New line of thinking...

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spotxc
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Re: New line of thinking...

Postby spotxc » Fri Oct 13, 2017 9:40 pm

I bet you're a conspiracy guy

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MEuRaH
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Re: New line of thinking...

Postby MEuRaH » Sat Oct 14, 2017 7:41 am

Top speed doesn't matter, but "fast enough" does. I don't want a RB who registers a top speed of 4.7 at the combine. 4.55? Probably fast enough given he can shake defenders.

At the combine, the ONLY thing I care about is his ability to change direction aka move his hips left/right, especially on WRs. Other than that, I find the combine mostly useless for armchair QBs like us.
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Goirish374
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Re: New line of thinking...

Postby Goirish374 » Sat Oct 14, 2017 11:47 am

Respectfully, you are incorrect.

“Bias,” when it is applied to any type of statistical or mathematical aggregation is relatively easily defined by which data we incorporate, how we incorporate them and why we choose to do so.

There are many types of defined bias that skew the interpretation and generalize-ability of statistical conclusions.

Selection bias is one of them.
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Dynasty DeLorean
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Re: New line of thinking...

Postby Dynasty DeLorean » Sat Oct 14, 2017 11:55 am

MEuRaH wrote: Sat Oct 14, 2017 7:41 am Top speed doesn't matter, but "fast enough" does. I don't want a RB who registers a top speed of 4.7 at the combine. 4.55? Probably fast enough given he can shake defenders.

At the combine, the ONLY thing I care about is his ability to change direction aka move his hips left/right, especially on WRs. Other than that, I find the combine mostly useless for armchair QBs like us.
its useless to you except it tells you how fast players are and you can also see and measure their agility. Seems like you are a bit conflicted.

Wait, are we talking about hips again? Sign me up for another round of that :P

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saw061600
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Re: New line of thinking...

Postby saw061600 » Sat Oct 14, 2017 2:01 pm

Goirish374 wrote: Sat Oct 14, 2017 11:47 am Respectfully, you are incorrect.

“Bias,” when it is applied to any type of statistical or mathematical aggregation is relatively easily defined by which data we incorporate, how we incorporate them and why we choose to do so.

There are many types of defined bias that skew the interpretation and generalize-ability of statistical conclusions.

Selection bias is one of them.
Respectfully, it would probably be best to actually read the two links. Neither is "biased" but rather are regression analysis to determine what measurable data is predictive of success. Regression analysis doesn't care about what any of us think, how we feel, or our bias regarding conferences or what a RB should look like on tape. A regression analysis can't be biased as it's only goal it to take sets of data and look for correlation. It's not trying to prove anything but rather is looking to see if anything predictive exists. There is a "qualifier" from Cole where he defines, for the purpose of his analysis, what exactly equates a "top RB", or successful RB, is included in the analysis to avoid the perception of "bias". Does his qualifier create an acceptable definition of what successful means for RB? Now that is a question that can be debated.
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Goirish374
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Re: New line of thinking...

Postby Goirish374 » Thu Oct 19, 2017 3:01 pm

saw061600 wrote: Sat Oct 14, 2017 2:01 pm Respectfully, it would probably be best to actually read the two links.
Yeah, it would be pretty irresponsible of me to comment without having read the linked material.
So. Yeah. I read them.
saw061600 wrote: Sat Oct 14, 2017 2:01 pm A regression analysis can't be biased as it's only goal it to take sets of data and look for correlation.
I’m not trying to be a dick or pick fights here...but you keep saying things that are demonstrably untrue.

“Combine matters, analytics show it.”
“That’s not the definition of bias”
“Regression analysis can’t be biased”

Are you confusing bias with objectivity or quantification?

In the realm of statistical analysis “bias” is a technical term. It has defined and specific meaning(s). Any time there is a mismatch between what you expect to get/predict from what you can measure and what the truth is based on what you can’t measure, there is bias. That is most studies.

In statistics its not a pejorative term. It’s descriptive. Knowing and understanding how our models are biased is critical to being able to use them effectively (for prediction, evaluation, etc.).

The idea that regression analyses are not susceptible to bias is incorrect. There are even types of bias that are specific to regression analyses. Off the top of my head “omitted variable bias” comes to mind.

Again, i’m not trying to pick a fight here and i’m happy to leave off if I’ve pulled things too far afield.

I think we can all agree that a potential corellation is exciting and worthy of further study.
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