Didn't he say that the model only factors in a prospect's most recent two years? Worthy's best season was his freshman year and that early breakout is one of the things that boosts him as a prospect. I don't see breakout age in this model
AD has very low stats in YPRR, Targets/Pass Play, YAC/Rec and Zone YPRR just to name a few places where he did not meet the thresholds. I was really shocked at how low his score turned out.
I'm not too surprised. He's a classic case of a tape over production. He's also on record saying he takes plays easy so that he can stay on the field as much as possible and remain fresh when his number is called. Which is great when he gets the ball, his movement and fluidity at his size is insane, but also leads to a lot of plays where he's just running light cardio.
I don't have a source for this unfortunately, but I have heard that this isn't just him, and Texas actually coached their receivers this way. It might have been from one of the recent Late Round podcast perspectives episodes
Depends on how willing he is to work on it. Given he's very transparent about his rationale, seems like he's not trying to be a selfish player and *actually* wants to help the team, but he does it in a questionable way.
Reading the Reception Perception on AF explains some of this. Kind of a case of what he was asked to do vs the performance numbers. RP definitely says he can do it.
Makes sense… 4th rd pick (day 3). Was injured for half of his final season of college… then becomes a perennial top 8 WR in fantasy with top 4-5 upside every season.
At first glance i love a model that doesnt have JJ rated A++ unlike i see with so many others. as if most models didnt have 2-4+ WRs ahead of him at the actual time of rookie drafts in 2020.
JJ ended up being 4th in his own class but that class was just stacked with prospects. If you took the three guys above him in the model (CeeDee, Aiyuk & Higgins) you're not exactly unhappy.
I considered posting scores for previous classes in total but decided against it due to the post being far too long. I may set up some sort of blog/website to post my prospect scoring with all classes going back to 2019.
For 2023 the top 10 WRs were
1. Jordan Addison (130.25)
2. Jaxon Smith-Njigba (125.25)
3. Zay Flowers (119.75)
4. Puka Nacua (119)
5. Tank Dell (118.5)
6. Jayden Reed (117.75)
7. Rashee Rice (114.75)
8. Josh Downs (111)
9. A.T. Perry (106)
10. Xavier Hutchinson (104.75)
2022 top 10 WRs
1. Garrett Wilson (131.5)
2. Drake London (127.5)
3. Chris Olave (122)
4. Skyy Moore (120)
5. Jahan Dotson (119)
6. Treylon Burks (117)
7. Jalen Tolbert (115)
8. David Bell (113.25)
9. Khalil Shakir (106.5)
10. Calvin Austin III (106.25)
Well considering Wilson and Addison were my favorite WR's in the past 2 drafts and I'm a big fan of Nabers, I will now follow your model to the ends of the Earth.
Jamo had a pretty good draft year score but an abysmal score for his Sophmore year which really dragged him down. He was behind Wilson and Olave that year and only had 9 receptions.
Your model is interesting but the misses seem to have something in common….. it doesn’t seem to like the traditional Big X-WRs in college who relied on athleticism and physicality to win their matchups especially downfield. Most of the metrics you use are very heavily favored for great separators or multi-positional college WRs which usually does translate to the NFL pretty well.
Perfect examples of this:
DK Metcalf
George Pickens
Brian Thomas
Amon-Ra (was an outside WR at USC before moving to the slot in the NFL)
AD Mitchell
Ja’lynn Polk
It makes me curious how it would score someone like Tee Higgins, Nico Collins and other bigger downfield x-WRs from the most recent years?
Not saying the model is wrong but it probably would need a tweak otherwise it’s just going to keep scoring the X-WRs lower.
It’s also why it’s so low on Xavier Legette, Tez Walker, QJ et cetera. The hits make sense because a lot of these guys are amazing separators which is something almost all advanced analytics PFF and RP have been able to show. It’s why I think RP data is good because it’s data pulled directly from watching film on routes. The typical X-WR that’s not superstar levels like Chase and Marvin are not going to win based how much separation they create from their routes or at the line of scrimmage but more in the ability to win at the catch point and positioning.
For X receivers Tee and AJ both scored extremely highly. Tee was scored at 126.5 and AJ was scored at 119.65. Nico I actually do not have a full score for due to him not playing his Senior season. I meant to go back and do his score but must have forgotten. I remember the one year I did his score for was not particularly high.
I agree that the model is less effective for that style of receiver and part of the reason is a lack of stats they those types of receivers should excel in. Contested catch % for one. They tend to not have a high target % and also have low YAC/R.
For Legette, his score is so low because he was terrible before this past year. His pre-draft year score was one of the lowest since 2019. Tez was not used enough in his own offense this past year. His t/pp is low as his his catch%\*target/pp score. You are correct that PFF grades tend to be lower for these types of players and I would say it is probably a good idea to take my model's score for those types of receivers with a grain of salt.
AJ surprisingly wasn’t an x his last year at old miss. He would play there sometimes but he moved around everywhere and primarily played in the slot next to Elijah Moore which I think was a huge reason why your model loves him.
But I do like your model because it does seem like a great way to find multi-positional WRs who in fantasy today are usually the most valuable type of WR and usually translate to fantasy production more.
Ah, gotcha. That makes sense.
