2019 Running Back Report
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Re: 2019 Running Back Report
Watch his tape and tell me he's only as fast and Jordan Howard.
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Re: 2019 Running Back Report
Yeah, I sent him an E mail about that, but he didn't seem to how enough info to adjust it. Jacobs 2nd pro day time, even with the adjustment, if Rappaport's number are accurate would be sub 4.60.ericanadian wrote: ↑Wed Jun 05, 2019 8:49 pmHe uses playerprofiler.com and my understanding is that they apply an adjustment to Pro Day times +0.05 if I recall correctly. I also believe they used Alabama’s first Pro Day where Jacobs ran a 4.63/4.66. So, if you want to use the second Pro Day, you’d run it as a 4.59 to be comparable.hoos89 wrote: ↑Wed Jun 05, 2019 6:46 pmGiven that Jacobs didn't run the 40 at the combine...where is his speed score coming from? Going by combine weight of 220, this is implying a 40 time of 4.69. His pro day times were reported as 4.52-4.56, which equates to a speed score over 100.Dynasty DeLorean wrote: ↑Mon Jun 03, 2019 2:06 pm If you were wondering if Jon Gruden uses "analytics" or not.
These are only Rb's selected in the first round. Josh Jacobs didn't complete his agility testing so I couldn't compare several other metrics for him, but could compare these.
Full Name ------- College Dominator Rating
Josh Jacobs 13.90%
Full Name ------- Speed Score
Josh Jacobs 90.9
It will be interesting to see how Jacobs pans out.
Janiel Dones Truther
Foodie. Well done steak goes in the trash.
Habaneros make the best tasting hot sauce. Throwing a bunch of random stuff on top of fries doesn't mean you call it "poutine".
Foodie. Well done steak goes in the trash.
Habaneros make the best tasting hot sauce. Throwing a bunch of random stuff on top of fries doesn't mean you call it "poutine".
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Re: 2019 Running Back Report
My instinct here was to thank you for putting this out here first, because you clearly put a lot of work into doing this, but then I noticed that you're actually asking for money to do this.
My problem with this kind of analysis is that it's a classic case of overfitting. If you're not familiar with the term, overfitting is when your analysis corresponds too closely or even exactly to a limited set of data points, to the point that it may fail to predict future outcomes reliably. This really, unfortunately, undermines the entire point of doing this kind of analysis. Here's a graphical example of what you've done here: Instead of describing the trend line (the black line) you're describing the blue line. The problem comes when somebody who looks exactly like DeAngelo Williams gets lumped into Tier 4, only they don't end up on a team that perpetually drafts RBs early; or when somebody who looks exactly like Le'Veon Bell lands in Tier 1b, only he doesn't shed a bunch of weight and then end up playing behind the Steelers' O-Line.
The point of analytics is to figure out which traits correlate with success, which traits correlate with failure, and which traits can be safely ignored. The next part is incredibly important: SOME PLAYERS WITH BAD TRAITS WILL STILL SUCCEED, AND SOME PLAYERS WITH GREAT PROFILES WILL FAIL. When you change your model to treat guys like Trent Richardson (who was incredibly productive and posted good athletic marks) and DeAngelo Williams (ditto) as Tier 4 guys, you're doing yourself and everybody who reads this a wild disservice. Again, I think you need to hear this: IT'S OK TO MISS ON TRENT RICHARDSON, BECAUSE EVEN GREAT MODELS SHOULD BE WRONG SOMETIMES.
What are you going to do going forward if Mixon posts another great season, or if Dalvin Cook or Marlon Mack breaks out? How does the model change then? If Christian McCaffery suffers a Zach Miller injury and is done for his career, are you going to drop him back down?
The other problem here is the lack of transparency. Without even identifying the inputs to the model, it's hard to adequately evaluate the science behind it. Telling people you have a model that predicts success, then not disclosing your process, and asking for people to give you money for doing it seems at least a little bit shady. I would strongly, strongly caution anybody from using this information for your fantasy teams.
My problem with this kind of analysis is that it's a classic case of overfitting. If you're not familiar with the term, overfitting is when your analysis corresponds too closely or even exactly to a limited set of data points, to the point that it may fail to predict future outcomes reliably. This really, unfortunately, undermines the entire point of doing this kind of analysis. Here's a graphical example of what you've done here: Instead of describing the trend line (the black line) you're describing the blue line. The problem comes when somebody who looks exactly like DeAngelo Williams gets lumped into Tier 4, only they don't end up on a team that perpetually drafts RBs early; or when somebody who looks exactly like Le'Veon Bell lands in Tier 1b, only he doesn't shed a bunch of weight and then end up playing behind the Steelers' O-Line.
