There is a disjoint between the two types of baseball stat geeks. I refer to what I practice as Sabr-magics for a damn good reason: I’m fairly well versed in most statistical analysis, but when it comes to realistically predicting the immediate future (that’s what fantasy baseball is,) you can often throw math out the window.

I hate to bring this back to cliches, but 1) steroids 2) injuries 3) off-season work, are all things you cannot predict. If you had information, you could factor them in but unfortunately you don’t have any *trustworthy* information.

e.g. Maybe Ankiel Worked Hard, Ludwick took HGH, and Pujols had a minor injury. There’s nothing you can do to predict the outcome of the season, all you have is incomplete information! Regardless of how poorly Pujols performs, you’re viewing each AB as an independent event.

I’m not sure what Dave claims to be, but there’s a huge difference between those that want accuracy, and those that want to be ahead of the curve in their fantasy baseball league.

I still maintain these early stats are the most important in pointing you in the right direction.

Someone brought up Andruw Jones, he was predicted by most models to hit 35-40 HR last year ( i believe)

Did you keep him on your squad for 200 PA? I doubt it.

I’d like to further emphasize this sample size issue, but leave baseball for a second. It is often the exact right thing to do to just flat out ignore some data when doing statistical analysis if there are sufficient problems with that data. This problem may not appear in baseball very often, given that those problems usually come from design or sampling issues. But that is the basic reason we don’t trust pre-WWII statistics very much.

As for something more baseball related, one of the major problems is going to be a skewed sample or nesting. So, say you are researching Lake Trout, but only sample 3 lakes. Even if you achieve statistical significance in your study in those 3 lakes, you can only makes conclusions about those 3 lakes (and may even only have achieved significance thanks to pseudoreplication, but I digress). Obviously, 3 lakes isn’t going to be representative of all Lake Trout. A similar problem is faced in baseball and is hinted on with Andruw Jones. He’s faced a higher portion of lefties than we would expect if he played everyday. Victor Martinez, and all other players have a slightly different issue. They have only faced a small handful of pitchers or teams. Currently he’s had 6 PAs against Sidney Ponson, 4 against Kevin Slowey and doesn’t have any more than 3 against any other pitcher, with 46 total pitchers faced (last year he faced 114 pitchers, against 18 he had 4 or more PAs). He’s had 27 PAs against the Royals in general, with a 1.222 in OPS against them. And has only faced 6 teams, getting just 9 PAs against the Red Sox. So 90% of Victor’s sample is nested in 5 teams. 2 of them have the highest team ERA in the game outside of the team Martinez plays for. Minnasota is 24th, Toronto 11th, KC 3rd (who Martinez has hit the best). Does that sound like a representative sample of the whole league?

Now, I’m not saying ignore these April numbers all together (nice straw man), but you have to recognize they have problems. These are small samples, even 100 PAs is pretty small. And because of the scheduling and nature of baseball, those small samples are nested within just a few teams or pitchers.

As for being an ass, how’s this: You and Dave Cameron talk about statistics like you read about it on wikipedia, and didn’t get it. Someone who wants to have a serious conversation about statistics and the misuse of statistical principles would not casually throw around the word significant, nor think its up for interpretation. They then would certainly not resort to such asinine comments, as Dave has, after being faced with criticism.

Being able to react to and answer criticism rationally and logically, even if you don’t know the answer or made a mistake, is absolutely necessary to maintain credibility. Obviously, I’m not terribly concerned with this, I’m an anonymous commenter. But Dave has a great deal of interest in maintaining his credibility….

“Second, you’re asking Dave to take out the noise for every player in a response to your post. It can’t be done.”

I won’t speak for Nacho, I don’t know him at all and don’t know what he believes, but I disagree with your assessment. I’m certainly not asking for the ‘noise’ to be taken out. I’m asking that the standard deviation for these data sets to be compared. I don’t have them and I don’t claim to be a baseball blogger (not even close!), so I’m not going to do this. My guess is that the standard deviations of the two projections cross, which means simply that there is no significant change. This has nothing to do with getting rid of noise, but has everything to do with doing serious, real analysis. This is not ignoring data, but instead considering all data available and conducting an actual comparison.

No one is being an ass but Dave and his blind supporters. He refuses to answer legitimate concerns with his writing, instead he prefers to insult people and look down.

In sum: Significance has a well-defined definition. Use it, or else you aren’t doing statistical analysis.

The observations made about Andrew Jones (seems to be tying in) if true, are provable from an observational standpoint, and not likely a statistical one. The Rangers appear to have protected him some to this point, and if he now plays every day for a month, we will see if his numbers have seen a remarkable jump.

Second, you’re asking Dave to take out the noise for every player in a response to your post. It can’t be done. But Dave gave Victor Martinez of an example where small sample performance can have an overall effect on projections. Could it be noise or error? Sure, but based upon his success so far, a projection system certainly has to take those numbers into account. Andruw’s success has come in significantly less AB’s and mostly against leftys. Should it have as much effect? No, it’s a smaller sample and in what should be favorable matchups for him. Modify your projections upward, but not nearly as significantly. It’s a relative calculation. Regardless, to ignore any information you can lay your hands on is ignorant.

Meanwhile, stop being such an ass.

When I said there wasn’t a huge difference in performance, I meant on field performance, not statistical performance.

But the main point I was trying to make, and never really got around to stating, is that what would be really interesting is when projections are made in the beginning of the season that information on what one and two Standard Deviations are for each projection. Each player should have a different deviation based on sample size of performance, and knowing the size of the bell curve would make these discussions on sample size much more scientifically sound. With the updated ZIPS it would be extremely informative to know whether or not the updated projection falls outside of one or two standard deviations, at which point you can make claims of statistical significance, if the update doesn’t then there is no significance in the data. Error bars are part of science, so if you want to make claims of scientific analysis, do science.

Further it would be absolutely amazing to know the size of the standard deviation for 100 AB, 200 AB, 300 AB, 400 AB, 500 AB, and 600 AB. This way we could look at a 100 AB sample and know whether or not it is a statistically significant (from the actual mathematical definition) deviation from expectations.

I think this is the same point being made by others and their issue with your use of the term significant. No one is arguing that Martinez is a bad player or that the first 100 AB of a season are meaningless, but without doing real statistical analysis your post isn’t telling anyone anything too groundbreaking.