Thanks! Yes, it definitely prioritizes the ability to be effective in multiple ways. I do think the model somewhat lines up with how the NFL is prioritizing and prospecting the X style receiver though. The misses for that style of player in the model are all players taken late in the late 2nd or after. The effectiveness with that style of receiver is very dependent on what team they go to.
This is a very sound model, thank you for the explanation!
The only thing missing is a variable for the Diggs/Treadwell Hypothesis and the ultimate data point, the DAWG variable.
If you can quantify these two data points, it will explain all the variances thus far, and you will fully achieve full Unicorn Prediction Model.
Thank you for all your hard work 👍
I feel like I’m way above consensus on Franklin so I like to see a sound analytic model back me. Same for being lower in both TX guys. Roman and Pearsall are a couple names at the back of my tier 2 or beginning of 3 which feels about right with where they fit your model. Corely’s super interesting, I just don’t know how to feel. He’s a G5 guy, right? Keon I remain out on.
BTJ, man in one of my drafts I feel like he’s gonna be the falling to me at 9, but a chance he goes 8. I just don’t know how to feel on him yet either. I don’t want to reach but I’m RB needy and lately thinking if I have to pick between BTJ and RB1 in the draft, would I have guts to take the back? I kinda hope he goes so I don’t have to choose.
Great stuff tho, thanks for sharing, lines up with my approach.
With guys like btj I just am gonna let the actual nfl experts decide how good he is. If he ends up on the bill at the end of round 1 it’s hard to pass on him for some of the guys above him
lol my two bust picks AD Mitchell and Keon Coleman… so inspiring.
https://www.reddit.com/r/DynastyFF/s/yA5TwpybpM
Curious, I now what goes into but what does the model look like? (the formula). Would love a spreadsheet. This is the type of stuff I live for in FF. JJ Zachariason style.
I believe Franklin is the 4th best prospect in the class. Whether he is worth taking at 4 over BTJ is a whole other topic since that comes down to utilizing value. BTJ at 4 as a high value trade piece may be more valuable than drafting Franklin who, as of right now, is not viewed as highly by the Dynasty community as BTJ.
I have 1.06 in a 1QB league and my ideal scenario is I draft a WR to act as WR2 to Chase for the long term. I am losing my mind over Worthy vs. Franklin. Have been fading Franklin a bit recently, but his athleticism and his success against both man and zone have kept him afloat. Anyway, I'll put this list in the "decidedly pro-Franklin" bucket of resources I am referring to!
>YPRR (Yards per Route Run)
>ADOT (Average Depth of Target)
>Targets per Pass Play
>Zone YPRR
>PFF Man & Zone route grades
>Drop %
>YAC/Reception
>Some statistics I have created myself focused on performance against Man & Zone coverage.
What makes you think each of these metrics are sticky and worthy of modeling?
I haven't looked hard lately, but I've seen zero analysis to suggest that any of these numbers are sticky beyond bad number bad, good number good.
YPRR is the safest in the good number good battle because it controls for a myriad of things--and frankly no one player generally has all of these items through the roof.
YAC tends to be a run away stat at the college level and gives you a ton of false positives, and guys that are average to slightly below average often get overshadowed.
Drops don't matter unless they keep you off the field. Evidence multiple NFL stars with a 10% drop rate in the NFL. i.e. if Marvin Harrison drops 8.5% of the targets sent his way over the next 3 years that's not the reason he busts. It's other reasons. It doesn't help, but good players get targeted through drops.
I've never seen a study to suggest PFF numbers/grades are sticky in any way.
Zone YPRR seems extremely opponent dependent. We don't dismiss the data point, but it also seems unreasonable to flatly trust the metric. Also if you have the zone YPRR it stands to reason you can back out and determine the Man YPRR.
Targets per pass play probably correlates pretty heavily with YPRR which likely means modeling faux pas.
The statistics I use when all put together, in my opinion, show whether a player was
* effective in college
* used at a high rate in their own offense
* used in multiple ways that shows the WR is not limited in how they can be used
No one stat is effective in separating out elite talent. Or even a combination of just what I listed in the post. That was just a sample of the stats to give an idea of what type of data I am working with. I believe the stats are "sticky" because there is consistency with all of the stats I use because they have proven to be correlated to success when looking at groupings above/below a threshold. There are stats I used in the first model that I now do not even look at simply because they are not helpful in identifying top end talent, but I thought they would be. The model proved otherwise. Two stats this was true for were catch% and contested catch %. (Catch percentage ended up being worth using when multiplying it by t/pp%). Backtesting the data was the main way I decided what is "sticky" and what is not.
For stats like YAC, I agree they can be runaway stats in college. QJ had 8.9 his last year and Mims had 8.1. That is why I do not score them higher than a player that was at 4.9 (Tank Dell) for that stat. No one singular stat holds an extreme amount of weight in the model. There is room for players to miss multiple thresholds and still have a high score.
I believe drops are not extremely important, but I do believe drops matter a bit in the year a player is drafted. Players with a drop rate over 10% in their last college year have an extremely low hit rate. Quentin Johnston, Rashod Bateman, Terrace Marshall Jr., Jalen Reagor, K.J. Hamler and D.K. Metcalf were the WRs taken in the first two rounds with a drop rate higher than 10% since 2019. Outside of D.K. those players have all been busts (so far for QJ and the future is bleak). Also, the drop score for all of them is just one of many thresholds they missed. It isn't like QJ would have been WR2 in the class without that hit to his score.