The point of analytics is to figure out which traits correlate with success, which traits correlate with failure, and which traits can be safely ignored. The next part is incredibly important: SOME PLAYERS WITH BAD TRAITS WILL STILL SUCCEED, AND SOME PLAYERS WITH GREAT PROFILES WILL FAIL. When you change your model to treat guys like Trent Richardson (who was incredibly productive and posted good athletic marks) and DeAngelo Williams (ditto) as Tier 4 guys, you're doing yourself and everybody who reads this a wild disservice. Again, I think you need to hear this: IT'S OK TO MISS ON TRENT RICHARDSON, BECAUSE EVEN GREAT MODELS SHOULD BE WRONG SOMETIMES.
What are you going to do going forward if Mixon posts another great season, or if Dalvin Cook or Marlon Mack breaks out? How does the model change then? If Christian McCaffery suffers a Zach Miller injury and is done for his career, are you going to drop him back down?
The other problem here is the lack of transparency. Without even identifying the inputs to the model, it's hard to adequately evaluate the science behind it. Telling people you have a model that predicts success, then not disclosing your process, and asking for people to give you money for doing it seems at least a little bit shady. I would strongly, strongly caution anybody from using this information for your fantasy teams.
COOGAN IS A CHEATER AND A THIEF
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Re: 2019 Running Back Report
I'll risk looking dumb here if I'm remembering correctly, but here goes:
DD initially provided the underlying whatever in his model, but the discussion was a big mess so he pulled it. He has since been questioned every single year (by me included) for not letting us peek behind the curtains.
DD initially provided the underlying whatever in his model, but the discussion was a big mess so he pulled it. He has since been questioned every single year (by me included) for not letting us peek behind the curtains.
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Re: 2019 Running Back Report
idk where you guys are getting this stuff from. i didn't change anything to put trich or dwill anywhere. it's been like this since about 5 years ago.OhCruelestRanter wrote: ↑Fri Jun 07, 2019 6:47 am When you change your model to treat guys like Trent Richardson (who was incredibly productive and posted good athletic marks) and DeAngelo Williams (ditto) as Tier 4 guys, you're doing yourself and everybody who reads this a wild disservice.
According to the report there's a high chance Mixon has another "great" season. So again, the people criticizing what i'm doing here don't even read the report.
I put a link for donations because the data-analysis package is $40. I'm not making any money off of this, in fact i'm losing money.
Re: 2019 Running Back Report
Don’t know your model, don’t trust your model but if it works for you that’s a good thing.
I don’t really evaluate RB’s in groups or predictive outcomes.
It’s all about scouting, system, line play, Ppr value for me and each are uniquely different.
Looks like players like Jacobs are pushed down in your model due to system in college to me.
I don’t really evaluate RB’s in groups or predictive outcomes.
It’s all about scouting, system, line play, Ppr value for me and each are uniquely different.
Looks like players like Jacobs are pushed down in your model due to system in college to me.
The Clock is Running and there are no Timeouts
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Re: 2019 Running Back Report
I read the report. I suppose I could be more clear- I'm not suggesting you changed the report after the fact to move Richardson or Williams down. I'm suggesting that trying to work the model so that they're considered failures is bad process. And, okay change Mixon to two great seasons- then what do you do? And what about Marlon Mack or Dalvin Cook?Dynasty DeLorean wrote: ↑Fri Jun 07, 2019 6:53 amidk where you guys are getting this stuff from. i didn't change anything to put trich or dwill anywhere. it's been like this since about 5 years ago.OhCruelestRanter wrote: ↑Fri Jun 07, 2019 6:47 am When you change your model to treat guys like Trent Richardson (who was incredibly productive and posted good athletic marks) and DeAngelo Williams (ditto) as Tier 4 guys, you're doing yourself and everybody who reads this a wild disservice.
Mixon is actually "predicted" to likely have another "great" season. So again, the people criticizing what i'm doing here don't even read the report.
I put a link for donations because the data-analysis package is $40. I'm not making any money off of this, in fact i'm losing money.
You haven't addressed the broad point that you're overfitting at all. It's just bad data analysis.
