Rubes

Yeah, apparently the Dolphins schedule tweet has been deleted...

🙄

Brady and Fox. Sounds about right.

That's my guess if it isn't the Rams.

Honestly hope this is the case. Opening on TNF would be great since I have to be away that weekend, plus we’d get that nice mini-bye right before that big game at home.

Nice summary, OP. A good review of what to watch during training camp. Interesting to think about what might happen with Moss. I think Beane has faith in him and is looking for him to rebound this year. But if he looks sharp in training camp and preseason, could we see Beane move him for a future pick to a team that gets snagged by the injury bug?

Such an underrated movie.

What the hell does any of this have to do with Edmunds and whether or not we should trade him or draft his replacement?

Great opening night game if true. Should be exciting. I’ll be a wreck.

Yeah, it’s been discussed throughout the thread…not precisely sure how much is needed, though…

Have to admit, this is what I’m hoping for, even if that gives us too many DEs.

Yeah but unless you’re moving him before this season, it makes no sense to use a first round pick on a guy who would not likely see the field much. Gotta use that pick on someone who will make a difference this season.

What would you do with it?

We traded up for that guy, didn't we. The year after we drafted EJ. 🤮

Maybe we'll finally get to see Sammy hit his true potential with a real QB.

"Go ***** yourself, San Diego."

I agree. I think Beane (and most other GMs) are waiting to see what happens in the draft and who ends up falling to them. After that they'll have an idea just how much they want or need to pursue a vet CB as well. Big difference if a good CB falls to them at #25 vs. none seem to make sense until the third round.

Yeah, thank goodness Beane didn't listen to the guys tweeting the replies to this back in 2018...

So by this logic, Burrow now has 3 playoff wins and has gotten to the Super Bowl, which Josh hasn't. Doesn't that mean Burrow should be listed above Allen?

Damn I wish I still had my card collection from the 70s.

Well *****, thanks for sharing it and making the rest of us look at it too!

Poorly worded on my part. Non-controlled studies may produce results of undetermined meaning—you may show a statistical difference before and after the intervention, but without a control group you can't say if that result would have happened anyway, in the absence of the intervention. Controlled studies can produce results with more confidence and meaning in their statistical significance—but the statistically significant value may not have much meaning in the real world, like if the study of max running speed had a large enough sample size to detect a tiny difference of 0.01MPH. You could conclude that ACLs had a significant impact on speed, but most people would look at 0.01MPH and say, "Who cares?" The former is less trustworthy, because you can't draw a valid conclusion with much confidence. The latter is more trustworthy, because it was designed well, even though the more valid conclusion may not ultimately mean much.

Sham surgeries are most definitely performed, on animals for animal studies, which of course are relevant for our understanding of similar scientific questions in humans. The sham surgeries are done because of the reasons I stated. Of course, not all queries lend themselves to a randomized trial. You can't do a prospective randomized trial of ACL injuries, for instance. Studies like that are best done as controlled observational studies the way I described. You can certainly do an uncontrolled before-after study, and lots of people publish those, but by no means are those studies considered to be high quality evidence. The main criticism of an uncontrolled before-after study is that the results are untrustworthy—you have no idea if the observed effects are truly significant or not. In many cases it's very difficult to identify a control group for a before-after study, and that's okay, you can't always have what you want. But by accepting that and publishing an uncontrolled before-after study, you're basically admitting that your results, while interesting, may or may not have real-world significance. You choose a study design based on the question you're trying to answer. If the question is: what is the impact of an ACL injury on an NFL player's career? then you know you'll be doing an observational study, but the real question you're trying to answer is: how does what happened to those injured players compare to what would have happened if they had never been injured? Since you can't do that directly, you do the next best thing—compare what happened to those injured players to what happened to a similar group of non-injured players. Imagine if the main outcome you were trying to test is a player's maximum running speed. So the main question is: what is the impact of an ACL injury and repair on a player's maximum running speed? Let's say you have all of the data on player's maximum speeds from the NFL combine, and now you identify players who had ACL injuries during their NFL career, so you test them again for their max running speed. You could just do a simple before-after study and compare their running speeds now vs. their running speeds then, and you'd probably see a decent difference. You could, for instance, say that those with an ACL injury saw an average loss of 1MPH in their max running speed. Is that a valid conclusion? Not really. Of course, the reason is that everyone slows down as they age, so what you'd really want to do is compare the average speed loss in those who are injured with the average speed loss in those who were never injured. Then you'd know the impact of the ACL injury on max running speed. You need the control group to know how significant the loss of speed observed is. And yes, the null hypothesis would be that the ACL injury had no effect on max running speed. And that may very well be the case. Or, there may be an effect, but it's not statistically significant. Or, the loss may be statistically significant, but it may not be significant in the real world. For instance, you could show a statistically significant loss of 0.1MPH to their speed, but how that impacts play in real games may not be a big deal. But you still need the control group to understand whether the observed differences pre- and post-injury are statistically above and beyond what you would have seen, on average, in the absence of the injury.

Perhaps, but it more likely has to do with baseline performance level. Tre at baseline is a Pro Bowl-caliber player, one of the top at his position. If his performance declines a certain amount, say 10-15%, as a result of his injury and surgery, he'd still be better than most CBs out there and would have a job. By some measures of performance, however, he would be worse off than pre-injury, so it's not entirely about being in the league or not.