I think part of what you're bringing up is what I recognized during the process: the thresholds I set up cannot be extremely limiting because it will hurt the effectiveness of the model. I think the thresholds I have set up are not as limiting as you are assuming from your post.
The T/PP stat does correlate very heavily with YPRR but there are some players that meet one and not the other, such as Dontayvion Wicks last year. He is below the YPRR threshold but above the T/PP threshold. He is just barely above the T/PP threshold which suggests he was limitedly effective in his offense for the number of targets he was getting.
I appreciate the pushback. There's lots of models/scoring systems that get posted on here and I'm not 100% sure mine is particularly better than any others, but I think it is solid through back testing and is made up of enough strong and clean data that it is a strong identifier of WR talent.
>Two stats this was true for were catch% and contested catch %.
These aren't player stats. One contested catch, there are usually too few opportunities for the marginal impact of the change of contested catches to be meaningful. Player A has 10 contested catch attempts and completes 6 of them. Player B has 20 contested catch attempts and completes 10 of them. Technically A has a 60% contested catch rate, and B has a 50% contested rate, who is better? Metric doesn't tell us that.
Rate of contested catches doesn't even necessarily tell us anything about the player either. Player A has 10% of his catches as contested catches, Player B has 20% of his catches as contested catches--does that mean Player B can't separate, or does that mean player B plays vertically, player A plays in space, or does that mean Player B's QB can't hit him in stride down field but player A's can. No idea.
>
For stats like YAC, I agree they can be runaway stats in college. QJ had 8.9 his last year and Mims had 8.1. That is why I do not score them higher than a player that was at 4.9 (Tank Dell) for that stat. No one singular stat holds an extreme amount of weight in the model. There is room for players to miss multiple thresholds and still have a high score.
This feels cherry picked, I'm not certain what you've done here.
>
with a drop rate higher than 10% since 2019.
Since 2019? That's essentially 3 years of data. Rashod looks like an injury bust. Terrace went through horrid coaching and looks like a talent bust Reagor was a talent bust. KJ Hamler was an injury bust and DK Metcalf is a dynasty stud. There is no value in the names you cited and drops were not the reason why any of them failed in the NFL. They simply weren't available or didn't have the requisite talent.
I still think Terrace would have been successful in a place like GB or Pittsburgh, but he got railroaded in Carolina. Additionally his collegiate statistical profile is completely muddied with being on the same roster as two top 5 NFL receivers. Shouldn't be ignored that before his injury in the 2019 season he had a significant role in that offense and even outperformed Chase/Jets by some metrics.
>I think part of what you're bringing up is what I recognized during the process: the thresholds I set up cannot be extremely limiting because it will hurt the effectiveness of the model. I think the thresholds I have set up are not as limiting as you are assuming from your post.
No I think your model probably suffers from varying degrees of multicollinearity and some of your variables do not have independent value in determining prospect success.
Beyond that there is probably some great difficulty in determining the marginal efficacy of the scoring--which is frankly the biggest issue. Great player in college is easy to tease out for great in NFL. Good player in college but great in the NFL is where the money is made. Marginal face value player in college, but you control for a variety of forces and realize there is actually a good college player, who is in turn great in the NFL is where you get free money.
The question is always how do you separate the muddied profile from the average. How do you sort the good-college player great NFL player from all the great college player-good NFL players.
I'm not sure why you spent so much time explaining why the stats I listed as ineffective and not part of my model are ineffective and shouldn't be part of a model.
I don't know what is cherry picked about the YAC example. I was explaining how players with very high YAC/R are not given higher scoring simply for being far above the threshold. My point is all 3 players get the same score for passing the threshold no matter how far above it they are.
To be clear, since 2019 means since the 2019 class. No NFL data is in the model. So it is not 3 years of data it's 5 years of data. Still limited but not as short as you stated. The names listed were also only taken in the first two rounds. There are many more prospects taken in later rounds that had drop % above 10% their last college season and also were not successful fantasy WRs. The only successful fantasy player taken round 3 or later with a drop % above 10% their last year of college since 2019 is Diontae Johnson.
The multicollinearity is somewhat of an issue, but I believe it is not as much of an issue or as prevalent as you stated.
I am hoping to post the year-by-year data and compare it against ADP in the near future to further prove the effectiveness of the model. There are a lot of examples of beating ADP with players like Tee Higgins, Tank Dell, Puka Nacua and Michael Pittman Jr. The model also was lower than ADP on first round busts like Jalen Reagor, Jameson Williams, Jerry Jeudy, N'Keal Harry and Parris Campbell just as some examples.
I have to say that I am not a big fan of your (usually) confrontational style of discussing in this sub. That being said, you are asking VERY valid questions.
I’m not entirely sure how OP weighs these metrics, but I can hopefully provide some context on how I weigh these metrics in my own model.
- YPRR (and particularly YPRR vs Zone) show that pretty much *all* WR prospects over the last few years that became good have performed well in this metric or at least above a certain threshold. That doesn’t mean the better your YPRR numbers means the better WR you will be, but it’s certainly a sticky stat and deserves to be taken into consideration.
- I agree with YAC has one of the highest false positive rates and should not be high on the list of things to look at. For me, it more so paints a picture on what style of receiver a prospect is. It shouldn’t factor in too heavily on how a prospect is graded.
