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Can the Packers D stop the Pittsburgh Offense?
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<blockquote data-quote="TMC" data-source="post: 342086"><p>While I sometimes read Advanced NFL statistics, do not put too much stock in their analysis. They make a ton of mistakes.</p><p></p><p>In this study, let me point out a few things that should raise red flags. </p><p></p><p>On the opening page, they discount rushing attempts per game because teams that are winning tend to rush more. Now, I agree with this. Running does not translate to winning because teams with the lead will run. They decide to use yards per pass attempt and massage it into a passing efficiency number. The problem with that is pretty simple. The idea that a team that produces more yards per attempt will win is pretty self-explanatory. The more yards a team can average per pass should easily state that they make more big plays. Big plays, splash plays, usually translate into scores. The more a team can complete passes down the field, the greater their chance of winning.</p><p></p><p>Second, are you telling me that the use of yards per pass attempt or "passing efficiency" is being evenly evaluated against yards per rush attempt? What is your baseline to make these numbers equal? A run of 4 yards or more is considered a success, but you better throw for more than 4 yards. There has to be some type of way to correlate the numbers. I am certain it would decrease the importance of passing or increase the importance of rushing.</p><p></p><p>Third, on the final page, he mentions using a linear regression analysis to judge these. The problem I have with his data set is simple, he takes sacks out of the passing data, but does not do anything with interceptions in this "passing efficiency" number. Instead, he gives it a separate category. The two are not independent of each other. If you do not pass, you do not throw interceptions. Therefore, because the two are related and given separate values, it creates multicollinearity in the data. It is also peculiar that he standardizes the variables after doing the multiple regression. It makes me wonder if he handled his outliers, how many standard deviations he uses, and how he performed his standardization. It can become very unreliable when you start manipulating the data.</p><p></p><p>And, let's also not forget, every time you do some type of statistical evaluation, everyone adds ceterus paribus, all other things being equal. All other things are never equal, they always have bearing on the data at hand.</p></blockquote><p></p>
[QUOTE="TMC, post: 342086"] While I sometimes read Advanced NFL statistics, do not put too much stock in their analysis. They make a ton of mistakes. In this study, let me point out a few things that should raise red flags. On the opening page, they discount rushing attempts per game because teams that are winning tend to rush more. Now, I agree with this. Running does not translate to winning because teams with the lead will run. They decide to use yards per pass attempt and massage it into a passing efficiency number. The problem with that is pretty simple. The idea that a team that produces more yards per attempt will win is pretty self-explanatory. The more yards a team can average per pass should easily state that they make more big plays. Big plays, splash plays, usually translate into scores. The more a team can complete passes down the field, the greater their chance of winning. Second, are you telling me that the use of yards per pass attempt or "passing efficiency" is being evenly evaluated against yards per rush attempt? What is your baseline to make these numbers equal? A run of 4 yards or more is considered a success, but you better throw for more than 4 yards. There has to be some type of way to correlate the numbers. I am certain it would decrease the importance of passing or increase the importance of rushing. Third, on the final page, he mentions using a linear regression analysis to judge these. The problem I have with his data set is simple, he takes sacks out of the passing data, but does not do anything with interceptions in this "passing efficiency" number. Instead, he gives it a separate category. The two are not independent of each other. If you do not pass, you do not throw interceptions. Therefore, because the two are related and given separate values, it creates multicollinearity in the data. It is also peculiar that he standardizes the variables after doing the multiple regression. It makes me wonder if he handled his outliers, how many standard deviations he uses, and how he performed his standardization. It can become very unreliable when you start manipulating the data. And, let's also not forget, every time you do some type of statistical evaluation, everyone adds ceterus paribus, all other things being equal. All other things are never equal, they always have bearing on the data at hand. [/QUOTE]
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