COOGAN IS A CHEATER AND A THIEF
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Re: 2019 Running Back Report
I actually do have to go back and double check Jacob's speed score. But you kind of run into problems if you're kind of picking and choosing which time to use. I typically go by the first legitimate one / combine ideally. Also, at what weight did he run during his 2nd pro day. If we don't have the weight then we can't use the time.FantasyFreak wrote: ↑Wed Jun 05, 2019 9:40 pmYeah, I sent him an E mail about that, but he didn't seem to how enough info to adjust it. Jacobs 2nd pro day time, even with the adjustment, if Rappaport's number are accurate would be sub 4.60.ericanadian wrote: ↑Wed Jun 05, 2019 8:49 pmHe uses playerprofiler.com and my understanding is that they apply an adjustment to Pro Day times +0.05 if I recall correctly. I also believe they used Alabama’s first Pro Day where Jacobs ran a 4.63/4.66. So, if you want to use the second Pro Day, you’d run it as a 4.59 to be comparable.
edit: I don't even know why i'm bothering to respond to the troll. I deleted my last post. If he confused anyone with his nonsense feel free to ask any questions you may have.
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Re: 2019 Running Back Report
Jacobs "failed" on multiple levels. But you're right, splitting time in college certainly didn't help.Ice wrote: ↑Fri Jun 07, 2019 7:00 am Don’t know your model, don’t trust your model but if it works for you that’s a good thing.
I don’t really evaluate RB’s in groups or predictive outcomes.
It’s all about scouting, system, line play, Ppr value for me and each are uniquely different.
Looks like players like Jacobs are pushed down in your model due to system in college to me.
Whether you choose to believe it or not, most stud rb's have the same few things in common. If that wasn't the case then I wouldn't be making this report. But the other things you listed such as system, line play, etc. i'm sure are great tools to evaluate the majority of rb's in the league.
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Re: 2019 Running Back Report
Are you calling me a troll? This is a troll:Dynasty DeLorean wrote: ↑Fri Jun 07, 2019 7:04 amI actually do have to go back and double check Jacob's speed score. But you kind of run into problems if you're kind of picking and choosing which time to use. I typically go by the first legitimate one / combine ideally. Also, at what weight did he run during his 2nd pro day. If we don't have the weight then we can't use the time.FantasyFreak wrote: ↑Wed Jun 05, 2019 9:40 pmYeah, I sent him an E mail about that, but he didn't seem to how enough info to adjust it. Jacobs 2nd pro day time, even with the adjustment, if Rappaport's number are accurate would be sub 4.60.ericanadian wrote: ↑Wed Jun 05, 2019 8:49 pm
He uses playerprofiler.com and my understanding is that they apply an adjustment to Pro Day times +0.05 if I recall correctly. I also believe they used Alabama’s first Pro Day where Jacobs ran a 4.63/4.66. So, if you want to use the second Pro Day, you’d run it as a 4.59 to be comparable.
edit: I don't even know why i'm bothering to respond to the troll. I deleted my last post. If he confused anyone with his nonsense feel free to ask any questions you may have.
I had valid criticism of your analysis, specifically overfitting, that you seem completely unable to address.
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Re: 2019 Running Back Report
@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.
------------
@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!!
----------
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.
------------
@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!!
10 tms 27 plrs PPR
Start: 2QB 2RB 3WR 2TE 2Flex / best ball
QB: Herbert, Love, Rodgers, G Smith, Stidham, T Taylor, Hall
RB: McCaffrey, Mixon, Pacheco, Montgomery, Z White, Allgeier, Dillon
WR: Hill, St. Brown, Kupp, Allen, Lockett, B Johnson
TE: Kelce, Kmet, Kraft, Okonkwo, Dulcich, Tremble
2024: 2.09, 3.07, 3.08, 3.10, 4.08
2025: 2nd (x2), 4th, 5th (x2)
2026: 1st, 2nd (x2), 3rd, 4th, 5th
12 tms 22 active plyrs. Salary Cap $300 PPR
Start: 1QB 2RB 3WR 1TE 1SF 1Flex / best ball
QB: Love ('24), Rodgers ('24), Stidham ('25), Lock ('25)
RB: Brooks ('27), Wright ('27), Guerendo ('26), Mitchell ('27), Dillon ('24), Rodriguez ('24), Spiller ('24)
WR: G Wilson ('26), AJ Brown ('26), Nabers ('28), Worthy ('28), Polk ('28), Franklin ('28), E Moore ('25), M Thomas ('25), DJ Montgomery ('25)
TE: --
2024 Cap Spent: $201
IR: --
TAXI SQUAD (4 max): Sinnott ('28), All ('28), Schrader ('26), A Reed ('28)
Start: 2QB 2RB 3WR 2TE 2Flex / best ball
QB: Herbert, Love, Rodgers, G Smith, Stidham, T Taylor, Hall
RB: McCaffrey, Mixon, Pacheco, Montgomery, Z White, Allgeier, Dillon
WR: Hill, St. Brown, Kupp, Allen, Lockett, B Johnson
