syhuang

Do you mean the percentage of votes?? Right, this is how HOFers are ranked.

Did I say I only used OPS? Have you checked other avdanced stats, like OPS+, VORP, win shares, and SLG+? I assume you do know there're stats to compare players in different era. Let's make it simple, just look at the league average at their times and then look at their traditional offensive stats. This should help you to remove any doubts. I never said he shouldn't. However, I don't agree with your statement of "Name 2 better offensive shortstops that preceded Ripken. You can't." because Honus Wagner and Arky Vaughan are definitely better offensive shortstops than Ripken.

This is the point. You asked the question "Name 2 better offensive shortstops that preceded Ripken. You can't." and people are replying to your question. Honus Wagner and Arky Vaughan are definitely better offensive shortstops than Ripken. End of story.

Miss my hint? Look at league average at their times and try to use more advanced statistics to compare players in different era.

Arky Vaughan

I don't want to waste time to make this a baseball statistics class. So here is the hint: There're statistics used to compare players in different era, for example, VORP, OPS+, and win shares. In your case, you can try OPS+ or *OPS+ for offensive power. Again, end of story.

Honus Wagner and Arky Vaughan are definitely better offensive shortstops than Ripken. End of story.

Honus Wagner, Arky Vaughan, Dan Brouthers, and Robin Yount

No, it is still Jerry Sloan. 19 years and counting......

True, there're other moves which could also help Bills. However, criticising Whitner pick based on mock drafts is ridiculous.

However, many fans depend on mock drafts to form their opinions. I remember people cried about Whitner pick last April becuase he wasn't high in most mock drafts. They wanted Bunckley becuase many mock drafts had him at no.8. The funny thing is that these people don't use Bunckley to criticise Whitner pick now and use Ngata instead after he has a good season.

No, we got a 11-5 record in 99.

I don't think many considering him as a first day pick or a good left tackle prospect before the draft. Peters is a great athelete, but the main reason he went undrafted was that teams couldn't find a position for him. I remember Peters practiced at both tight end and takcle in his first year and didn't start to focus on tackle position until the offseason after his first season.

It's a straight forward question. Why avoid answering it? Dibs, this question is actually not that straight forward. First, in such a group, some of them are likely to get scores closer to population mean upon retaking the test. Some of them are likely to get scores higher than 140. Furthermore, the first subgroup is larger than the second subgroup. Therefore, the more accurate statement is "When retaking the test, more people in this group are likely to get scores closer to population mean than not" or "The average score of the group is likely to be closer to population mean when retaking the test". However, when talking about "people tend to score closer to the population's mean", I think it's kind of ambiguous. One side can say "Yes, they are, becuase there're more people likely to score closer to population mean or the average score is likely to be closer to population mean". The other side may say "No, because not all people are likely to score closer to population mean". Therefore, it's kind of about "semantics", does "average score" or "majority of the group" represent the whole group? Some may agree, some may disagree. I can understand what both sides are talking about, but I really don't want to join this name-calling war. So, let me try it once to see if it helps the discussion. First, the phenomenon HA described exists, but the more accurate statement is "average score of the group" instead of "people in the group". However, does the average score regress toward the "population mean"? This is another ambiguous part. I think HA's answer is yes, becuase "the average score of the group" is likely to be closer to population mean upon retaking the test. BJ's answer is no, becuase the target (not a good statistics term, but you know what I mean) of the "average score" regressing toward to is not "population mean", it's "mean of error". What's the difference? Let's say in a normally distribute population with mean of 100, one of them has a real IQ of 120 (from other more accurate tests) and he scores 140 the first time. Of course, the assumption here is normally distributed error with mean of zero. When retaking the test, (1) is he likely to score lower than 140? Yes. (2) is he likely to score closer to population mean(100)? Yes. (3) is he likely to score closer to his real IQ (120)? Yes. So what's the target of his score regress toward to? 120 or 100? I think most of you will say 120, which is his real IQ. Ok, now, go back to the example with a group of people who score 140 the first time. The assumption is the same (normally distributed error with mean of error and normally distributed population. The population mean is 100), and I simplified the example. Let's say there're three people who score 140 the first time and we know from other more accurate tests that their real IQ are Person A: 150 Person B: 125 Person C: 115 The average of their real IQ is (150+125+115)/3=130. (again, this may not be a good statistics way. Hope this can show the idea) Thus, when retaking the test, A is likely to get a score higher than 140 and be closer to his real IQ. B and C are likely to get a score lower than 140. The average score of the group is likely to be lower than 140. When retaking the test, (1) is the average score likely to be lower than 140? Yes. (2) is the average score likely to be closer to population mean(100)? Yes. (3) is the average score likely to be closer to their mean of real IQ (130)? Yes. Like the example of one person earlier, what is the target of the average score regressing toward? The population mean(100) or mean of real IQ (130)? I think most will say mean of their real IQ. Thus, the argument here is again about "semantics". You can say the statement of "regression toward mean of population" is right because the average score is likely to be closer to population mean". You can also say the statement is not right becuase the target of the regression is not population mean, which happens to be in the same direction of mean of erorr. In short, the phenomenon exists, but can the statement be called "regression toward the population mean"? it depends on what you refer to, a direction or a target.