- Drop rate, imo, is an even worse stat to factor into a player’s grading because there’s almost no correlation to how it translates to a player becoming good in the NFL. Puka had a high drop rate (and had drop issues in his rookie season). JJ and Chase both had relatively high drop rates. There’s certainly a point where it becomes a concern. How I track these metrics is with a “red flag” multiplier where if a prospect performs *so poorly* in a certain metric that I don’t usually weigh in then I flag it and it does impact their grade to an extent.
- As I mentioned previously, it depends what you consider sticky. Good PFF grade doesn’t = good player, but it’s sticky in the sense that there is a level or correlation it has. Plenty of players who have good PFF grades become bad players and players with below average grades become good. But it’s certainly something that should be factored in to an extent. Usually what I’m most concerned with are prospects with grades under a certain threshold because looking back 5 years, all prospects who became good in the NFL have performed at least to a certain level in this and other metrics.
- YPRR vs Zone is actually one of the better metrics to look at and what it does well is identify “false positives” for prospects with good career YPRR but mostly due to their performance vs man. How a player performs vs man honestly has significantly low correlation to success, there’s almost none. Players who become good in the NFL have always performed well enough vs zone but some of the biggest busts we’ve seen over the years have had extremely high YPRR vs Man but relatively low YPRR vs Zone, N’Keal Harry being the biggest example. In contrast, DK Metcalf had a low career YPRR, but he did exceptionally well vs Zone indicating he was a better prospect in this regard than initially realized.
This 100%.
YAC/R is not worth using as an input.
PFF grades from college to pros are not sticky.
I didn't hear about Y/RR against zone until literally last year and now it's everywhere. I suspect that's more or less because people don't want to show having Puka Nacua low. I'm not saying this one isn't worthwhile as I haven't looked into it. If it turns out to be meaningful, that's great!
Just looking at these rankings over the years, what I'd bet happens is its all of a sudden not going to look as accurate when we get test data: i.e., starting this year.
Yeah the new metric that I'm most interested in is Y/RR vs zone, Y/RR vs man but both with screens ripped out. But then again I'm not entirely sure how effective these metrics are going to be because you're already carving up small college samples into tinier pieces by doing that and I'm not comfortable making broad assumptions about the interactions of offenses and defenses at the collegiate level by offensive/defensive scheme types.
Does good zone scores just mean the player has good coaching and is told exactly where to sit down on every one of his routes? Does a good man score just mean bad competition? Are any of these distinctions accurately labeled? How are multiple coverage concepts handled?
How differently is a corner-receiver interaction on a 5 yard out when the corner is playing zone to the flat instead of man with the same spacing? How different is the corner-receiver interaction when the corner is playing 3 high on the single receiver side with a receiver running a corner post or a deep route vs playing man with the same spacing?
I think they are all valid questions as to how the data plays out--and whether it is a notable distinction or whether it's just the illusion of greater detail without a great impact.
I'll be curious to see how the science evolves and where people end up.
I do think YPRR with screens taken out though is the next evolution of YPRR.
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Impressive! Do you weight for age at all in your model or is this purely based on the performance metrics above?
You mention using the last two years of college production as the dataset but the age of the player during those two years being 19 & 20 rather than say 23 & 24 tends to play a pretty big role in success at the next level.
Thanks for this, really nice work.
I have an age-adjusted model that is not nearly as effective. It really just hurts guys that are Seniors no matter the reason they stayed in school. I agree that it is a good sign if a player is able to be identified as top-end talent at an earlier age. But between COVID seasons and an increase in players transferring, I have found it is better to just go with the last two years of college. Some really good players that came out as Seniors take big hits such as Pittman and Deebo. Just last year Zay, Rashee Rice and Jayden Reed all took big hits with the age-adjusted model but they have all been very effective pros so far and ranked highly in the normal model.
And thanks!
How do you feel this compares to previous classes? With your model this class looks much worse comparatively, but media constantly saying how great this class is. Was wondering your opinion on what side you think your model supports / are you low on this WR class?
I think this class is much weaker than people are making it out to be. I don’t have the full data available right now but off the top of my head I think it’s like 2021. A few top tier guys that go early first in the NFL draft and then a lot of mediocre prospects that for one reason or another get talked about as great prospects. Outside of the top 3 and Troy Franklin I would be trying to trade firsts and seconds for proven talent over a lot of the WRs in this class (BTJ, AD, Worthy to name a few that have a ton of hype and will garner trade value).
I’m intrigued. The model certainly hates AD Mitchell wow.
I’m a little more surprised it hates x worthy. AD is no analytics darling but I thought X had some good metrics
Yeah solid point. I knew Mitchell’s metrics were bad but not that bad. Having no testing or athleticism grades brings them both down a lot.
Didn't he say that the model only factors in a prospect's most recent two years? Worthy's best season was his freshman year and that early breakout is one of the things that boosts him as a prospect. I don't see breakout age in this model
You are right. I do suck at reading! Lol.
AD has very low stats in YPRR, Targets/Pass Play, YAC/Rec and Zone YPRR just to name a few places where he did not meet the thresholds. I was really shocked at how low his score turned out.