TE: Kelce, Kmet, Kraft, Okonkwo, Dulcich, Tremble
2024: 2.09, 3.07, 3.08, 3.10, 4.08
2025: 2nd (x2), 4th, 5th (x2)
2026: 1st, 2nd (x2), 3rd, 4th, 5th
12 tms 22 active plyrs. Salary Cap $300 PPR
Start: 1QB 2RB 3WR 1TE 1SF 1Flex / best ball
QB: Love ('24), Rodgers ('24), Stidham ('25), Lock ('25)
RB: Brooks ('27), Wright ('27), Guerendo ('26), Mitchell ('27), Dillon ('24), Rodriguez ('24), Spiller ('24)
WR: G Wilson ('26), AJ Brown ('26), Nabers ('28), Worthy ('28), Polk ('28), Franklin ('28), E Moore ('25), M Thomas ('25), DJ Montgomery ('25)
TE: --
2024 Cap Spent: $201
IR: --
TAXI SQUAD (4 max): Sinnott ('28), All ('28), Schrader ('26), A Reed ('28)
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Re: 2019 Running Back Report
I wasn't trolling DD. I should have been more clear. I sent Matt Kelley, the creator of player profiler an E mail in regards to Jacobs time and the 2nd pro day. I wasn't implying I emailed you and you didn't do anything, that would have been disingenuous.Dynasty DeLorean wrote: ↑Fri Jun 07, 2019 7:04 amI actually do have to go back and double check Jacob's speed score. But you kind of run into problems if you're kind of picking and choosing which time to use. I typically go by the first legitimate one / combine ideally. Also, at what weight did he run during his 2nd pro day. If we don't have the weight then we can't use the time.FantasyFreak wrote: ↑Wed Jun 05, 2019 9:40 pmYeah, I sent him an E mail about that, but he didn't seem to how enough info to adjust it. Jacobs 2nd pro day time, even with the adjustment, if Rappaport's number are accurate would be sub 4.60.ericanadian wrote: ↑Wed Jun 05, 2019 8:49 pm
He uses playerprofiler.com and my understanding is that they apply an adjustment to Pro Day times +0.05 if I recall correctly. I also believe they used Alabama’s first Pro Day where Jacobs ran a 4.63/4.66. So, if you want to use the second Pro Day, you’d run it as a 4.59 to be comparable.
edit: I don't even know why i'm bothering to respond to the troll. I deleted my last post. If he confused anyone with his nonsense feel free to ask any questions you may have.
Janiel Dones Truther
Foodie. Well done steak goes in the trash.
Habaneros make the best tasting hot sauce. Throwing a bunch of random stuff on top of fries doesn't mean you call it "poutine".
Foodie. Well done steak goes in the trash.
Habaneros make the best tasting hot sauce. Throwing a bunch of random stuff on top of fries doesn't mean you call it "poutine".
- Dynasty DeLorean
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Re: 2019 Running Back Report
Not you, someone elseFantasyFreak wrote: ↑Fri Jun 07, 2019 11:04 amI wasn't trolling DD. I should have been more clear. I sent Matt Kelley, the creator of player profiler an E mail in regards to Jacobs time and the 2nd pro day. I wasn't implying I emailed you and you didn't do anything, that would have been disingenuous.Dynasty DeLorean wrote: ↑Fri Jun 07, 2019 7:04 amI actually do have to go back and double check Jacob's speed score. But you kind of run into problems if you're kind of picking and choosing which time to use. I typically go by the first legitimate one / combine ideally. Also, at what weight did he run during his 2nd pro day. If we don't have the weight then we can't use the time.FantasyFreak wrote: ↑Wed Jun 05, 2019 9:40 pm
Yeah, I sent him an E mail about that, but he didn't seem to how enough info to adjust it. Jacobs 2nd pro day time, even with the adjustment, if Rappaport's number are accurate would be sub 4.60.
edit: I don't even know why i'm bothering to respond to the troll. I deleted my last post. If he confused anyone with his nonsense feel free to ask any questions you may have.
Re: 2019 Running Back Report
Really great explanation for people who dont get this stuff like me. i hate stats with a passion, anything math related gives me a headache like a drank a bottle of tequila. This was a spot on explanation though. I like this report but sometimes explaining it is just too much for me.moishetreats 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.
----------
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.
------------
@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!!
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Re: 2019 Running Back Report
I’m pretty sure the troll in question is me. I think, if your work is so hard to defend that your response to criticism of how you do your analysis is to just call people trolls, then your work isn’t worth much.
Still waiting to hear about how the model that has Derrius Guice in Tier 1 and Trent Richardson in Tier 4 hasn’t been overfit.
Still waiting to hear about how the model that has Derrius Guice in Tier 1 and Trent Richardson in Tier 4 hasn’t been overfit.
COOGAN IS A CHEATER AND A THIEF
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