fairly close (0.3 wins apart in Walker’s favor). The other three forecasters at Fangraphs have Vidro ahead by a bit higher margin. In short, both around league average hitters, and, as fielders, they are fine DH. This makes them, at best, +1 WAR.
USSM dealt alot with Vidro, so let me give you the skinny: Mariners are paying 12MM for the next two years (plus a vesting option in 2009, and Nats are paying 4MM for the next two years). Todd Walker won an arbitration award for 4MM, and Padres are walking away by paying him 1MM.
In short, the Padres don’t want to pay an extra 3MM for Walker, which is pretty much a borderline kind of play for a typical team, but makes more sense given the Padres depth at the Walker positions (1B, power-hitting, slick-fielding, and young Adrian Gonzalez; 2B, Giles, who is in the same class as Walker as a hitter, but a much better fielder).
And the Mariners, who intend to only use Vidro as DH… well, not only are they paying him all that money, but they had to give up players as well. I love Jose Vidro, as does all Expos fans. Our hope is that he produces far more than he can.
I’m linking these studies because:
1 - I had no recollection at all that MGL did a study on the issue
2 - Hopefully it’ll inspire others to take up the cause, and look at a whole series of component-aging
Here is MGL taking a look at banner years on walks:
http://www.tangotiger.net/archives/stud0233.shtml
And this is me:
http://www.tangotiger.net/archives/artBanner.shtml
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