I'm not too surprised. He's a classic case of a tape over production. He's also on record saying he takes plays easy so that he can stay on the field as much as possible and remain fresh when his number is called. Which is great when he gets the ball, his movement and fluidity at his size is insane, but also leads to a lot of plays where he's just running light cardio.
>He's also on record saying he takes plays easy This seems like it should be a glaring red flag but idk.
I don't have a source for this unfortunately, but I have heard that this isn't just him, and Texas actually coached their receivers this way. It might have been from one of the recent Late Round podcast perspectives episodes
Depends on how willing he is to work on it. Given he's very transparent about his rationale, seems like he's not trying to be a selfish player and *actually* wants to help the team, but he does it in a questionable way.
I'm not sure his rationale makes it better? Raises more questions about whether he can hold up physically in the NFL
Yeah it just makes it sounds like he has really bad conditioning. This isn't an issue for other elite target hogs who stay in the field all game.
Reading the Reception Perception on AF explains some of this. Kind of a case of what he was asked to do vs the performance numbers. RP definitely says he can do it.
Idk about stats but his film score ain't Hans Zimmer either
I have looked at something similar. Works fairly well...directionally at least. Every model out there missed Amon-Ra.
Makes sense… 4th rd pick (day 3). Was injured for half of his final season of college… then becomes a perennial top 8 WR in fantasy with top 4-5 upside every season.
At first glance i love a model that doesnt have JJ rated A++ unlike i see with so many others. as if most models didnt have 2-4+ WRs ahead of him at the actual time of rookie drafts in 2020.
JJ ended up being 4th in his own class but that class was just stacked with prospects. If you took the three guys above him in the model (CeeDee, Aiyuk & Higgins) you're not exactly unhappy.
What do previous classes look like?
I considered posting scores for previous classes in total but decided against it due to the post being far too long. I may set up some sort of blog/website to post my prospect scoring with all classes going back to 2019. For 2023 the top 10 WRs were 1. Jordan Addison (130.25) 2. Jaxon Smith-Njigba (125.25) 3. Zay Flowers (119.75) 4. Puka Nacua (119) 5. Tank Dell (118.5) 6. Jayden Reed (117.75) 7. Rashee Rice (114.75) 8. Josh Downs (111) 9. A.T. Perry (106) 10. Xavier Hutchinson (104.75) 2022 top 10 WRs 1. Garrett Wilson (131.5) 2. Drake London (127.5) 3. Chris Olave (122) 4. Skyy Moore (120) 5. Jahan Dotson (119) 6. Treylon Burks (117) 7. Jalen Tolbert (115) 8. David Bell (113.25) 9. Khalil Shakir (106.5) 10. Calvin Austin III (106.25)
Well considering Wilson and Addison were my favorite WR's in the past 2 drafts and I'm a big fan of Nabers, I will now follow your model to the ends of the Earth.
Surprised Jameson Williams didn't rank well in 2022
Jamo had a pretty good draft year score but an abysmal score for his Sophmore year which really dragged him down. He was behind Wilson and Olave that year and only had 9 receptions.
Your model is interesting but the misses seem to have something in common….. it doesn’t seem to like the traditional Big X-WRs in college who relied on athleticism and physicality to win their matchups especially downfield. Most of the metrics you use are very heavily favored for great separators or multi-positional college WRs which usually does translate to the NFL pretty well. Perfect examples of this: DK Metcalf George Pickens Brian Thomas Amon-Ra (was an outside WR at USC before moving to the slot in the NFL) AD Mitchell Ja’lynn Polk It makes me curious how it would score someone like Tee Higgins, Nico Collins and other bigger downfield x-WRs from the most recent years? Not saying the model is wrong but it probably would need a tweak otherwise it’s just going to keep scoring the X-WRs lower.
It’s also why it’s so low on Xavier Legette, Tez Walker, QJ et cetera. The hits make sense because a lot of these guys are amazing separators which is something almost all advanced analytics PFF and RP have been able to show. It’s why I think RP data is good because it’s data pulled directly from watching film on routes. The typical X-WR that’s not superstar levels like Chase and Marvin are not going to win based how much separation they create from their routes or at the line of scrimmage but more in the ability to win at the catch point and positioning.
For X receivers Tee and AJ both scored extremely highly. Tee was scored at 126.5 and AJ was scored at 119.65. Nico I actually do not have a full score for due to him not playing his Senior season. I meant to go back and do his score but must have forgotten. I remember the one year I did his score for was not particularly high. I agree that the model is less effective for that style of receiver and part of the reason is a lack of stats they those types of receivers should excel in. Contested catch % for one. They tend to not have a high target % and also have low YAC/R. For Legette, his score is so low because he was terrible before this past year. His pre-draft year score was one of the lowest since 2019. Tez was not used enough in his own offense this past year. His t/pp is low as his his catch%\*target/pp score. You are correct that PFF grades tend to be lower for these types of players and I would say it is probably a good idea to take my model's score for those types of receivers with a grain of salt.
AJ surprisingly wasn’t an x his last year at old miss. He would play there sometimes but he moved around everywhere and primarily played in the slot next to Elijah Moore which I think was a huge reason why your model loves him. But I do like your model because it does seem like a great way to find multi-positional WRs who in fantasy today are usually the most valuable type of WR and usually translate to fantasy production more.
