Re: 2019 Running Back Report
Posted: Fri Jun 07, 2019 7:18 pm
My understanding of these reports is not so much you’ll hit on a pick but more that you’ll avoid a miss. Focus more on who not to draft vs who to draft?
https://forum.dynastyleaguefootball.com/
https://forum.dynastyleaguefootball.com/viewtopic.php?t=183692
I have no idea what this meansDynasty DeLorean wrote: ↑Fri Jun 07, 2019 6:45 pmThink of it like this, instead of asking how pregnant a woman is, rather ask, can a woman be just a little bit pregnant?ericanadian wrote: ↑Fri Jun 07, 2019 6:25 pm
It’s hard to tell if the model would’ve eliminated Trent Richardson, because he didn’t run any agility drills, which I think are a factor in the model. Maybe had he run them, there would’ve been a red flag we all would’ve seen.
In any case, the model’s big claim to fame was identifying David Johnson and I think it’s too early to say whether or not it’s been effective on most of the other running backs that have come out since he started doing these.
I agree, and that’s what makes not disclosing how the model somehow identified T-Rich’s short career even more concerning.
Thank you for the follow-up, explanations, and clarifications!Dynasty DeLorean wrote: ↑Fri Jun 07, 2019 12:54 pmmoishetreats wrote: ↑Fri Jun 07, 2019 10:32 am @Dynasty DeLorean: PLEASE publicly correct if I'm way off or off in a small way. And thank you, thank you, thank you again for sharing your thoughts AND for so fastidiously replying to all the questions, comments, and critiques.
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My understanding of this report is that it is looking for correlative indicators to success. By entering many points of concrete data (e.g., height, weight, draft slot, etc.) and then by determining what "success" means (e.g., 1,000 yards rushing), you can being to identify which data correlates to success.
I think that many people on this thread are confusing correlation with causation. Just because a player's data does or not fit the successful profile (i.e., correlate) does NOT mean that that player will or will not succeed (i.e., causation). DD's report isn't telling you that Player X will be successful and that Player Y will not be successful. Rather, DD's report is telling you that Player X's concrete data gives him a high, middle, or low likelihood of success based on the how other players with similar metrics ultimately performed.
Note that this kind of data analysis does not answer "why" questions. Why did Player X over-perform or under-perform? Why does Data Point A correlate to predicting success but not Data Point B? How do new schemes and play-calling affect metrics? These are not the questions that DD's report will answer. He's using a data-based approach to predict which RBs profile as more or less likely to succeed.
Indeed, one strength of this model is the ability for correlative indicators to change with more data. That's a good thing!! If there is the occasional outlier, then the correlative indicators won't be affected in anything more than a minimal way. But, if when there are numerous outliers and/or some players that entirely break the model, then the correlative indicators for success would change. Again, that's a good thing: the correlative indicators change because there is now more data to confirm or potentially reject the previous correlative assumptions. That makes the newly-updated correlative indicators MORE reliable!
For those who look at tape, schemes, coaching fit, etc. (i.e., subjective analysis), DD's report is likely not going to be your starting place or even necessarily something on which you would rely heavily. For those who look to survey methodology and data (i.e., concrete information), this is gold.
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@DD: Is this close, far off? Helpful, in your estimation, or just confusing people even more? My hope is the former!! And thank you again for your contributions!!
I started off trying to see if anything correlated with success. Most things didn't, but I did indeed find a few things that did. I think what I stumbled upon that maybe nobody else really thought of is that you can't make one big blanket statement. So for example (and i'm not saying I do this exactly, it's just an example), instead of saying "agility is important", you could ask is agility as important for a bigger back as it is a smaller back. Another example, if you have good speed do you need good agility. What about the inverse, if you have good agility do you need good speed. I don't think many people have asked these types of questions before and it's why I don't believe there's been anything like what I do out there.
As for if this would have worked in the past or will it work in the future. I have my doubts that 30-40 years ago this same exact model would have worked. These days everything is much more standardized, we have a lot more data, nutrition is better, workouts and strength and condition are optimized, from a teams perspective scouting and data analysis is better and there's more of an emphasis on efficiency and maybe to some extent the passing game. Scouts have a lot more access to smaller school players now than they did before. Years ago, there was a bigger emphasis on the running game and "Lesser" rb's were probably getting more work then than they would now. I know I looked back at a few rb's that had long careers and their YPC was in the toilet, and I wonder how long they would have lasted in today's game. If we fastforward into the future, let's say teams decide to go from a 50/50 or 40/60 run pass split to a 30/70 or 20/80 run pass split. The 1k threshold probably would be largely irrelevant. Will new rules be introduced to the game that affect the run game, who knows. So idk how long this will last. Does it work right now? Yeah i'm pretty convinced it does because it's so simple and so effective and it's been the same for a long stretch of years (15 or so, whatever I have data for). Is it possible it's just a giant fluke coincidence? Possible but unlikely imo.
