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Concept Coop

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About Concept Coop

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  1. Absolutely. The industry is incredibly agile and, by NFL standards, affordable. Most teams are probably using Amazon. That said, I’m certainly curious how much weight they put in it; I honestly have no idea.
  2. I’m sure there are plenty of data scientists with fantasy football hobbies. In that sense, you could be right. But we’re doing incredible things with machine leaning/AI. The gap between cutting-edge analytics and guys like us using Excel and manually building models can’t be overstated.
  3. If there is any predictive value, there’s no doubt they’re incorporating it in their models. How much stock the decision makers put in analytics is a question, but all 32 orgs have an analytics team running models that we hobbyists can’t compete with. My biggest issue with most hobbyist metrics is that the creators don’t test against or control for draft position. It’s like creating a gambling model that doesn’t aim to beat the spread. Knowing that home teams win 55% of their games does a gambler zero good, as the betting lines already account for it. Your model has to beat the spread to have any value. If the league is aware of past trends and still highly values a player on the wrong side of those trends, we shouldn’t be ignoring that. And that’s even assuming the hobbyist model has predictive value. You can’t apply a model to past happenings and call it predictive. Predictive models have to be measured by actual predictions.
  4. The odds of all 4 of them missing. (Edit: And my was right the first time. Fixed it.) 0.92*0.92*0.92*0.92 = 0.72 So you think there is a 72%?chance that none of them ever finish top 24?
  5. If my math is right, you think there is a ~72% chance of 0 top 24 season between the four guys you list? You’d take that bet?
  6. I’m struggling to follow. If there were 2 false positives in the first round alone, how is Tyreek Hill the only one? It might be easier to read if you break it up by those who met the criteria and those who didn’t, then again by those with a top 24 finish l and those who without. If you could give me those 4 numbers by round, I’d be in your debt.
  7. I agree with some of this. I do think dom% and breakout age are general indicators of ability. My question is whether they keep being quality indicators once we control for draft spot. I know the best WRs tend to breakout earlier. But what is the track record for WR drafted early despite a later breakout age? How do 2nd round WRs with a late breakout compare to 3rd round WRs with an early one, etc., etc.?
  8. He was refuting your argument. You picked a variable and found a correlation. He picked a different variable and found an equally strong correlation. Why one would have predictive value and not the other is a valid question - and you haven’t answered that.
  9. He’s not. He’s performing the exact same univariate analysis that you are. He simply changed the variable. In doing so, he found an even stronger correlation than you did. It’s a valid question.
  10. Are you sure it predicts misses? It could - and I’m open to that - but I don’t think you’ve shown that it has any predictive value. You’d have to test it against other variables, at the very least.
  11. Have any breakout/dom% studies controlled for draft spot? Is the NFL behind here? Is there truly an inefficiency to exploit? A few years ago a poster did a study that found that NFL teams were under-drafting small school WRs. For a few years at least, there was value in drafting small school WRs ahead of big school WRs drafted in the same range. (But the NFL quickly corrected for that.) Was the NFL ever behind on these metrics? If so, have they caught up? I think what clop is saying - is that correlation a isn’t predictive model in and of itself.
  12. That doesn’t make any sense. Even if it’s discounted from what it was a couple months ago -and I’m not saying it is or should be - the newly reduced value is still “full value”.
  13. He didn’t even tell us what his asking price was.