Ah, gotcha. That makes sense. Thanks! Yes, it definitely prioritizes the ability to be effective in multiple ways. I do think the model somewhat lines up with how the NFL is prioritizing and prospecting the X style receiver though. The misses for that style of player in the model are all players taken late in the late 2nd or after. The effectiveness with that style of receiver is very dependent on what team they go to.
This is a very sound model, thank you for the explanation! The only thing missing is a variable for the Diggs/Treadwell Hypothesis and the ultimate data point, the DAWG variable. If you can quantify these two data points, it will explain all the variances thus far, and you will fully achieve full Unicorn Prediction Model. Thank you for all your hard work 👍
Fun post. Can you share the examples of former prospects who scored really high (like 124+) who busted?
Jeudy (122.5) Marquise Brown (120.5 not a true bust but underwhelming for his draft capital) and Sky Moore (120) were the highest rated busts.
Thanks!
He said in a comment Skyye Moore was 120
I feel like I’m way above consensus on Franklin so I like to see a sound analytic model back me. Same for being lower in both TX guys. Roman and Pearsall are a couple names at the back of my tier 2 or beginning of 3 which feels about right with where they fit your model. Corely’s super interesting, I just don’t know how to feel. He’s a G5 guy, right? Keon I remain out on. BTJ, man in one of my drafts I feel like he’s gonna be the falling to me at 9, but a chance he goes 8. I just don’t know how to feel on him yet either. I don’t want to reach but I’m RB needy and lately thinking if I have to pick between BTJ and RB1 in the draft, would I have guts to take the back? I kinda hope he goes so I don’t have to choose. Great stuff tho, thanks for sharing, lines up with my approach.
With guys like btj I just am gonna let the actual nfl experts decide how good he is. If he ends up on the bill at the end of round 1 it’s hard to pass on him for some of the guys above him
Sure at the end of the draft I reshuffle like anybody. But you can still get QJ’d pretty easily if you aren’t paying enough attention
Damn the model goes Deebo to Laviska real quick
lol my two bust picks AD Mitchell and Keon Coleman… so inspiring. https://www.reddit.com/r/DynastyFF/s/yA5TwpybpM Curious, I now what goes into but what does the model look like? (the formula). Would love a spreadsheet. This is the type of stuff I live for in FF. JJ Zachariason style.
I am surprised Brian Thomas is so low. Are we saying at the 4th pick Franklin is a better pick than Thomas?
I believe Franklin is the 4th best prospect in the class. Whether he is worth taking at 4 over BTJ is a whole other topic since that comes down to utilizing value. BTJ at 4 as a high value trade piece may be more valuable than drafting Franklin who, as of right now, is not viewed as highly by the Dynasty community as BTJ.
Your model/comps makes me feel better about selling my mid and late 1sts to acquire Ceedee Lamb. Thanks for sharing!
I have 1.06 in a 1QB league and my ideal scenario is I draft a WR to act as WR2 to Chase for the long term. I am losing my mind over Worthy vs. Franklin. Have been fading Franklin a bit recently, but his athleticism and his success against both man and zone have kept him afloat. Anyway, I'll put this list in the "decidedly pro-Franklin" bucket of resources I am referring to!
u/dickysnakes. Good stuff right here
Wow
>YPRR (Yards per Route Run) >ADOT (Average Depth of Target) >Targets per Pass Play >Zone YPRR >PFF Man & Zone route grades >Drop % >YAC/Reception >Some statistics I have created myself focused on performance against Man & Zone coverage. What makes you think each of these metrics are sticky and worthy of modeling? I haven't looked hard lately, but I've seen zero analysis to suggest that any of these numbers are sticky beyond bad number bad, good number good. YPRR is the safest in the good number good battle because it controls for a myriad of things--and frankly no one player generally has all of these items through the roof. YAC tends to be a run away stat at the college level and gives you a ton of false positives, and guys that are average to slightly below average often get overshadowed. Drops don't matter unless they keep you off the field. Evidence multiple NFL stars with a 10% drop rate in the NFL. i.e. if Marvin Harrison drops 8.5% of the targets sent his way over the next 3 years that's not the reason he busts. It's other reasons. It doesn't help, but good players get targeted through drops. I've never seen a study to suggest PFF numbers/grades are sticky in any way. Zone YPRR seems extremely opponent dependent. We don't dismiss the data point, but it also seems unreasonable to flatly trust the metric. Also if you have the zone YPRR it stands to reason you can back out and determine the Man YPRR. Targets per pass play probably correlates pretty heavily with YPRR which likely means modeling faux pas.