Again I really want to say that i'm not wildly changing things on a yearly basis. The lists have not really changed over the years. I would say as I gather more data i'm able to simplify things rather than complicate or change it more. I think there is a distinct difference. I'm sure there will be outliers here and there, and disappointments. Ideally the studs list has 3+ 1k yard seasons, so guys like Ryan Mathews and DMC were kind of disappointments already. I'm sure there will be more. I don't know about the thing you're saying about numerous outliers and then I change everything, I think that would sort of be impossible.
If we look at the "studs" and "semi studs" (which is essentially the main part of the report) the model predicted since it's inception (2015), pre-2018 every player on that list with the exception of Foreman (who had the injury of course) has at least 1 1k yard season under their belt. I'd say that is a good indication that it's working as intended. Only time will tell if it continues on that course or not.
DWill doesn’t make it because breakout age is factored in, although in general I have been mulling over whether to eliminate that or not because as the years have gone by I’ve noticed it appears less and less useful. I guess I just never really gave much thought towards DWill since his career is over. He has 2 1k yard seasons which is normal for on the list or off so I guess I just never gave him the scrutiny he deserved. But I will look that over tonight. Trich just flat out doesn’t make it.Lord_Varys wrote: ↑Sat Jun 08, 2019 4:24 pm I gotta admit... As a stats guy I was really pulled into this... But now I need the curtains pulled back on Richardson and Williams. If I had a formula to predict studs or busts, I would be happy to share that formula, or at least it's factors.
His final year he had a 47% dominator rating. He did have more yards and td's but not triple the amount. 1964 yards, 18 tds. 12 receptions, 78 yards, 1 td.For tight ends and running backs, a 15% Dominator Rating is necessary to qualify for a breakout designation.
That’s kind of the problem- two really disparate prospects projected for the same very specific outcome, retrospectively, despite reaching that outcome for different reasons.Lord_Varys wrote: ↑Sun Jun 09, 2019 4:48 am Dunno anything about player profiler...
What's the reason for Richardson?
Wrong again. This is what I don’t understand, you don’t know but you insist you know. That seems... weird, doesn’t it? I don't answer your questions because all you've done in this thread is present your incorrect assumptions as fact, which in turn confuses everyone else.OhCruelestRanter wrote: ↑Sun Jun 09, 2019 5:16 amThat’s kind of the problem- two really disparate prospects projected for the same very specific outcome, retrospectively, despite reaching that outcome for different reasons.Lord_Varys wrote: ↑Sun Jun 09, 2019 4:48 am Dunno anything about player profiler...
What's the reason for Richardson?
I don't have a problem with that.OhCruelestRanter wrote: ↑Sun Jun 09, 2019 5:16 amThat’s kind of the problem- two really disparate prospects projected for the same very specific outcome, retrospectively, despite reaching that outcome for different reasons.Lord_Varys wrote: ↑Sun Jun 09, 2019 4:48 am Dunno anything about player profiler...
What's the reason for Richardson?
Sure! But then how do you arrive at exactly 5 prospects in that one tier since 2008, all of whom fit pretty well? Again, this is at best just bad statistical analysis, at worst some sketchy “trust me, I have a formula” stuff. I’d lean toward the former.Lord_Varys wrote: ↑Sun Jun 09, 2019 6:36 amI don't have a problem with that.OhCruelestRanter wrote: ↑Sun Jun 09, 2019 5:16 amThat’s kind of the problem- two really disparate prospects projected for the same very specific outcome, retrospectively, despite reaching that outcome for different reasons.Lord_Varys wrote: ↑Sun Jun 09, 2019 4:48 am Dunno anything about player profiler...
What's the reason for Richardson?
He could have breakout age as a factor that brings Williams down. Then he could have elusiveness (or whatever the mystery factor is) that brings Richardson down. Both find their way into tier 4, but for different reasons.
Okay, but there actually was a formula laid out in the original version of this. I’m pretty sure he’s since deleted it, but for many of us that saw the original version, this isn’t what you claim as we’ve seen the formula. He’s since made some changes, but the core mechanics must be similar because the results are also very similar.OhCruelestRanter wrote: ↑Sun Jun 09, 2019 7:11 amSure! But then how do you arrive at exactly 5 prospects in that one tier since 2008, all of whom fit pretty well? Again, this is at best just bad statistical analysis, at worst some sketchy “trust me, I have a formula” stuff. I’d lean toward the former.Lord_Varys wrote: ↑Sun Jun 09, 2019 6:36 amI don't have a problem with that.OhCruelestRanter wrote: ↑Sun Jun 09, 2019 5:16 am
That’s kind of the problem- two really disparate prospects projected for the same very specific outcome, retrospectively, despite reaching that outcome for different reasons.
He could have breakout age as a factor that brings Williams down. Then he could have elusiveness (or whatever the mystery factor is) that brings Richardson down. Both find their way into tier 4, but for different reasons.
I think I just heard someone pull their hair outDynasty DeLorean wrote: ↑Wed Jun 12, 2019 10:13 pm Matt Kelley actually came through and fixed his breakout age, which in turn puts DWill on the "studs" list. What an interesting turn of events.