I think many Bills fans agree there're questionable playcalling. However, what people don't agree with you is not because you question playcalling. It is because your comments like following: -------------------------------- I didn't say they would fire Fairchild, but I bet the process of decision making on game turning calls is changed. There may be a committe of Dick Jauron, the OC and the DC and Bobby April. -------------------------------- Yes, bragging rights about a change made in who makes the final decsion on game hinging calls. I wouldn't be surprized if Marv ends up jumping in on those calls. --------------------------------

Jags, OG Manuwai agree on long-term extension

Determining Draft Order • Strength of schedule for the previous season is the first tie-breaker for teams with the same winning percentage. • Divisional and conference records are the next step in the tie-breaking procedure. • As a last resort, a coin toss is used to determine the order of selection for teams with the same winning percentage.

No, I'm not talking about mean of population or population distribution. I'm only talking about error distribution and normally distributed error causing "regression toward the mean (of error)" phenomenon. You need to explain things step by step. It seems like HA agrees that not all the errors cause regression toward the meam. Although he and me may not refer to the same definition of "mean", it's not the point of my example, which is if errors always cause regression toward the mean. In my example, both "mean of population" and "mean of error" are the same, so we can focus on the effect of error distribution. It looks like the discussion now can move to the next topic (for example, mean of error vs. mean of population). Anyway, I'm done with the discussion and already let HA know what I try to show him. Now, let's go back to the usual name-calling shouting match.

Didn't I state that the "mean" in "regression toward the mean" is "the mean of error" when I brought up two questions?

It seems like we also agree on this one. Under normal circumstances, a normally distributed error would cause "regression toward the mean". When talking about math or statistics, in theory, errors don't always cause "regression toward the mean". Thus, you can NOT simply say "errors cause regression toward the mean", since this statement is not always true. You have to be more specific and state the conditions when your statement is true. Be more scientific, for example, "In normal circumstances, errors cause regression toward the mean" or "When the error is normally distributed, error causes regression toward the mean".

As you may know, I agree that normally distributed errors cause regression toward the mean. However, here I'm trying to show you that not all the errors cause regression toward the mean by using this extremely abnormally distributed error. Please note I stated the error here is normally distributed. Let me repeat the question: If a person's real IQ is 140 (known by other more accurate tests) and scores a 160 or 120 in a test with zero-mean normally-distributed error, will he likely get a score closer to the mean when retaking the test? It seems like you agree "regression toward the mean" normally applies to individuals. Right, the person's true IQ is 140. And because of an abnormally distributed error, when this person takes this test, the only outcomes are 135 (50%) and 145 (50%). Also, as I mentioned in another post, it's common that the expected value is not one of the possible outcomes. I'm aware this case is unrealistic. However, this abnormally distributed error example shows that not all the errors cause "regression toward the mean" on individuals.

Good. We now agree this abnormally distributed error doesn't cause any individual regressed toward the mean. In other words, even with error existed, "regression toward the mean" on individuals may not happen. I think I just show you that error doesn't always cause "regression toward the mean" on individuals.

Ha, it seems like I'm good at this. Anyone also wants me to take words out of your mouth? I'm two for two today.

You said "someone", thus, it looks like you're talking about "one person". If you're talking about a group of people, please answer the questions in my previous post about if "regression toward the mean" applies to individuals. With one person taking this test, he only gets one outcome, either 135 or 145. There's nothing to average after the score is out. Also, it's called "expected value", not "average outcome". You can say, before taking the test, the expected value of the score is 140. However, once the test is taken, there is only one score, 135 or 145. Even the expected value is 140, you can not score 140 in this test. It's common that expected value is NOT one of the possible outcomes. Need more example? take the famous dice example in this thread, the expected value of one throw is 3.5, but you can never throw a 3.5. Please don't confuse expected value with possible outcomes. When talking about "regression toward the mean", it's about the possibility of the score upon retaking the test being closer to the mean. It's not about if the expected value is closer to the mean or not.

Do you imply that "regression toward the mean" only applies to a group of people and doesn't apply to individuals? Anyway, answer this question, does "regression toward the mean" apply to individuals? Or if you want an example, here is one. If a person's real IQ is 140 (known by other more accurate tests) and scores a 160 or 120 in a test with zero-mean normally-distributed error, will he likely get a score closer to the mean when retaking the test? If your answer is yes, please answer the next question. Q: If a person (not a group of people) gets a 135 in this test with abnormally distributed error, does he likely get a score (either 135 or 145) closer to the mean when retaking the test?