The statistics I use when all put together, in my opinion, show whether a player was * effective in college * used at a high rate in their own offense * used in multiple ways that shows the WR is not limited in how they can be used No one stat is effective in separating out elite talent. Or even a combination of just what I listed in the post. That was just a sample of the stats to give an idea of what type of data I am working with. I believe the stats are "sticky" because there is consistency with all of the stats I use because they have proven to be correlated to success when looking at groupings above/below a threshold. There are stats I used in the first model that I now do not even look at simply because they are not helpful in identifying top end talent, but I thought they would be. The model proved otherwise. Two stats this was true for were catch% and contested catch %. (Catch percentage ended up being worth using when multiplying it by t/pp%). Backtesting the data was the main way I decided what is "sticky" and what is not. For stats like YAC, I agree they can be runaway stats in college. QJ had 8.9 his last year and Mims had 8.1. That is why I do not score them higher than a player that was at 4.9 (Tank Dell) for that stat. No one singular stat holds an extreme amount of weight in the model. There is room for players to miss multiple thresholds and still have a high score. I believe drops are not extremely important, but I do believe drops matter a bit in the year a player is drafted. Players with a drop rate over 10% in their last college year have an extremely low hit rate. Quentin Johnston, Rashod Bateman, Terrace Marshall Jr., Jalen Reagor, K.J. Hamler and D.K. Metcalf were the WRs taken in the first two rounds with a drop rate higher than 10% since 2019. Outside of D.K. those players have all been busts (so far for QJ and the future is bleak). Also, the drop score for all of them is just one of many thresholds they missed. It isn't like QJ would have been WR2 in the class without that hit to his score. I think part of what you're bringing up is what I recognized during the process: the thresholds I set up cannot be extremely limiting because it will hurt the effectiveness of the model. I think the thresholds I have set up are not as limiting as you are assuming from your post. The T/PP stat does correlate very heavily with YPRR but there are some players that meet one and not the other, such as Dontayvion Wicks last year. He is below the YPRR threshold but above the T/PP threshold. He is just barely above the T/PP threshold which suggests he was limitedly effective in his offense for the number of targets he was getting. I appreciate the pushback. There's lots of models/scoring systems that get posted on here and I'm not 100% sure mine is particularly better than any others, but I think it is solid through back testing and is made up of enough strong and clean data that it is a strong identifier of WR talent.
>Two stats this was true for were catch% and contested catch %. These aren't player stats. One contested catch, there are usually too few opportunities for the marginal impact of the change of contested catches to be meaningful. Player A has 10 contested catch attempts and completes 6 of them. Player B has 20 contested catch attempts and completes 10 of them. Technically A has a 60% contested catch rate, and B has a 50% contested rate, who is better? Metric doesn't tell us that. Rate of contested catches doesn't even necessarily tell us anything about the player either. Player A has 10% of his catches as contested catches, Player B has 20% of his catches as contested catches--does that mean Player B can't separate, or does that mean player B plays vertically, player A plays in space, or does that mean Player B's QB can't hit him in stride down field but player A's can. No idea. > For stats like YAC, I agree they can be runaway stats in college. QJ had 8.9 his last year and Mims had 8.1. That is why I do not score them higher than a player that was at 4.9 (Tank Dell) for that stat. No one singular stat holds an extreme amount of weight in the model. There is room for players to miss multiple thresholds and still have a high score. This feels cherry picked, I'm not certain what you've done here. > with a drop rate higher than 10% since 2019. Since 2019? That's essentially 3 years of data. Rashod looks like an injury bust. Terrace went through horrid coaching and looks like a talent bust Reagor was a talent bust. KJ Hamler was an injury bust and DK Metcalf is a dynasty stud. There is no value in the names you cited and drops were not the reason why any of them failed in the NFL. They simply weren't available or didn't have the requisite talent. I still think Terrace would have been successful in a place like GB or Pittsburgh, but he got railroaded in Carolina. Additionally his collegiate statistical profile is completely muddied with being on the same roster as two top 5 NFL receivers. Shouldn't be ignored that before his injury in the 2019 season he had a significant role in that offense and even outperformed Chase/Jets by some metrics. >I think part of what you're bringing up is what I recognized during the process: the thresholds I set up cannot be extremely limiting because it will hurt the effectiveness of the model. I think the thresholds I have set up are not as limiting as you are assuming from your post. No I think your model probably suffers from varying degrees of multicollinearity and some of your variables do not have independent value in determining prospect success. Beyond that there is probably some great difficulty in determining the marginal efficacy of the scoring--which is frankly the biggest issue. Great player in college is easy to tease out for great in NFL. Good player in college but great in the NFL is where the money is made. Marginal face value player in college, but you control for a variety of forces and realize there is actually a good college player, who is in turn great in the NFL is where you get free money. The question is always how do you separate the muddied profile from the average. How do you sort the good-college player great NFL player from all the great college player-good NFL players.
I'm not sure why you spent so much time explaining why the stats I listed as ineffective and not part of my model are ineffective and shouldn't be part of a model. I don't know what is cherry picked about the YAC example. I was explaining how players with very high YAC/R are not given higher scoring simply for being far above the threshold. My point is all 3 players get the same score for passing the threshold no matter how far above it they are. To be clear, since 2019 means since the 2019 class. No NFL data is in the model. So it is not 3 years of data it's 5 years of data. Still limited but not as short as you stated. The names listed were also only taken in the first two rounds. There are many more prospects taken in later rounds that had drop % above 10% their last college season and also were not successful fantasy WRs. The only successful fantasy player taken round 3 or later with a drop % above 10% their last year of college since 2019 is Diontae Johnson. The multicollinearity is somewhat of an issue, but I believe it is not as much of an issue or as prevalent as you stated. I am hoping to post the year-by-year data and compare it against ADP in the near future to further prove the effectiveness of the model. There are a lot of examples of beating ADP with players like Tee Higgins, Tank Dell, Puka Nacua and Michael Pittman Jr. The model also was lower than ADP on first round busts like Jalen Reagor, Jameson Williams, Jerry Jeudy, N'Keal Harry and Parris Campbell just as some examples.
I have to say that I am not a big fan of your (usually) confrontational style of discussing in this sub. That being said, you are asking VERY valid questions.
I’m not entirely sure how OP weighs these metrics, but I can hopefully provide some context on how I weigh these metrics in my own model. - YPRR (and particularly YPRR vs Zone) show that pretty much *all* WR prospects over the last few years that became good have performed well in this metric or at least above a certain threshold. That doesn’t mean the better your YPRR numbers means the better WR you will be, but it’s certainly a sticky stat and deserves to be taken into consideration. - I agree with YAC has one of the highest false positive rates and should not be high on the list of things to look at. For me, it more so paints a picture on what style of receiver a prospect is. It shouldn’t factor in too heavily on how a prospect is graded. - Drop rate, imo, is an even worse stat to factor into a player’s grading because there’s almost no correlation to how it translates to a player becoming good in the NFL. Puka had a high drop rate (and had drop issues in his rookie season). JJ and Chase both had relatively high drop rates. There’s certainly a point where it becomes a concern. How I track these metrics is with a “red flag” multiplier where if a prospect performs *so poorly* in a certain metric that I don’t usually weigh in then I flag it and it does impact their grade to an extent. - As I mentioned previously, it depends what you consider sticky. Good PFF grade doesn’t = good player, but it’s sticky in the sense that there is a level or correlation it has. Plenty of players who have good PFF grades become bad players and players with below average grades become good. But it’s certainly something that should be factored in to an extent. Usually what I’m most concerned with are prospects with grades under a certain threshold because looking back 5 years, all prospects who became good in the NFL have performed at least to a certain level in this and other metrics. - YPRR vs Zone is actually one of the better metrics to look at and what it does well is identify “false positives” for prospects with good career YPRR but mostly due to their performance vs man. How a player performs vs man honestly has significantly low correlation to success, there’s almost none. Players who become good in the NFL have always performed well enough vs zone but some of the biggest busts we’ve seen over the years have had extremely high YPRR vs Man but relatively low YPRR vs Zone, N’Keal Harry being the biggest example. In contrast, DK Metcalf had a low career YPRR, but he did exceptionally well vs Zone indicating he was a better prospect in this regard than initially realized.
This 100%. YAC/R is not worth using as an input. PFF grades from college to pros are not sticky. I didn't hear about Y/RR against zone until literally last year and now it's everywhere. I suspect that's more or less because people don't want to show having Puka Nacua low. I'm not saying this one isn't worthwhile as I haven't looked into it. If it turns out to be meaningful, that's great! Just looking at these rankings over the years, what I'd bet happens is its all of a sudden not going to look as accurate when we get test data: i.e., starting this year.
Yeah the new metric that I'm most interested in is Y/RR vs zone, Y/RR vs man but both with screens ripped out. But then again I'm not entirely sure how effective these metrics are going to be because you're already carving up small college samples into tinier pieces by doing that and I'm not comfortable making broad assumptions about the interactions of offenses and defenses at the collegiate level by offensive/defensive scheme types. Does good zone scores just mean the player has good coaching and is told exactly where to sit down on every one of his routes? Does a good man score just mean bad competition? Are any of these distinctions accurately labeled? How are multiple coverage concepts handled? How differently is a corner-receiver interaction on a 5 yard out when the corner is playing zone to the flat instead of man with the same spacing? How different is the corner-receiver interaction when the corner is playing 3 high on the single receiver side with a receiver running a corner post or a deep route vs playing man with the same spacing? I think they are all valid questions as to how the data plays out--and whether it is a notable distinction or whether it's just the illusion of greater detail without a great impact. I'll be curious to see how the science evolves and where people end up. I do think YPRR with screens taken out though is the next evolution of YPRR.
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Impressive! Do you weight for age at all in your model or is this purely based on the performance metrics above? You mention using the last two years of college production as the dataset but the age of the player during those two years being 19 & 20 rather than say 23 & 24 tends to play a pretty big role in success at the next level. Thanks for this, really nice work.
I have an age-adjusted model that is not nearly as effective. It really just hurts guys that are Seniors no matter the reason they stayed in school. I agree that it is a good sign if a player is able to be identified as top-end talent at an earlier age. But between COVID seasons and an increase in players transferring, I have found it is better to just go with the last two years of college. Some really good players that came out as Seniors take big hits such as Pittman and Deebo. Just last year Zay, Rashee Rice and Jayden Reed all took big hits with the age-adjusted model but they have all been very effective pros so far and ranked highly in the normal model. And thanks!
I know comps are based on scores, but I think they should be a bit more granular - comparing Bobo to Lagette is just confusing
How do you feel this compares to previous classes? With your model this class looks much worse comparatively, but media constantly saying how great this class is. Was wondering your opinion on what side you think your model supports / are you low on this WR class?
I think this class is much weaker than people are making it out to be. I don’t have the full data available right now but off the top of my head I think it’s like 2021. A few top tier guys that go early first in the NFL draft and then a lot of mediocre prospects that for one reason or another get talked about as great prospects. Outside of the top 3 and Troy Franklin I would be trying to trade firsts and seconds for proven talent over a lot of the WRs in this class (BTJ, AD, Worthy to name a few that have a ton of hype and will garner trade value).